This document provides an introduction to neural networks. It begins with an outline covering statistical machine learning concepts like underfitting, overfitting and consistency. It then discusses multi-layer perceptrons, the basic building blocks of neural networks. It covers how perceptrons are presented, their theoretical properties, and how learning occurs. Finally, it provides an overview of deep neural networks and convolutional neural networks. The goal is to introduce fundamental concepts in neural networks from statistical learning to modern deep learning architectures.
A short introduction to statistical learningtuxette
This document provides an introduction to statistical learning methods. It begins with background information on statistical learning problems and discusses concepts like underfitting, overfitting, and consistency. It then summarizes decision trees and random forests, describing how they are learned from data and make predictions. Support vector machines and neural networks are also briefly mentioned. Key goals of statistical learning methods include accuracy on training data as well as generalization to new data.
Machine Learning: Foundations Course Number 0368403401butest
This machine learning course will cover theoretical and practical machine learning concepts. It will include 4 homework assignments and programming in Matlab. Lectures will be supplemented by student-submitted class notes in LaTeX. Topics will include learning approaches like storage and retrieval, rule learning, and flexible model estimation, as well as applications in areas like control, medical diagnosis, and web search. A final exam format has not been determined yet.
Machine Learning: Foundations Course Number 0368403401butest
This machine learning foundations course will consist of 4 homework assignments, both theoretical and programming problems in Matlab. There will be a final exam. Students will work in groups of 2-3 to take notes during classes in LaTeX format. These class notes will contribute 30% to the overall grade. The course will cover basic machine learning concepts like storage and retrieval, learning rules, estimating flexible models, and applications in areas like control, medical diagnosis, and document retrieval.
RECENT ADVANCES in PREDICTIVE (MACHINE) LEARNINGbutest
This document provides an introduction to recent advances in predictive machine learning, specifically support vector machines and boosted decision trees. It begins with an overview of predictive learning and common methods. It then describes kernel methods, including how they were extended to support vector machines. Next, it discusses extending decision trees with boosting. The document concludes by comparing support vector machines and boosted decision trees, and noting they are not the only recent advances in machine learning.
Presentation on Machine Learning and Data Miningbutest
The document discusses the differences between automatic learning/machine learning and data mining. It provides definitions for supervised vs unsupervised learning, what automated induction is, and the base components of data mining. Additionally, it outlines differences in the scientific approach between automatic learning and data mining, as well as differences from an industry perspective, including common data mining techniques used and tips for successful data mining projects.
In this work, the TREPAN algorithm is enhanced and extended for extracting decision trees from neural networks. We empirically evaluated the performance of the algorithm on a set of databases from real world events. This benchmark enhancement was achieved by adapting Single-test TREPAN and C4.5 decision tree induction algorithms to analyze the datasets. The models are then compared with X-TREPAN for
comprehensibility and classification accuracy. Furthermore, we validate the experimentations by applying statistical methods. Finally, the modified algorithm is extended to work with multi-class regression problems and the ability to comprehend generalized feed forward networks is achieved.
ANALYSIS AND COMPARISON STUDY OF DATA MINING ALGORITHMS USING RAPIDMINERIJCSEA Journal
Comparison study of algorithms is very much required before implementing them for the needs of any
organization. The comparisons of algorithms are depending on the various parameters such as data
frequency, types of data and relationship among the attributes in a given data set. There are number of
learning and classifications algorithms are used to analyse, learn patterns and categorize data are
available. But the problem is the one to find the best algorithm according to the problem and desired
output. The desired result has always been higher accuracy in predicting future values or events from the
given dataset. Algorithms taken for the comparisons study are Neural net, SVM, Naïve Bayes, BFT and
Decision stump. These top algorithms are most influential data mining algorithms in the research
community. These algorithms have been considered and mostly used in the field of knowledge discovery
and data mining.
A short introduction to statistical learningtuxette
This document provides an introduction to statistical learning methods. It begins with background information on statistical learning problems and discusses concepts like underfitting, overfitting, and consistency. It then summarizes decision trees and random forests, describing how they are learned from data and make predictions. Support vector machines and neural networks are also briefly mentioned. Key goals of statistical learning methods include accuracy on training data as well as generalization to new data.
Machine Learning: Foundations Course Number 0368403401butest
This machine learning course will cover theoretical and practical machine learning concepts. It will include 4 homework assignments and programming in Matlab. Lectures will be supplemented by student-submitted class notes in LaTeX. Topics will include learning approaches like storage and retrieval, rule learning, and flexible model estimation, as well as applications in areas like control, medical diagnosis, and web search. A final exam format has not been determined yet.
Machine Learning: Foundations Course Number 0368403401butest
This machine learning foundations course will consist of 4 homework assignments, both theoretical and programming problems in Matlab. There will be a final exam. Students will work in groups of 2-3 to take notes during classes in LaTeX format. These class notes will contribute 30% to the overall grade. The course will cover basic machine learning concepts like storage and retrieval, learning rules, estimating flexible models, and applications in areas like control, medical diagnosis, and document retrieval.
RECENT ADVANCES in PREDICTIVE (MACHINE) LEARNINGbutest
This document provides an introduction to recent advances in predictive machine learning, specifically support vector machines and boosted decision trees. It begins with an overview of predictive learning and common methods. It then describes kernel methods, including how they were extended to support vector machines. Next, it discusses extending decision trees with boosting. The document concludes by comparing support vector machines and boosted decision trees, and noting they are not the only recent advances in machine learning.
Presentation on Machine Learning and Data Miningbutest
The document discusses the differences between automatic learning/machine learning and data mining. It provides definitions for supervised vs unsupervised learning, what automated induction is, and the base components of data mining. Additionally, it outlines differences in the scientific approach between automatic learning and data mining, as well as differences from an industry perspective, including common data mining techniques used and tips for successful data mining projects.
In this work, the TREPAN algorithm is enhanced and extended for extracting decision trees from neural networks. We empirically evaluated the performance of the algorithm on a set of databases from real world events. This benchmark enhancement was achieved by adapting Single-test TREPAN and C4.5 decision tree induction algorithms to analyze the datasets. The models are then compared with X-TREPAN for
comprehensibility and classification accuracy. Furthermore, we validate the experimentations by applying statistical methods. Finally, the modified algorithm is extended to work with multi-class regression problems and the ability to comprehend generalized feed forward networks is achieved.
ANALYSIS AND COMPARISON STUDY OF DATA MINING ALGORITHMS USING RAPIDMINERIJCSEA Journal
Comparison study of algorithms is very much required before implementing them for the needs of any
organization. The comparisons of algorithms are depending on the various parameters such as data
frequency, types of data and relationship among the attributes in a given data set. There are number of
learning and classifications algorithms are used to analyse, learn patterns and categorize data are
available. But the problem is the one to find the best algorithm according to the problem and desired
output. The desired result has always been higher accuracy in predicting future values or events from the
given dataset. Algorithms taken for the comparisons study are Neural net, SVM, Naïve Bayes, BFT and
Decision stump. These top algorithms are most influential data mining algorithms in the research
community. These algorithms have been considered and mostly used in the field of knowledge discovery
and data mining.
This document provides an introduction to statistical model selection. It discusses various approaches to model selection including predictive risk, Bayesian methods, information theoretic measures like AIC and MDL, and adaptive methods. The key goals of model selection are to understand the bias-variance tradeoff and select models that offer the best guaranteed predictive performance on new data. Model selection aims to find the right level of complexity to explain patterns in available data while avoiding overfitting.
A preliminary survey on optimized multiobjective metaheuristic methods for da...ijcsit
The present survey provides the state-of-the-art of research, copiously devoted to Evolutionary Approach
(EAs) for clustering exemplified with a diversity of evolutionary computations. The Survey provides a
nomenclature that highlights some aspects that are very important in the context of evolutionary data
clustering. The paper missions the clustering trade-offs branched out with wide-ranging Multi Objective
Evolutionary Approaches (MOEAs) methods. Finally, this study addresses the potential challenges of
MOEA design and data clustering, along with conclusions and recommendations for novice and
researchers by positioning most promising paths of future research.
Selective inference and single-cell differential analysistuxette
This document discusses selective inference and single-cell differential analysis. It introduces the problem of "double dipping" in the standard single-cell analysis pipeline where the same dataset is used for clustering and differential analysis. Two approaches for addressing this are presented: 1) A method that perturbs clusters before testing for differences, and 2) A test based on a truncated distribution that assumes clusters and genes are given separately. Experiments applying these methods to real single-cell datasets are described. The document outlines challenges in extending these approaches to more complex analyses.
Incorporating Prior Domain Knowledge Into Inductive Machine ...butest
This document discusses incorporating prior domain knowledge into inductive machine learning. It introduces concepts of inductive machine learning such as consistency, generalization, and convergence. Incorporating prior domain knowledge can help improve performance in these three key areas. The document proposes analyzing how domain knowledge can be incorporated while balancing risks. It also presents a new hierarchical modeling method called VQSVM and tests it on imbalanced datasets.
The document summarizes statistical pattern recognition techniques. It is divided into 9 sections that cover topics like dimensionality reduction, classifiers, classifier combination, and unsupervised classification. The goal of pattern recognition is supervised or unsupervised classification of patterns based on features. Dimensionality reduction aims to reduce the number of features to address the curse of dimensionality when samples are limited. Multiple classifiers can be combined through techniques like stacking, bagging, and boosting. Unsupervised classification uses clustering algorithms to construct decision boundaries without labeled training data.
Fundementals of Machine Learning and Deep Learning ParrotAI
Introduction to machine learning and deep learning to beginners.Learn the applications of machine learning and deep learning and how ti can solve different problems
An Optimal Approach For Knowledge Protection In Structured Frequent PatternsWaqas Tariq
Data mining is valuable technology to facilitate the extraction of useful patterns and trends from large volume of data. When these patterns are to be shared in a collaborative environment, they must be protectively shared among the parties concerned in order to preserve the confidentiality of the sensitive data. Sharing of information may be in the form of datasets or in any of the structured patterns like trees, graphs, lattices, etc., This paper propose a sanitization algorithm for protecting sensitive data in a structured frequent pattern(tree).
This document provides an overview of supervised and unsupervised learning, with a focus on clustering as an unsupervised learning technique. It describes the basic concepts of clustering, including how clustering groups similar data points together without labeled categories. It then covers two main clustering algorithms - k-means, a partitional clustering method, and hierarchical clustering. It discusses aspects like cluster representation, distance functions, strengths and weaknesses of different approaches. The document aims to introduce clustering and compare it with supervised learning.
The document defines several key machine learning and neural network terminology including:
- Activation level - The output value of a neuron in an artificial neural network.
- Activation function - The function that determines the output value of a neuron based on its net input.
- Attributes - Properties of an instance that can be used to determine its classification in machine learning tasks.
- Axon - The output part of a biological neuron that transmits signals to other neurons.
The document provides an overview of concepts and topics to be covered in the MIS End Term Exam for AI and A2 on February 6th 2020, including: decision trees, classifier algorithms like ID3, CART and Naive Bayes; supervised and unsupervised learning; clustering using K-means; bias and variance; overfitting and underfitting; ensemble learning techniques like bagging and random forests; and the use of test and train data.
Rainfall Prediction using Data-Core Based Fuzzy Min-Max Neural Network for Cl...IJERA Editor
This paper proposes the Rainfall Prediction System by using classification technique. The advanced and modified neural network called Data Core Based Fuzzy Min Max Neural Network (DCFMNN) is used for pattern classification. This classification method is applied to predict Rainfall. The neural network called fuzzy min max neural network (FMNN) that creates hyperboxes for classification and predication, has a problem of overlapping neurons that resoled in DCFMNN to give greater accuracy. This system is composed of forming of hyperboxes, and two kinds of neurons called as Overlapping Neurons and Classifying neurons, and classification used for prediction. For each kind of hyperbox its data core and geometric center of data is calculated. The advantage of this method is it gives high accuracy and strong robustness. According to evaluation results we can say that this system gives better prediction of rainfall and classification tool in real environment.
The Advancement and Challenges in Computational Physics - PhdassistancePhD Assistance
For the last five decades, computational physics has been a valuable scientific instrument in physics. In comparison to using only theoretical and experimental approaches, it has enabled physicists to understand complex problems better. Computational physics was mostly a scientific activity at the time, with relatively few organised undergraduate study.
Ph.D. Assistance serves as an external mentor to brainstorm your idea and translate that into a research model. Hiring a mentor or tutor is common and therefore let your research committee know about the same. We do not offer any writing services without the involvement of the researcher.
Learn More: https://bit.ly/3AUvG0y
Contact Us:
Website: https://www.phdassistance.com/
UK NO: +44–1143520021
India No: +91–4448137070
WhatsApp No: +91 91769 66446
Email: info@phdassistance.com
ANALYTICAL STUDY OF FEATURE EXTRACTION TECHNIQUES IN OPINION MININGcsandit
Although opinion mining is in a nascent stage of development but still the ground is set for dense growth of researches in the field. One of the important activities of opinion mining is to extract opinions of people based on characteristics of the object under study. Feature extraction in opinion mining can be done by various ways like that of clustering, support vector machines
etc. This paper is an attempt to appraise the various techniques of feature extraction. The first part discusses various techniques and second part makes a detailed appraisal of the major techniques used for feature extraction
Analytical study of feature extraction techniques in opinion miningcsandit
Although opinion mining is in a nascent stage of development but still the ground is set for
dense growth of researches in the field. One of the important activities of opinion mining is to
extract opinions of people based on characteristics of the object under study. Feature extraction
in opinion mining can be done by various ways like that of clustering, support vector machines
etc. This paper is an attempt to appraise the various techniques of feature extraction. The first
part discusses various techniques and second part makes a detailed appraisal of the major
techniques used for feature extraction
This document describes the construction and demonstration of a Ternary Prediction Classification Model (TPCM) for determining the predictive capabilities of mathematical models. The author first defines key concepts like testability and degrees of confidence in observations and predictions. He then outlines current techniques for obtaining confidence in predictions, including data-oriented modeling, theoretical modeling, and hybrid approaches. Finally, the author constructs the TPCM, which aims to integrate experimental uncertainty with mathematical operations to allow models and data to be evaluated using the same framework. In Section 2, the author will demonstrate the TPCM using Hooke's law and experimental data, and in Section 3 will discuss conclusions and possible objections.
The Advancement and Challenges in Computational Physics - PhdassistancePhD Assistance
For the last five decades, computational physics has been a valuable scientific instrument in physics. In comparison to using only theoretical and experimental approaches, it has enabled physicists to understand complex problems better. Computational physics was mostly a scientific activity at the time, with relatively few organised undergraduate study.
Ph.D. Assistance serves as an external mentor to brainstorm your idea and translate that into a research model. Hiring a mentor or tutor is common and therefore let your research committee know about the same. We do not offer any writing services without the involvement of the researcher.
Learn More: https://bit.ly/3AUvG0y
Contact Us:
Website: https://www.phdassistance.com/
UK NO: +44–1143520021
India No: +91–4448137070
WhatsApp No: +91 91769 66446
Email: info@phdassistance.com
COMPARISON OF WAVELET NETWORK AND LOGISTIC REGRESSION IN PREDICTING ENTERPRIS...ijcsit
Enterprise financial distress or failure includes bankruptcy prediction, financial distress, corporate performance prediction and credit risk estimation. The aim of this paper is that using wavelet networks innon-linear combination prediction to solve ARMA (Auto-Regressive and Moving Average) model problem.ARMA model need estimate the value of all parameters in the model, it has a large amount of computation.Under this aim, the paper provides an extensive review of Wavelet networks and Logistic regression. Itdiscussed the Wavelet neural network structure, Wavelet network model training algorithm, Accuracy rateand error rate (accuracy of classification, Type I error, and Type II error). The main research opportunity exist a proposed of business failure prediction model (wavelet network model and logistic regression
model). The empirical research which is comparison of Wavelet Network and Logistic Regression on training and forecasting sample, the result shows that this wavelet network model is high accurate and the overall prediction accuracy, Type Ⅰerror and Type Ⅱ error, wavelet networks model is better thanlogistic regression model.
This document describes a new genetic algorithm (GA)-based system for predicting the future performance of individual stocks. The system uses GAs for inductive machine learning rather than optimization. It is compared to a neural network system using data from over 1,600 stocks. The study finds that the GA system can predict stock returns 12 weeks in the future and that combining GA and neural network forecasts provides synergistic benefits.
Machine Learning and Real-World ApplicationsMachinePulse
This presentation was created by Ajay, Machine Learning Scientist at MachinePulse, to present at a Meetup on Jan. 30, 2015. These slides provide an overview of widely used machine learning algorithms. The slides conclude with examples of real world applications.
Ajay Ramaseshan, is a Machine Learning Scientist at MachinePulse. He holds a Bachelors degree in Computer Science from NITK, Suratkhal and a Master in Machine Learning and Data Mining from Aalto University School of Science, Finland. He has extensive experience in the machine learning domain and has dealt with various real world problems.
Machine learning in Healthcare - WeCloudDataWeCloudData
This document provides an overview of machine learning applications in healthcare. It discusses how machine learning can be used to improve diagnosis, treatment, and other areas by automating processes and analyzing patient information. Different types of health data that can be used as inputs for machine learning models are described, including medical information, molecular features, and medical images. Common machine learning tasks for images like detection, segmentation, and diagnosis are also outlined. The document then explains the basic machine learning process of gathering and cleaning data, building and evaluating models, and deploying selected models. Common machine learning algorithms like linear regression, regularization techniques, and deep learning approaches like convolutional neural networks are briefly introduced.
This document provides an introduction to statistical model selection. It discusses various approaches to model selection including predictive risk, Bayesian methods, information theoretic measures like AIC and MDL, and adaptive methods. The key goals of model selection are to understand the bias-variance tradeoff and select models that offer the best guaranteed predictive performance on new data. Model selection aims to find the right level of complexity to explain patterns in available data while avoiding overfitting.
A preliminary survey on optimized multiobjective metaheuristic methods for da...ijcsit
The present survey provides the state-of-the-art of research, copiously devoted to Evolutionary Approach
(EAs) for clustering exemplified with a diversity of evolutionary computations. The Survey provides a
nomenclature that highlights some aspects that are very important in the context of evolutionary data
clustering. The paper missions the clustering trade-offs branched out with wide-ranging Multi Objective
Evolutionary Approaches (MOEAs) methods. Finally, this study addresses the potential challenges of
MOEA design and data clustering, along with conclusions and recommendations for novice and
researchers by positioning most promising paths of future research.
Selective inference and single-cell differential analysistuxette
This document discusses selective inference and single-cell differential analysis. It introduces the problem of "double dipping" in the standard single-cell analysis pipeline where the same dataset is used for clustering and differential analysis. Two approaches for addressing this are presented: 1) A method that perturbs clusters before testing for differences, and 2) A test based on a truncated distribution that assumes clusters and genes are given separately. Experiments applying these methods to real single-cell datasets are described. The document outlines challenges in extending these approaches to more complex analyses.
Incorporating Prior Domain Knowledge Into Inductive Machine ...butest
This document discusses incorporating prior domain knowledge into inductive machine learning. It introduces concepts of inductive machine learning such as consistency, generalization, and convergence. Incorporating prior domain knowledge can help improve performance in these three key areas. The document proposes analyzing how domain knowledge can be incorporated while balancing risks. It also presents a new hierarchical modeling method called VQSVM and tests it on imbalanced datasets.
The document summarizes statistical pattern recognition techniques. It is divided into 9 sections that cover topics like dimensionality reduction, classifiers, classifier combination, and unsupervised classification. The goal of pattern recognition is supervised or unsupervised classification of patterns based on features. Dimensionality reduction aims to reduce the number of features to address the curse of dimensionality when samples are limited. Multiple classifiers can be combined through techniques like stacking, bagging, and boosting. Unsupervised classification uses clustering algorithms to construct decision boundaries without labeled training data.
Fundementals of Machine Learning and Deep Learning ParrotAI
Introduction to machine learning and deep learning to beginners.Learn the applications of machine learning and deep learning and how ti can solve different problems
An Optimal Approach For Knowledge Protection In Structured Frequent PatternsWaqas Tariq
Data mining is valuable technology to facilitate the extraction of useful patterns and trends from large volume of data. When these patterns are to be shared in a collaborative environment, they must be protectively shared among the parties concerned in order to preserve the confidentiality of the sensitive data. Sharing of information may be in the form of datasets or in any of the structured patterns like trees, graphs, lattices, etc., This paper propose a sanitization algorithm for protecting sensitive data in a structured frequent pattern(tree).
This document provides an overview of supervised and unsupervised learning, with a focus on clustering as an unsupervised learning technique. It describes the basic concepts of clustering, including how clustering groups similar data points together without labeled categories. It then covers two main clustering algorithms - k-means, a partitional clustering method, and hierarchical clustering. It discusses aspects like cluster representation, distance functions, strengths and weaknesses of different approaches. The document aims to introduce clustering and compare it with supervised learning.
The document defines several key machine learning and neural network terminology including:
- Activation level - The output value of a neuron in an artificial neural network.
- Activation function - The function that determines the output value of a neuron based on its net input.
- Attributes - Properties of an instance that can be used to determine its classification in machine learning tasks.
- Axon - The output part of a biological neuron that transmits signals to other neurons.
The document provides an overview of concepts and topics to be covered in the MIS End Term Exam for AI and A2 on February 6th 2020, including: decision trees, classifier algorithms like ID3, CART and Naive Bayes; supervised and unsupervised learning; clustering using K-means; bias and variance; overfitting and underfitting; ensemble learning techniques like bagging and random forests; and the use of test and train data.
Rainfall Prediction using Data-Core Based Fuzzy Min-Max Neural Network for Cl...IJERA Editor
This paper proposes the Rainfall Prediction System by using classification technique. The advanced and modified neural network called Data Core Based Fuzzy Min Max Neural Network (DCFMNN) is used for pattern classification. This classification method is applied to predict Rainfall. The neural network called fuzzy min max neural network (FMNN) that creates hyperboxes for classification and predication, has a problem of overlapping neurons that resoled in DCFMNN to give greater accuracy. This system is composed of forming of hyperboxes, and two kinds of neurons called as Overlapping Neurons and Classifying neurons, and classification used for prediction. For each kind of hyperbox its data core and geometric center of data is calculated. The advantage of this method is it gives high accuracy and strong robustness. According to evaluation results we can say that this system gives better prediction of rainfall and classification tool in real environment.
The Advancement and Challenges in Computational Physics - PhdassistancePhD Assistance
For the last five decades, computational physics has been a valuable scientific instrument in physics. In comparison to using only theoretical and experimental approaches, it has enabled physicists to understand complex problems better. Computational physics was mostly a scientific activity at the time, with relatively few organised undergraduate study.
Ph.D. Assistance serves as an external mentor to brainstorm your idea and translate that into a research model. Hiring a mentor or tutor is common and therefore let your research committee know about the same. We do not offer any writing services without the involvement of the researcher.
Learn More: https://bit.ly/3AUvG0y
Contact Us:
Website: https://www.phdassistance.com/
UK NO: +44–1143520021
India No: +91–4448137070
WhatsApp No: +91 91769 66446
Email: info@phdassistance.com
ANALYTICAL STUDY OF FEATURE EXTRACTION TECHNIQUES IN OPINION MININGcsandit
Although opinion mining is in a nascent stage of development but still the ground is set for dense growth of researches in the field. One of the important activities of opinion mining is to extract opinions of people based on characteristics of the object under study. Feature extraction in opinion mining can be done by various ways like that of clustering, support vector machines
etc. This paper is an attempt to appraise the various techniques of feature extraction. The first part discusses various techniques and second part makes a detailed appraisal of the major techniques used for feature extraction
Analytical study of feature extraction techniques in opinion miningcsandit
Although opinion mining is in a nascent stage of development but still the ground is set for
dense growth of researches in the field. One of the important activities of opinion mining is to
extract opinions of people based on characteristics of the object under study. Feature extraction
in opinion mining can be done by various ways like that of clustering, support vector machines
etc. This paper is an attempt to appraise the various techniques of feature extraction. The first
part discusses various techniques and second part makes a detailed appraisal of the major
techniques used for feature extraction
This document describes the construction and demonstration of a Ternary Prediction Classification Model (TPCM) for determining the predictive capabilities of mathematical models. The author first defines key concepts like testability and degrees of confidence in observations and predictions. He then outlines current techniques for obtaining confidence in predictions, including data-oriented modeling, theoretical modeling, and hybrid approaches. Finally, the author constructs the TPCM, which aims to integrate experimental uncertainty with mathematical operations to allow models and data to be evaluated using the same framework. In Section 2, the author will demonstrate the TPCM using Hooke's law and experimental data, and in Section 3 will discuss conclusions and possible objections.
The Advancement and Challenges in Computational Physics - PhdassistancePhD Assistance
For the last five decades, computational physics has been a valuable scientific instrument in physics. In comparison to using only theoretical and experimental approaches, it has enabled physicists to understand complex problems better. Computational physics was mostly a scientific activity at the time, with relatively few organised undergraduate study.
Ph.D. Assistance serves as an external mentor to brainstorm your idea and translate that into a research model. Hiring a mentor or tutor is common and therefore let your research committee know about the same. We do not offer any writing services without the involvement of the researcher.
Learn More: https://bit.ly/3AUvG0y
Contact Us:
Website: https://www.phdassistance.com/
UK NO: +44–1143520021
India No: +91–4448137070
WhatsApp No: +91 91769 66446
Email: info@phdassistance.com
COMPARISON OF WAVELET NETWORK AND LOGISTIC REGRESSION IN PREDICTING ENTERPRIS...ijcsit
Enterprise financial distress or failure includes bankruptcy prediction, financial distress, corporate performance prediction and credit risk estimation. The aim of this paper is that using wavelet networks innon-linear combination prediction to solve ARMA (Auto-Regressive and Moving Average) model problem.ARMA model need estimate the value of all parameters in the model, it has a large amount of computation.Under this aim, the paper provides an extensive review of Wavelet networks and Logistic regression. Itdiscussed the Wavelet neural network structure, Wavelet network model training algorithm, Accuracy rateand error rate (accuracy of classification, Type I error, and Type II error). The main research opportunity exist a proposed of business failure prediction model (wavelet network model and logistic regression
model). The empirical research which is comparison of Wavelet Network and Logistic Regression on training and forecasting sample, the result shows that this wavelet network model is high accurate and the overall prediction accuracy, Type Ⅰerror and Type Ⅱ error, wavelet networks model is better thanlogistic regression model.
This document describes a new genetic algorithm (GA)-based system for predicting the future performance of individual stocks. The system uses GAs for inductive machine learning rather than optimization. It is compared to a neural network system using data from over 1,600 stocks. The study finds that the GA system can predict stock returns 12 weeks in the future and that combining GA and neural network forecasts provides synergistic benefits.
Machine Learning and Real-World ApplicationsMachinePulse
This presentation was created by Ajay, Machine Learning Scientist at MachinePulse, to present at a Meetup on Jan. 30, 2015. These slides provide an overview of widely used machine learning algorithms. The slides conclude with examples of real world applications.
Ajay Ramaseshan, is a Machine Learning Scientist at MachinePulse. He holds a Bachelors degree in Computer Science from NITK, Suratkhal and a Master in Machine Learning and Data Mining from Aalto University School of Science, Finland. He has extensive experience in the machine learning domain and has dealt with various real world problems.
Machine learning in Healthcare - WeCloudDataWeCloudData
This document provides an overview of machine learning applications in healthcare. It discusses how machine learning can be used to improve diagnosis, treatment, and other areas by automating processes and analyzing patient information. Different types of health data that can be used as inputs for machine learning models are described, including medical information, molecular features, and medical images. Common machine learning tasks for images like detection, segmentation, and diagnosis are also outlined. The document then explains the basic machine learning process of gathering and cleaning data, building and evaluating models, and deploying selected models. Common machine learning algorithms like linear regression, regularization techniques, and deep learning approaches like convolutional neural networks are briefly introduced.
Machine learning algorithms show promise in improving medical image analysis and diagnosis by helping physicians more accurately interpret images. Such algorithms can be trained using labeled medical image data to learn the differences between benign and malignant tumors, and then apply that learning to analyze new images and predict the likelihood of tumors being benign or malignant. However, it is important to address the potential pitfalls of machine learning and ensure its safe and effective use in medical applications.
The document discusses artificial neural networks (ANNs) and summarizes key information about soft computing techniques, ANNs, and some specific ANN models including perceptrons, ADALINE, and MADALINE. It defines soft computing as a collection of computational techniques including neural networks, fuzzy logic, and evolutionary computing. ANNs are modeled after the human brain and consist of interconnected neurons that can learn from examples. Perceptrons, ADALINE, and MADALINE are early ANN models that use different learning rules to update weights and biases.
The document discusses artificial neural networks (ANNs) and summarizes key information about ANNs and related topics. It defines soft computing as a field that aims to build intelligent machines using techniques like ANNs, fuzzy logic, and evolutionary computing. ANNs are modeled after biological neural networks and consist of interconnected nodes that can learn from data. Early ANN models like the perceptron, ADALINE, and MADALINE are described along with their learning rules and architectures. Applications of ANNs in various domains are also listed.
The document discusses soft computing and artificial neural networks. It provides an overview of soft computing techniques including artificial neural networks (ANNs), fuzzy logic, and evolutionary computing. It then focuses on ANNs, describing their biological inspiration from neurons in the brain. The basic components of ANNs are discussed including network architecture, learning algorithms, and activation functions. Specific ANN models are then summarized, such as the perceptron, ADALINE, and their learning rules. Applications of ANNs are also briefly mentioned.
Deep Learning: concepts and use cases (October 2018)Julien SIMON
An introduction to Deep Learning theory
Neurons & Neural Networks
The Training Process
Backpropagation
Optimizers
Common network architectures and use cases
Convolutional Neural Networks
Recurrent Neural Networks
Long Short Term Memory Networks
Generative Adversarial Networks
Getting started
This document provides an overview of Bayesian networks through a 3-day tutorial. Day 1 introduces Bayesian networks and provides a medical diagnosis example. It defines key concepts like Bayes' theorem and influence diagrams. Day 2 covers propagation algorithms, demonstrating how evidence is propagated through a sample chain network. Day 3 will cover learning from data and using continuous variables and software. The overview outlines propagation algorithms for singly and multiply connected graphs.
This document discusses Thales' work on implementing deep recurrent neural networks for sequence learning using the Spark framework. It begins by providing context on Thales' large data sources and need for sequence learning. It then discusses recurrent neural networks and challenges training them at scale. The document outlines Thales' Spark implementation of deep learning algorithms, including recurrent layers. Finally, it proposes two potential use cases - predictive maintenance using sensor data sequences, and sentiment analysis of text sequences from social media. The predictive maintenance case study shows recurrent networks outperforming logistic regression, and sentiment analysis experiments demonstrate recurrent networks achieving higher accuracy than other models on larger datasets.
IRJET- Disease Prediction using Machine LearningIRJET Journal
This document discusses using machine learning techniques to predict diseases based on patient symptoms. Specifically, it proposes using naive bayes, k-nearest neighbors (KNN), and logistic regression algorithms on structured and unstructured hospital data to predict diseases like diabetes, malaria, jaundice, dengue, and tuberculosis. The system is intended to make disease prediction more accessible to end users by analyzing their symptoms without needing to visit a doctor. It aims to improve prediction accuracy by handling both structured and unstructured data using machine learning models.
Artificial neural networks are a form of artificial intelligence inspired by biological neural networks. They are composed of interconnected processing units that can learn patterns from data through training. Neural networks are well-suited for tasks like pattern recognition, classification, and prediction. They learn by example without being explicitly programmed, similarly to how the human brain learns.
The document discusses several machine learning algorithms: artificial neural networks, naive Bayes classification, and decision trees. It provides examples of applying these algorithms to classify banking customers and compare their performance. Neural networks had the highest accuracy at 88.92% but the longest processing time of 8.01 seconds. Naive Bayes had the shortest processing time of 0.02 seconds but the lowest accuracy at 86.88%. Decision trees achieved 88.98% accuracy with a processing time of 0.04 seconds. The document also provides real-world examples of applying neural networks to tasks like ECG analysis, credit risk management, and environmental modeling.
Sep2009 Introduction to Medical Expert Decision Support Systems for Mayo Clinicdoc_vogt
This document discusses expert systems and their potential application to medical decision support. It provides background on expert systems, describing their components like knowledge bases, inference engines, and explanation facilities. It also discusses different approaches to building expert systems, such as production rules, pattern recognition, fuzzy logic, and imagery analysis. The document then discusses some examples of medical expert systems from the past and potential benefits of developing new expert decision support systems.
This document provides an overview of probability, statistics, and their applications in engineering. It defines key probability and statistics concepts like trials, outcomes, random experiments, and frequency distributions. It explains how engineers use statistics and probability to analyze data from tests and experiments to better understand product quality and failure rates. Examples are given of measures of central tendency like mean and median, measures of variation like standard deviation and variance, and the normal distribution curve. Engineering applications include using these analytical techniques to assess results from a class and compare two data histograms.
1) Machine learning involves developing algorithms that learn from data without being explicitly programmed. It is a multidisciplinary field that includes statistics, mathematics, artificial intelligence, and more.
2) There are three main areas of machine learning: supervised learning which uses labeled training data, unsupervised learning which finds patterns in unlabeled data, and reinforcement learning which learns from rewards and punishments.
3) Supervised learning is well-studied and includes techniques like support vector machines, neural networks, decision trees, and Bayesian algorithms which are used for problems like pattern recognition, regression, and time series analysis.
Classifiers are algorithms that map input data to categories in order to build models for predicting unknown data. There are several types of classifiers that can be used including logistic regression, decision trees, random forests, support vector machines, Naive Bayes, and neural networks. Each uses different techniques such as splitting data, averaging predictions, or maximizing margins to classify data. The best classifier depends on the problem and achieving high accuracy, sensitivity, and specificity.
Machine Learning, Data Mining, Genetic Algorithms, Neural ...butest
The document discusses various machine learning concepts including concept learning, decision trees, genetic algorithms, and neural networks. It provides details on each concept, such as how concept learning uses positive and negative examples to learn concepts, how decision trees use nodes and branches to classify data, and how genetic algorithms and neural networks are modeled after biological processes. It also gives examples of applications for each concept, such as using decision trees for classification and neural networks for tasks like handwriting recognition where explicit rules are difficult to define.
This document discusses classifier performance evaluation. It covers the following key points in 3 sentences:
The document outlines different methods for evaluating classifier performance, including hold out, k-fold cross validation, and bootstrap aggregating. It emphasizes that evaluation should be treated as statistical hypothesis testing using metrics like accuracy, precision, and recall calculated from a confusion matrix. Proper evaluation also requires partitioning data into separate training and test sets to avoid overfitting and get an accurate estimate of a classifier's generalization performance.
Racines en haut et feuilles en bas : les arbres en mathstuxette
1. The document discusses methods for clustering and differential analysis of Hi-C matrices, which represent the 3D organization of DNA.
2. It proposes extending Ward's hierarchical clustering to directly use Hi-C similarity matrices while enforcing adjacency constraints. A fast algorithm was also developed.
3. A new method called "treediff" was created to perform differential analysis of Hi-C matrices based on the Wasserstein distance between hierarchical clusterings. Software implementations of these methods were also developed.
Méthodes à noyaux pour l’intégration de données hétérogènestuxette
The document discusses a presentation about multi-omics data integration methods using kernel methods. The presentation introduces kernel methods, how they can be used to integrate heterogeneous omics data, and examples of applications. Specifically, it discusses using kernel methods to perform unsupervised transformation-based integration of multi-omics data. It also presents an application of constrained kernel hierarchical clustering to analyze Hi-C data by directly using Hi-C matrices as kernels.
Méthodologies d'intégration de données omiquestuxette
This document presents a presentation on multi-omics data integration methods given by Nathalie Vialaneix on December 13, 2023. The presentation discusses different types of omics data that can be integrated, both vertically across different levels of omics data on the same samples and horizontally across similar types of omics data on different samples. It also discusses different analysis approaches that can be taken, including supervised and unsupervised methods. The rest of the presentation focuses on unsupervised transformation-based integration methods using kernels.
The document discusses current and future work on analyzing Hi-C data and differential analysis of Hi-C matrices. It describes a clustering method developed to partition chromosomes based on Hi-C matrix similarity. It also introduces a new method called treediff for differential analysis of Hi-C data that calculates the distance between hierarchical clusterings. Current work includes reviewing differential analysis methods, investigating differential subtrees with multiple testing control, and inferring chromatin interaction networks.
Can deep learning learn chromatin structure from sequence?tuxette
This document discusses a deep learning model called ORCA that can predict chromatin structure from DNA sequence. The model uses a neural network with an encoder to extract features from sequence and a decoder to predict Hi-C matrices. It was trained on Hi-C data from multiple cell types and can predict interactions between regions at various resolutions. The model accurately captures features like CTCF-mediated loops and can predict effects of structural variants on chromatin structure. It allows for in silico mutagenesis to study how mutations may alter 3D genome organization.
Multi-omics data integration methods: kernel and other machine learning appro...tuxette
The document discusses multi-omics data integration methods, particularly kernel methods. It describes how kernel methods transform data into similarity matrices between samples rather than relying on variable space. Multiple kernel integration approaches are presented that combine multiple similarity matrices into a consensus kernel in an unsupervised manner, such as through a STATIS-like framework that maximizes the similarity between kernels. Examples of applications to datasets from the TARA Oceans expedition are given.
This document provides an overview of the MetaboWean and Idefics projects. MetaboWean aims to study the co-evolution of gut microbiota and epithelium during suckling-to-weaning transition in rabbits, using metabolomics, metagenomics, and single-cell RNA sequencing data. Idefics integrates multiple omics datasets from human skin samples to understand relationships between microorganisms and molecules and how they are structured in patient groups. The datasets include metagenomics, metabolomics, and proteomics from host and microbiota.
Rserve, renv, flask, Vue.js dans un docker pour intégrer des données omiques ...tuxette
ASTERICS is an interactive and integrative data analysis tool for omics data. It uses Rserve and PyRserve with Flask and Vue.js in a Docker container to integrate omics data. The backend uses Rserve and PyRserve with Flask on the server side, while the frontend uses Vue.js. This architecture was chosen for its open source and light design. Data communication between Rserve and PyRserve is limited, requiring an object database. ASTERICS is deployed using three Docker containers for R, Python, and
Apprentissage pour la biologie moléculaire et l’analyse de données omiquestuxette
This document summarizes a scientific presentation about molecular biology and omics data analysis. The presentation covers topics related to analyzing large omics datasets using methods like kernel methods, graphical models, and neural networks to learn gene regulation networks and predict phenotypes. Key challenges addressed are handling big data, missing values, non-Gaussian data types like counts and compositional data. The goal is to better understand complex biological systems from multi-omics data.
Quelques résultats préliminaires de l'évaluation de méthodes d'inférence de r...tuxette
The document summarizes preliminary results from evaluating methods for inferring gene regulatory networks from expression data in Bacillus subtilis. It finds that recall of the known network is generally poor (<20% for random forest), but inferred clusters still retain biological information about common regulators. It plans to confirm results, test restricting edges to sigma factors, and explore other inference methods like Bayesian networks and ARACNE.
Intégration de données omiques multi-échelles : méthodes à noyau et autres ap...tuxette
The document discusses methods for integrating multi-scale omics data using kernel and machine learning approaches. It describes how omics data is large, heterogeneous, and multi-scaled, creating bottlenecks for analysis. Methods discussed for data integration include multiple kernel learning to combine different relational datasets in an unsupervised way. The methods are applied to integrate different datasets from the TARA Oceans expedition to identify patterns in ocean microbial communities. Improving interpretability of the methods and making them more accessible to biological users is discussed.
Journal club: Validation of cluster analysis results on validation datatuxette
This document presents a framework for validating cluster analysis results on validation data. It describes situations where clustering is inferential versus descriptive and recommends using validation data separate from the data used for clustering. A typology of validation methods is provided, including validation based on the clustering method or results, and evaluation using internal validation, external validation, visual properties, or stability measures.
The document discusses the differences between overfitting and overparametrization in machine learning models. It explores how random forests may exhibit a phenomenon known as "double descent" where test error initially decreases then increases with more parameters before decreasing again. While double descent has been observed in other models, the document questions whether it is directly due to model complexity in random forests since very large trees may be unable to fully interpolate extremely large datasets.
SOMbrero : un package R pour les cartes auto-organisatricestuxette
SOMbrero is an R package that implements self-organizing map (SOM) algorithms. It can handle numeric, non-numeric, and relational data. The package contains functions for training SOMs, diagnosing results, and plotting maps. It also includes tools like a shiny app and vignettes to aid users without programming experience. SOMbrero supports missing data imputation and extends SOM to relational datasets through non-Euclidean distance measures.
Graph Neural Network for Phenotype Predictiontuxette
This document describes a study on using graph neural networks (GNNs) for phenotype prediction from gene expression data. The objectives are to determine if including network information can improve predictions, which network types work best, and if GNNs can learn network inferences. It provides background on GNNs and how they generalize convolutional layers to graph data. The authors implemented a GNN model from previous work as a starting point and tested it on different network types to see which network information is most useful for predictions. Their methodology involves comparing GNN performance to other methods like random forests using 10-fold cross validation.
A short and naive introduction to using network in prediction modelstuxette
The document provides an introduction to using network information in prediction models. It discusses representing a network as a graph with a Laplacian matrix. The Laplacian captures properties like random walks on the graph and heat diffusion. Eigenvectors of the Laplacian related to small eigenvalues are strongly tied to graph structure. The document discusses using the Laplacian in prediction models by working in the feature space defined by the Laplacian eigenvectors or directly regularizing a linear model with the Laplacian. This introduces network information and encourages similar contributions from connected nodes. The approaches are applied to problems like predicting phenotypes from gene expression using a known gene network.
PPT on Alternate Wetting and Drying presented at the three-day 'Training and Validation Workshop on Modules of Climate Smart Agriculture (CSA) Technologies in South Asia' workshop on April 22, 2024.
Mending Clothing to Support Sustainable Fashion_CIMaR 2024.pdfSelcen Ozturkcan
Ozturkcan, S., Berndt, A., & Angelakis, A. (2024). Mending clothing to support sustainable fashion. Presented at the 31st Annual Conference by the Consortium for International Marketing Research (CIMaR), 10-13 Jun 2024, University of Gävle, Sweden.
(June 12, 2024) Webinar: Development of PET theranostics targeting the molecu...Scintica Instrumentation
Targeting Hsp90 and its pathogen Orthologs with Tethered Inhibitors as a Diagnostic and Therapeutic Strategy for cancer and infectious diseases with Dr. Timothy Haystead.
CLASS 12th CHEMISTRY SOLID STATE ppt (Animated)eitps1506
Description:
Dive into the fascinating realm of solid-state physics with our meticulously crafted online PowerPoint presentation. This immersive educational resource offers a comprehensive exploration of the fundamental concepts, theories, and applications within the realm of solid-state physics.
From crystalline structures to semiconductor devices, this presentation delves into the intricate principles governing the behavior of solids, providing clear explanations and illustrative examples to enhance understanding. Whether you're a student delving into the subject for the first time or a seasoned researcher seeking to deepen your knowledge, our presentation offers valuable insights and in-depth analyses to cater to various levels of expertise.
Key topics covered include:
Crystal Structures: Unravel the mysteries of crystalline arrangements and their significance in determining material properties.
Band Theory: Explore the electronic band structure of solids and understand how it influences their conductive properties.
Semiconductor Physics: Delve into the behavior of semiconductors, including doping, carrier transport, and device applications.
Magnetic Properties: Investigate the magnetic behavior of solids, including ferromagnetism, antiferromagnetism, and ferrimagnetism.
Optical Properties: Examine the interaction of light with solids, including absorption, reflection, and transmission phenomena.
With visually engaging slides, informative content, and interactive elements, our online PowerPoint presentation serves as a valuable resource for students, educators, and enthusiasts alike, facilitating a deeper understanding of the captivating world of solid-state physics. Explore the intricacies of solid-state materials and unlock the secrets behind their remarkable properties with our comprehensive presentation.
SDSS1335+0728: The awakening of a ∼ 106M⊙ black hole⋆Sérgio Sacani
Context. The early-type galaxy SDSS J133519.91+072807.4 (hereafter SDSS1335+0728), which had exhibited no prior optical variations during the preceding two decades, began showing significant nuclear variability in the Zwicky Transient Facility (ZTF) alert stream from December 2019 (as ZTF19acnskyy). This variability behaviour, coupled with the host-galaxy properties, suggests that SDSS1335+0728 hosts a ∼ 106M⊙ black hole (BH) that is currently in the process of ‘turning on’. Aims. We present a multi-wavelength photometric analysis and spectroscopic follow-up performed with the aim of better understanding the origin of the nuclear variations detected in SDSS1335+0728. Methods. We used archival photometry (from WISE, 2MASS, SDSS, GALEX, eROSITA) and spectroscopic data (from SDSS and LAMOST) to study the state of SDSS1335+0728 prior to December 2019, and new observations from Swift, SOAR/Goodman, VLT/X-shooter, and Keck/LRIS taken after its turn-on to characterise its current state. We analysed the variability of SDSS1335+0728 in the X-ray/UV/optical/mid-infrared range, modelled its spectral energy distribution prior to and after December 2019, and studied the evolution of its UV/optical spectra. Results. From our multi-wavelength photometric analysis, we find that: (a) since 2021, the UV flux (from Swift/UVOT observations) is four times brighter than the flux reported by GALEX in 2004; (b) since June 2022, the mid-infrared flux has risen more than two times, and the W1−W2 WISE colour has become redder; and (c) since February 2024, the source has begun showing X-ray emission. From our spectroscopic follow-up, we see that (i) the narrow emission line ratios are now consistent with a more energetic ionising continuum; (ii) broad emission lines are not detected; and (iii) the [OIII] line increased its flux ∼ 3.6 years after the first ZTF alert, which implies a relatively compact narrow-line-emitting region. Conclusions. We conclude that the variations observed in SDSS1335+0728 could be either explained by a ∼ 106M⊙ AGN that is just turning on or by an exotic tidal disruption event (TDE). If the former is true, SDSS1335+0728 is one of the strongest cases of an AGNobserved in the process of activating. If the latter were found to be the case, it would correspond to the longest and faintest TDE ever observed (or another class of still unknown nuclear transient). Future observations of SDSS1335+0728 are crucial to further understand its behaviour. Key words. galaxies: active– accretion, accretion discs– galaxies: individual: SDSS J133519.91+072807.4
Sexuality - Issues, Attitude and Behaviour - Applied Social Psychology - Psyc...PsychoTech Services
A proprietary approach developed by bringing together the best of learning theories from Psychology, design principles from the world of visualization, and pedagogical methods from over a decade of training experience, that enables you to: Learn better, faster!
Candidate young stellar objects in the S-cluster: Kinematic analysis of a sub...Sérgio Sacani
Context. The observation of several L-band emission sources in the S cluster has led to a rich discussion of their nature. However, a definitive answer to the classification of the dusty objects requires an explanation for the detection of compact Doppler-shifted Brγ emission. The ionized hydrogen in combination with the observation of mid-infrared L-band continuum emission suggests that most of these sources are embedded in a dusty envelope. These embedded sources are part of the S-cluster, and their relationship to the S-stars is still under debate. To date, the question of the origin of these two populations has been vague, although all explanations favor migration processes for the individual cluster members. Aims. This work revisits the S-cluster and its dusty members orbiting the supermassive black hole SgrA* on bound Keplerian orbits from a kinematic perspective. The aim is to explore the Keplerian parameters for patterns that might imply a nonrandom distribution of the sample. Additionally, various analytical aspects are considered to address the nature of the dusty sources. Methods. Based on the photometric analysis, we estimated the individual H−K and K−L colors for the source sample and compared the results to known cluster members. The classification revealed a noticeable contrast between the S-stars and the dusty sources. To fit the flux-density distribution, we utilized the radiative transfer code HYPERION and implemented a young stellar object Class I model. We obtained the position angle from the Keplerian fit results; additionally, we analyzed the distribution of the inclinations and the longitudes of the ascending node. Results. The colors of the dusty sources suggest a stellar nature consistent with the spectral energy distribution in the near and midinfrared domains. Furthermore, the evaporation timescales of dusty and gaseous clumps in the vicinity of SgrA* are much shorter ( 2yr) than the epochs covered by the observations (≈15yr). In addition to the strong evidence for the stellar classification of the D-sources, we also find a clear disk-like pattern following the arrangements of S-stars proposed in the literature. Furthermore, we find a global intrinsic inclination for all dusty sources of 60 ± 20◦, implying a common formation process. Conclusions. The pattern of the dusty sources manifested in the distribution of the position angles, inclinations, and longitudes of the ascending node strongly suggests two different scenarios: the main-sequence stars and the dusty stellar S-cluster sources share a common formation history or migrated with a similar formation channel in the vicinity of SgrA*. Alternatively, the gravitational influence of SgrA* in combination with a massive perturber, such as a putative intermediate mass black hole in the IRS 13 cluster, forces the dusty objects and S-stars to follow a particular orbital arrangement. Key words. stars: black holes– stars: formation– Galaxy: center– galaxies: star formation
TOPIC OF DISCUSSION: CENTRIFUGATION SLIDESHARE.pptxshubhijain836
Centrifugation is a powerful technique used in laboratories to separate components of a heterogeneous mixture based on their density. This process utilizes centrifugal force to rapidly spin samples, causing denser particles to migrate outward more quickly than lighter ones. As a result, distinct layers form within the sample tube, allowing for easy isolation and purification of target substances.
Microbial interaction
Microorganisms interacts with each other and can be physically associated with another organisms in a variety of ways.
One organism can be located on the surface of another organism as an ectobiont or located within another organism as endobiont.
Microbial interaction may be positive such as mutualism, proto-cooperation, commensalism or may be negative such as parasitism, predation or competition
Types of microbial interaction
Positive interaction: mutualism, proto-cooperation, commensalism
Negative interaction: Ammensalism (antagonism), parasitism, predation, competition
I. Mutualism:
It is defined as the relationship in which each organism in interaction gets benefits from association. It is an obligatory relationship in which mutualist and host are metabolically dependent on each other.
Mutualistic relationship is very specific where one member of association cannot be replaced by another species.
Mutualism require close physical contact between interacting organisms.
Relationship of mutualism allows organisms to exist in habitat that could not occupied by either species alone.
Mutualistic relationship between organisms allows them to act as a single organism.
Examples of mutualism:
i. Lichens:
Lichens are excellent example of mutualism.
They are the association of specific fungi and certain genus of algae. In lichen, fungal partner is called mycobiont and algal partner is called
II. Syntrophism:
It is an association in which the growth of one organism either depends on or improved by the substrate provided by another organism.
In syntrophism both organism in association gets benefits.
Compound A
Utilized by population 1
Compound B
Utilized by population 2
Compound C
utilized by both Population 1+2
Products
In this theoretical example of syntrophism, population 1 is able to utilize and metabolize compound A, forming compound B but cannot metabolize beyond compound B without co-operation of population 2. Population 2is unable to utilize compound A but it can metabolize compound B forming compound C. Then both population 1 and 2 are able to carry out metabolic reaction which leads to formation of end product that neither population could produce alone.
Examples of syntrophism:
i. Methanogenic ecosystem in sludge digester
Methane produced by methanogenic bacteria depends upon interspecies hydrogen transfer by other fermentative bacteria.
Anaerobic fermentative bacteria generate CO2 and H2 utilizing carbohydrates which is then utilized by methanogenic bacteria (Methanobacter) to produce methane.
ii. Lactobacillus arobinosus and Enterococcus faecalis:
In the minimal media, Lactobacillus arobinosus and Enterococcus faecalis are able to grow together but not alone.
The synergistic relationship between E. faecalis and L. arobinosus occurs in which E. faecalis require folic acid
PPT on Sustainable Land Management presented at the three-day 'Training and Validation Workshop on Modules of Climate Smart Agriculture (CSA) Technologies in South Asia' workshop on April 22, 2024.
Sustainable Land Management - Climate Smart Agriculture
An introduction to neural network
1. An introduction to neural networks
Nathalie Vialaneix & Hyphen
nathalie.vialaneix@inra.fr
http://www.nathalievialaneix.eu &
https://www.hyphen-stat.com/en/
DATE ET LIEU
Nathalie Vialaneix & Hyphen | Introduction to neural networks 1/41
2. Outline
1 Statistical / machine / automatic learning
Background and notations
Underfitting / Overfitting
Consistency
Avoiding overfitting
2 (non deep) Neural networks
Presentation of multi-layer perceptrons
Theoretical properties of perceptrons
Learning perceptrons
Learning in practice
3 Deep neural networks
Overview
CNN
Nathalie Vialaneix & Hyphen | Introduction to neural networks 2/41
3. Outline
1 Statistical / machine / automatic learning
Background and notations
Underfitting / Overfitting
Consistency
Avoiding overfitting
2 (non deep) Neural networks
Presentation of multi-layer perceptrons
Theoretical properties of perceptrons
Learning perceptrons
Learning in practice
3 Deep neural networks
Overview
CNN
Nathalie Vialaneix & Hyphen | Introduction to neural networks 3/41
5. Background
Purpose: predict Y from X;
What we have: n observations of (X, Y), (x1, y1), . . . , (xn, yn);
Nathalie Vialaneix & Hyphen | Introduction to neural networks 4/41
6. Background
Purpose: predict Y from X;
What we have: n observations of (X, Y), (x1, y1), . . . , (xn, yn);
What we want: estimate unknown Y from new X: xn+1, . . . , xm.
Nathalie Vialaneix & Hyphen | Introduction to neural networks 4/41
7. Basics
From (xi, yi)i, definition of a machine, Φn
s.t.:
ˆynew = Φn
(xnew).
Nathalie Vialaneix & Hyphen | Introduction to neural networks 5/41
8. Basics
From (xi, yi)i, definition of a machine, Φn
s.t.:
ˆynew = Φn
(xnew).
if Y is numeric, Φn
is called a regression function fonction de
régression;
if Y is a factor, Φn
is called a classifier classifieur;
Nathalie Vialaneix & Hyphen | Introduction to neural networks 5/41
9. Basics
From (xi, yi)i, definition of a machine, Φn
s.t.:
ˆynew = Φn
(xnew).
if Y is numeric, Φn
is called a regression function fonction de
régression;
if Y is a factor, Φn
is called a classifier classifieur;
Φn
is said to be trained or learned from the observations (xi, yi)i.
Nathalie Vialaneix & Hyphen | Introduction to neural networks 5/41
10. Basics
From (xi, yi)i, definition of a machine, Φn
s.t.:
ˆynew = Φn
(xnew).
if Y is numeric, Φn
is called a regression function fonction de
régression;
if Y is a factor, Φn
is called a classifier classifieur;
Φn
is said to be trained or learned from the observations (xi, yi)i.
Desirable properties
accuracy to the observations: predictions made on known data are
close to observed values;
Nathalie Vialaneix & Hyphen | Introduction to neural networks 5/41
11. Basics
From (xi, yi)i, definition of a machine, Φn
s.t.:
ˆynew = Φn
(xnew).
if Y is numeric, Φn
is called a regression function fonction de
régression;
if Y is a factor, Φn
is called a classifier classifieur;
Φn
is said to be trained or learned from the observations (xi, yi)i.
Desirable properties
accuracy to the observations: predictions made on known data are
close to observed values;
generalization ability: predictions made on new data are also
accurate.
Nathalie Vialaneix & Hyphen | Introduction to neural networks 5/41
12. Basics
From (xi, yi)i, definition of a machine, Φn
s.t.:
ˆynew = Φn
(xnew).
if Y is numeric, Φn
is called a regression function fonction de
régression;
if Y is a factor, Φn
is called a classifier classifieur;
Φn
is said to be trained or learned from the observations (xi, yi)i.
Desirable properties
accuracy to the observations: predictions made on known data are
close to observed values;
generalization ability: predictions made on new data are also
accurate.
Conflicting objectives!!
Nathalie Vialaneix & Hyphen | Introduction to neural networks 5/41
13. Underfitting/Overfitting sous/sur - apprentissage
Function x → y to be estimated
Nathalie Vialaneix & Hyphen | Introduction to neural networks 6/41
14. Underfitting/Overfitting sous/sur - apprentissage
Observations we might have
Nathalie Vialaneix & Hyphen | Introduction to neural networks 6/41
15. Underfitting/Overfitting sous/sur - apprentissage
Observations we do have
Nathalie Vialaneix & Hyphen | Introduction to neural networks 6/41
16. Underfitting/Overfitting sous/sur - apprentissage
First estimation from the observations: underfitting
Nathalie Vialaneix & Hyphen | Introduction to neural networks 6/41
17. Underfitting/Overfitting sous/sur - apprentissage
Second estimation from the observations: accurate estimation
Nathalie Vialaneix & Hyphen | Introduction to neural networks 6/41
18. Underfitting/Overfitting sous/sur - apprentissage
Third estimation from the observations: overfitting
Nathalie Vialaneix & Hyphen | Introduction to neural networks 6/41
20. Errors
training error (measures the accuracy to the observations)
Nathalie Vialaneix & Hyphen | Introduction to neural networks 7/41
21. Errors
training error (measures the accuracy to the observations)
if y is a factor: misclassification rate
{ˆyi yi, i = 1, . . . , n}
n
Nathalie Vialaneix & Hyphen | Introduction to neural networks 7/41
22. Errors
training error (measures the accuracy to the observations)
if y is a factor: misclassification rate
{ˆyi yi, i = 1, . . . , n}
n
if y is numeric: mean square error (MSE)
1
n
n
i=1
(ˆyi − yi)2
Nathalie Vialaneix & Hyphen | Introduction to neural networks 7/41
23. Errors
training error (measures the accuracy to the observations)
if y is a factor: misclassification rate
{ˆyi yi, i = 1, . . . , n}
n
if y is numeric: mean square error (MSE)
1
n
n
i=1
(ˆyi − yi)2
or root mean square error (RMSE) or pseudo-R2
: 1−MSE/Var((yi)i)
Nathalie Vialaneix & Hyphen | Introduction to neural networks 7/41
24. Errors
training error (measures the accuracy to the observations)
if y is a factor: misclassification rate
{ˆyi yi, i = 1, . . . , n}
n
if y is numeric: mean square error (MSE)
1
n
n
i=1
(ˆyi − yi)2
or root mean square error (RMSE) or pseudo-R2
: 1−MSE/Var((yi)i)
test error: a way to prevent overfitting (estimates the generalization
error) is the simple validation
Nathalie Vialaneix & Hyphen | Introduction to neural networks 7/41
25. Errors
training error (measures the accuracy to the observations)
if y is a factor: misclassification rate
{ˆyi yi, i = 1, . . . , n}
n
if y is numeric: mean square error (MSE)
1
n
n
i=1
(ˆyi − yi)2
or root mean square error (RMSE) or pseudo-R2
: 1−MSE/Var((yi)i)
test error: a way to prevent overfitting (estimates the generalization
error) is the simple validation
1 split the data into training/test sets (usually 80%/20%)
2 train Φn
from the training dataset
3 compute the test error from the remaining data
Nathalie Vialaneix & Hyphen | Introduction to neural networks 7/41
30. Practical consequences
A machine or an algorithm is a method that:
start from a given set of prediction functions C
use observations (xi, yi)i to pick the “best” prediction function
according to what has already been observed.
This is often done by the estimation of some parameters (e.g.,
coefficients in linear models, weights in neural networks, ...)
Nathalie Vialaneix & Hyphen | Introduction to neural networks 9/41
31. Practical consequences
A machine or an algorithm is a method that:
start from a given set of prediction functions C
use observations (xi, yi)i to pick the “best” prediction function
according to what has already been observed.
This is often done by the estimation of some parameters (e.g.,
coefficients in linear models, weights in neural networks, ...)
C can depend on user choices (e.g., achitecture, number of neurons in
neural networks) ⇒ hyper-parameters
parameters (learned from data) hyper-parameters (can not be learned from data)
Nathalie Vialaneix & Hyphen | Introduction to neural networks 9/41
32. Practical consequences
A machine or an algorithm is a method that:
start from a given set of prediction functions C
use observations (xi, yi)i to pick the “best” prediction function
according to what has already been observed.
This is often done by the estimation of some parameters (e.g.,
coefficients in linear models, weights in neural networks, ...)
C can depend on user choices (e.g., achitecture, number of neurons in
neural networks) ⇒ hyper-parameters
parameters (learned from data) hyper-parameters (can not be learned from data)
hyper-parameters often control the richness of C: must be carefully tuned
to ensure a good accuracy and avoid overfitting
Nathalie Vialaneix & Hyphen | Introduction to neural networks 9/41
33. Model selection / Fair error evaluation
Several strategies can be used to estimate the error of a set C and to be
able to select the best family of models C (tuning of hyper-parameters):
split the data into a training/test set (∼ 67/33%): the model is
estimated with the training dataset and a test error is computed with
the remaining observations;
Nathalie Vialaneix & Hyphen | Introduction to neural networks 10/41
34. Model selection / Fair error evaluation
Several strategies can be used to estimate the error of a set C and to be
able to select the best family of models C (tuning of hyper-parameters):
split the data into a training/test set (∼ 67/33%)
cross validation: split the data into L folds. For each fold, train a
model without the data included in the current fold and compute the
error with the data included in the fold: the averaging of these L errors
is the cross validation error;
fold 1 fold 2 fold 3 . . . fold K
train without fold 2: Φ−2
error fold 2: err(φ−2
, fold 2)
CV error: 1
K l err(Φ−l
, fold l)
Nathalie Vialaneix & Hyphen | Introduction to neural networks 10/41
35. Model selection / Fair error evaluation
Several strategies can be used to estimate the error of a set C and to be
able to select the best family of models C (tuning of hyper-parameters):
split the data into a training/test set (∼ 67/33%)
cross validation: split the data into L folds. For each fold, train a
model without the data included in the current fold and compute the
error with the data included in the fold: the averaging of these L errors
is the cross validation error;
out-of-bag error: create B bootstrap samples (size n taken with
replacement in the original sample) and compute a prediction based
on Out-Of-Bag samples (see next slide).
Nathalie Vialaneix & Hyphen | Introduction to neural networks 10/41
36. Out-of-bag observations, prediction and error
OOB (Out-Of Bags) error: error based on the observations not included in
the “bag”:
(xi, yi)
i ∈ T 1
i ∈ T b i T B
Φ1
Φb
ΦB
OOB prediction: ΦB
(xi)
used to train the model
prediction algorithm
Nathalie Vialaneix & Hyphen | Introduction to neural networks 11/41
37. Other model selection methods and strategies to avoid
overfitting
Main idea: Penalize the training error with something that represents the
complexity of the model
model selection with explicit penalization strategy: minimize (∼) error
+ complexity
Example: BIC/AIC criterion in linear models
−2L + log n × p
avoiding overfitting with implicit penalization strategy: constrain the
parameters w to take their values within a reduced subset
min error(w) + λ w
with w the parameter of the model (learned) and . typically the 1 or
2 norm
Nathalie Vialaneix & Hyphen | Introduction to neural networks 12/41
38. Outline
1 Statistical / machine / automatic learning
Background and notations
Underfitting / Overfitting
Consistency
Avoiding overfitting
2 (non deep) Neural networks
Presentation of multi-layer perceptrons
Theoretical properties of perceptrons
Learning perceptrons
Learning in practice
3 Deep neural networks
Overview
CNN
Nathalie Vialaneix & Hyphen | Introduction to neural networks 13/41
39. What are (artificial) neural networks?
Common properties
(artificial) “Neural networks”: general name for supervised and
unsupervised methods developed in (vague) analogy to the brain;
Nathalie Vialaneix & Hyphen | Introduction to neural networks 14/41
40. What are (artificial) neural networks?
Common properties
(artificial) “Neural networks”: general name for supervised and
unsupervised methods developed in (vague) analogy to the brain;
combination (network) of simple elements (neurons).
Nathalie Vialaneix & Hyphen | Introduction to neural networks 14/41
41. What are (artificial) neural networks?
Common properties
(artificial) “Neural networks”: general name for supervised and
unsupervised methods developed in (vague) analogy to the brain;
combination (network) of simple elements (neurons).
Example of graphical representation:
INPUTS
OUTPUTS
Nathalie Vialaneix & Hyphen | Introduction to neural networks 14/41
42. Different types of neural networks
A neural network is defined by:
1 the network structure;
2 the neuron type.
Nathalie Vialaneix & Hyphen | Introduction to neural networks 15/41
43. Different types of neural networks
A neural network is defined by:
1 the network structure;
2 the neuron type.
Standard examples
Multilayer perceptrons (MLP) Perceptron multi-couches: dedicated to
supervised problems (classification and regression);
Nathalie Vialaneix & Hyphen | Introduction to neural networks 15/41
44. Different types of neural networks
A neural network is defined by:
1 the network structure;
2 the neuron type.
Standard examples
Multilayer perceptrons (MLP) Perceptron multi-couches: dedicated to
supervised problems (classification and regression);
Radial basis function networks (RBF): same purpose but based on
local smoothing;
Nathalie Vialaneix & Hyphen | Introduction to neural networks 15/41
45. Different types of neural networks
A neural network is defined by:
1 the network structure;
2 the neuron type.
Standard examples
Multilayer perceptrons (MLP) Perceptron multi-couches: dedicated to
supervised problems (classification and regression);
Radial basis function networks (RBF): same purpose but based on
local smoothing;
Self-organizing maps (SOM also sometimes called Kohonen’s maps)
or Topographic maps: dedicated to unsupervised problems
(clustering), self-organized;
. . .
Nathalie Vialaneix & Hyphen | Introduction to neural networks 15/41
46. Different types of neural networks
A neural network is defined by:
1 the network structure;
2 the neuron type.
Standard examples
Multilayer perceptrons (MLP) Perceptron multi-couches: dedicated to
supervised problems (classification and regression);
Radial basis function networks (RBF): same purpose but based on
local smoothing;
Self-organizing maps (SOM also sometimes called Kohonen’s maps)
or Topographic maps: dedicated to unsupervised problems
(clustering), self-organized;
. . .
In this talk, focus on MLP.
Nathalie Vialaneix & Hyphen | Introduction to neural networks 15/41
47. MLP: Advantages/Drawbacks
Advantages
classification OR regression (i.e., Y can be a numeric variable or a
factor);
non parametric method: flexible;
good theoretical properties.
Nathalie Vialaneix & Hyphen | Introduction to neural networks 16/41
48. MLP: Advantages/Drawbacks
Advantages
classification OR regression (i.e., Y can be a numeric variable or a
factor);
non parametric method: flexible;
good theoretical properties.
Drawbacks
hard to train (high computational cost, especially when d is large);
overfit easily;
“black box” models (hard to interpret).
Nathalie Vialaneix & Hyphen | Introduction to neural networks 16/41
49. References
Advised references:
[Bishop, 1995, Ripley, 1996] overview of the topic from a learning (more
than statistical) perspective
[Devroye et al., 1996, Györfi et al., 2002] in dedicated chapters present
statistical properties of perceptrons
Nathalie Vialaneix & Hyphen | Introduction to neural networks 17/41
50. Analogy to the brain
1 a neuron collects signals
from neighboring
neurons through its
dendrites
connexions which frequently lead to activating a neuron are enforced (tend
to have an increasing impact on the destination neuron)
Nathalie Vialaneix & Hyphen | Introduction to neural networks 18/41
51. Analogy to the brain
1 a neuron collects signals
from neighboring
neurons through its
dendrites
2 when total signal is
above a given threshold,
the neuron is activated
connexions which frequently lead to activating a neuron are enforced (tend
to have an increasing impact on the destination neuron)
Nathalie Vialaneix & Hyphen | Introduction to neural networks 18/41
52. Analogy to the brain
1 a neuron collects signals
from neighboring
neurons through its
dendrites
2 when total signal is
above a given threshold,
the neuron is activated
3 ... and a signal is sent to
other neurons through
the axon
connexions which frequently lead to activating a neuron are enforced (tend
to have an increasing impact on the destination neuron)
Nathalie Vialaneix & Hyphen | Introduction to neural networks 18/41
53. First model of artificial neuron
[Mc Culloch and Pitts, 1943, Rosenblatt, 1958, Rosenblatt, 1962]
x(1)
x(2)
x(p)
f(x)Σ+
w1
w2
wp
w0
f : x ∈ Rp
→ 1 p
j=1
wjx(j)+w0 ≥ 0
Nathalie Vialaneix & Hyphen | Introduction to neural networks 19/41
54. (artificial) Perceptron
Layers
MLP have one input layer (x ∈ Rp
), one output layer (y ∈ R or
∈ {1, . . . , K − 1} values) and several hidden layers;
no connections within a layer;
connections between two consecutive layers (feedforward).
Example (regression, y ∈ R):
INPUTS
x = (x(1)
, . . . , x(p)
)
Nathalie Vialaneix & Hyphen | Introduction to neural networks 20/41
55. (artificial) Perceptron
Layers
MLP have one input layer (x ∈ Rp
), one output layer (y ∈ R or
∈ {1, . . . , K − 1} values) and several hidden layers;
no connections within a layer;
connections between two consecutive layers (feedforward).
Example (regression, y ∈ R):
INPUTS
x = (x(1)
, . . . , x(p)
) Layer 1
weights w
(1)
jk
Nathalie Vialaneix & Hyphen | Introduction to neural networks 20/41
56. (artificial) Perceptron
Layers
MLP have one input layer (x ∈ Rp
), one output layer (y ∈ R or
∈ {1, . . . , K − 1} values) and several hidden layers;
no connections within a layer;
connections between two consecutive layers (feedforward).
Example (regression, y ∈ R):
INPUTS
x = (x(1)
, . . . , x(p)
) Layer 1
Nathalie Vialaneix & Hyphen | Introduction to neural networks 20/41
57. (artificial) Perceptron
Layers
MLP have one input layer (x ∈ Rp
), one output layer (y ∈ R or
∈ {1, . . . , K − 1} values) and several hidden layers;
no connections within a layer;
connections between two consecutive layers (feedforward).
Example (regression, y ∈ R):
INPUTS
x = (x(1)
, . . . , x(p)
) Layer 1 Layer 2
Nathalie Vialaneix & Hyphen | Introduction to neural networks 20/41
58. (artificial) Perceptron
Layers
MLP have one input layer (x ∈ Rp
), one output layer (y ∈ R or
∈ {1, . . . , K − 1} values) and several hidden layers;
no connections within a layer;
connections between two consecutive layers (feedforward).
Example (regression, y ∈ R):
INPUTS
x = (x(1)
, . . . , x(p)
) Layer 1 Layer 2 y
OUTPUTS
2 hidden layer MLP
Nathalie Vialaneix & Hyphen | Introduction to neural networks 20/41
59. A neuron in MLP
v1
v2
v3
×w1
×w2
×w3
+
w0 (Bias Biais)
Nathalie Vialaneix & Hyphen | Introduction to neural networks 21/41
60. A neuron in MLP
v1
v2
v3
×w1
×w2
×w3 ×w2
×w1
+
w0
Nathalie Vialaneix & Hyphen | Introduction to neural networks 21/41
61. A neuron in MLP
v1
v2
v3
×w1
×w2
×w3 ×w2
×w1
+
w0
Standard activation functions fonctions de transfert / d’activation
Biologically inspired: Heaviside function
h(z) =
0 if z < 0
1 otherwise.
Nathalie Vialaneix & Hyphen | Introduction to neural networks 21/41
62. A neuron in MLP
v1
v2
v3
×w1
×w2
×w3 ×w2
×w1
+
w0
Standard activation functions
Main issue with the Heaviside function: not continuous!
Identity
h(z) = z
Nathalie Vialaneix & Hyphen | Introduction to neural networks 21/41
63. A neuron in MLP
v1
v2
v3
×w1
×w2
×w3 ×w2
×w1
+
w0
Standard activation functions
But identity activation function gives linear model if used with one hidden
layer: not flexible enough
Logistic function
h(z) = 1
1+exp(−z)
Nathalie Vialaneix & Hyphen | Introduction to neural networks 21/41
64. A neuron in MLP
v1
v2
v3
×w1
×w2
×w3 ×w2
×w1
+
w0
Standard activation functions
Another popular activation function (useful to model positive real numbers)
Rectified linear (ReLU)
h(z) = max(0, z)
Nathalie Vialaneix & Hyphen | Introduction to neural networks 21/41
65. A neuron in MLP
v1
v2
v3
×w1
×w2
×w3 ×w2
×w1
+
w0
General sigmoid
sigmoid: nondecreasing function h : R → R such that
lim
z→+∞
h(z) = 1 lim
z→−∞
h(z) = 0
Nathalie Vialaneix & Hyphen | Introduction to neural networks 21/41
66. Focus on one-hidden-layer perceptrons (R package nnet)
Regression case
x(1)
x(2)
x(p) w(1)
w(2)+
w
(0)
1
+
w
(0)
Q
Φ(x)
Φ(x) =
Q
k=1
w
(2)
k
hk x w
(1)
k
+ w
(0)
k
+ w
(2)
0
, with hk a (logistic) sigmoid
Nathalie Vialaneix & Hyphen | Introduction to neural networks 22/41
67. Focus on one-hidden-layer perceptrons (R package nnet)
Binary classification case (y ∈ {0, 1})
x(1)
x(2)
x(p) w(1)
w(2)+
w
(0)
1
+
w
(0)
Q
φ(x)
φ(x) = h0
Q
k=1
w
(2)
k
hk x w
(1)
k
+ w
(0)
k
+ w
(2)
0
with h0 logistic sigmoid or identity.
Nathalie Vialaneix & Hyphen | Introduction to neural networks 22/41
68. Focus on one-hidden-layer perceptrons (R package nnet)
Binary classification case (y ∈ {0, 1})
x(1)
x(2)
x(p) w(1)
w(2)+
w
(0)
1
+
w
(0)
Q
φ(x)
decision with:
Φ(x) =
0 if φ(x) < 1/2
1 otherwise
Nathalie Vialaneix & Hyphen | Introduction to neural networks 22/41
69. Focus on one-hidden-layer perceptrons (R package nnet)
Extension to any classification problem in {1, . . . , K − 1}
x(1)
x(2)
x(p) w(1)
w(2)+
w
(0)
1
+
w
(0)
Q
φ(x)
Straightforward extension to multiple classes with a multiple output
perceptron (number of output units equal to K) and a maximum probability
rule for the decision.
Nathalie Vialaneix & Hyphen | Introduction to neural networks 22/41
70. Theoretical properties of perceptrons
This section answers two questions:
1 can we approximate any function g : [0, 1]p
→ R arbitrary well with a
perceptron?
Nathalie Vialaneix & Hyphen | Introduction to neural networks 23/41
71. Theoretical properties of perceptrons
This section answers two questions:
1 can we approximate any function g : [0, 1]p
→ R arbitrary well with a
perceptron?
2 when a perceptron is trained with i.i.d. observations from an arbitrary
random variable pair (X, Y), is it consistent? (i.e., does it reach the
minimum possible error asymptotically when the number of
observations grows to infinity?)
Nathalie Vialaneix & Hyphen | Introduction to neural networks 23/41
72. Illustration of the universal approximation property
Simple examples
a function to approximate: g : [0, 1] → sin 1
x+0.1
Nathalie Vialaneix & Hyphen | Introduction to neural networks 24/41
73. Illustration of the universal approximation property
Simple examples
a function to approximate: g : [0, 1] → sin 1
x+0.1
trying to approximate (how this is performed is explained later in this talk) this
function with MLP having different numbers of neurons on their
hidden layer
Nathalie Vialaneix & Hyphen | Introduction to neural networks 24/41
74. Sketch of main properties of 1-hidden layer MLP
1 universal approximation [Pinkus, 1999, Devroye et al., 1996,
Hornik, 1991, Hornik, 1993, Stinchcombe, 1999]: 1-hidden-layer MLP
can approximate arbitrary well any (regular enough) function (but, in
theory, the number of neurons can grow arbitrarily for that)
2 consistency
[Farago and Lugosi, 1993, Devroye et al., 1996, White, 1990,
White, 1991, Barron, 1994, McCaffrey and Gallant, 1994]:
1-hidden-layer MLP are universally consistent with a number of
neurons that grows slower than O n
log n to +∞
⇒ the number of neurons controls the richness of the MLP and has to
increase with n
Nathalie Vialaneix & Hyphen | Introduction to neural networks 25/41
75. Empirical error minimization
Given i.i.d. observations of (X, Y), (xi, yi)i, how to choose the weights w?
x(1)
x(2)
x(p) w(1)
w(2)+
w
(0)
1
+
w
(0)
Q
Φw(x)
Nathalie Vialaneix & Hyphen | Introduction to neural networks 26/41
76. Empirical error minimization
Given i.i.d. observations of (X, Y), (xi, yi)i, how to choose the weights w?
Standard approach: minimize the empirical L2 risk:
Ln(w) =
n
i=1
[fw(xi) − yi]2
with
yi ∈ R for the regression case
yi ∈ {0, 1} for the classification case, with the associated decision rule
x → 1{Φw (x)≤1/2}.
Nathalie Vialaneix & Hyphen | Introduction to neural networks 26/41
77. Empirical error minimization
Given i.i.d. observations of (X, Y), (xi, yi)i, how to choose the weights w?
Standard approach: minimize the empirical L2 risk:
Ln(w) =
n
i=1
[fw(xi) − yi]2
with
yi ∈ R for the regression case
yi ∈ {0, 1} for the classification case, with the associated decision rule
x → 1{Φw (x)≤1/2}.
But: Ln(w) is not convex in w ⇒ general optimization problem
Nathalie Vialaneix & Hyphen | Introduction to neural networks 26/41
78. Optimization with gradient descent
Method: initialize (randomly or with some prior knowledge) the weights
w(0) ∈ RQp+2Q+1
Batch approach: for t = 1, . . . , T
w(t + 1) = w(t) − µ(t) wLn(w)(w(t));
Nathalie Vialaneix & Hyphen | Introduction to neural networks 27/41
79. Optimization with gradient descent
Method: initialize (randomly or with some prior knowledge) the weights
w(0) ∈ RQp+2Q+1
Batch approach: for t = 1, . . . , T
w(t + 1) = w(t) − µ(t) wLn(w)(w(t));
online (or stochastic) approach: write
Ln(w)(w) =
n
i=1
[fw(xi) − yi]2
=Ei
and for t = 1, . . . , T, randomly pick i ∈ {1, . . . , n} and update:
w(t + 1) = w(t) − µ(t) wEi(w(t)).
Nathalie Vialaneix & Hyphen | Introduction to neural networks 27/41
80. Discussion about practical choices for this approach
batch version converges (from an optimization point of view) to a local
minimum of the error for a good choice of µ(t) but convergence can
be slow
stochastic version is usually inefficient but is useful for large datasets
(n large)
more efficient algorithms exist to solve the optimization task. The one
implemented in the R package nnet uses higher order derivatives
(BFGS algorithm) but this is a very active area of research in which
many improvements have been made recently
in all cases, solutions returned are, at best, local minima which
strongly depends on the initialization: using more than one
initialization state is advised, as well as checking the variability among
a few runs
Nathalie Vialaneix & Hyphen | Introduction to neural networks 28/41
81. Gradient backpropagation method
[Rumelhart and Mc Clelland, 1986]
The gradient backpropagation rétropropagation du gradient principle is
used to easily computes gradients in perceptrons (or in other types of
neural network):
Nathalie Vialaneix & Hyphen | Introduction to neural networks 29/41
82. Gradient backpropagation method
[Rumelhart and Mc Clelland, 1986]
The gradient backpropagation rétropropagation du gradient principle is
used to easily computes gradients in perceptrons (or in other types of
neural network):
This way, stochastic gradient descent alternates:
a forward step which aims at calculating outputs from all observations
xi given a value of the weights w
a backward step in which the gradient backpropagation principle is
used to obtain the gradient for the current weights w
Nathalie Vialaneix & Hyphen | Introduction to neural networks 29/41
90. Initialization and stopping of the training algorithm
1 How to initialize weights? Standard choices w
(1)
jk
∼ N(0, 1/
√
p) and
w
(2)
k
∼ N(0, 1/
√
Q)
Nathalie Vialaneix & Hyphen | Introduction to neural networks 31/41
91. Initialization and stopping of the training algorithm
1 How to initialize weights? Standard choices w
(1)
jk
∼ N(0, 1/
√
p) and
w
(2)
k
∼ N(0, 1/
√
Q)
2 When to stop the algorithm? (gradient descent or alike) Standard
choices:
bounded T
target value of the error ˆLn(w)
target value of the evolution ˆLn(w(t)) − ˆLn(w(t + 1))
Nathalie Vialaneix & Hyphen | Introduction to neural networks 31/41
92. Initialization and stopping of the training algorithm
1 How to initialize weights? Standard choices w
(1)
jk
∼ N(0, 1/
√
p) and
w
(2)
k
∼ N(0, 1/
√
Q)
In the R package nnet, weights are sampled uniformly between
[−0.5, 0.5] or between − 1
maxi X
(j)
i
, 1
maxi X
(j)
i
if X(j) is large.
2 When to stop the algorithm? (gradient descent or alike) Standard
choices:
bounded T
target value of the error ˆLn(w)
target value of the evolution ˆLn(w(t)) − ˆLn(w(t + 1))
In the R package nnet, a combination of the three criteria is used and
tunable.
Nathalie Vialaneix & Hyphen | Introduction to neural networks 31/41
93. Strategies to avoid overfitting
Properly tune Q with a CV or a bootstrap estimation of the
generalization ability of the method
Nathalie Vialaneix & Hyphen | Introduction to neural networks 32/41
94. Strategies to avoid overfitting
Properly tune Q with a CV or a bootstrap estimation of the
generalization ability of the method
Early stopping: for Q large enough, use a part of the data as a
validation set and stops the training (gradient descent) when the
empirical error computed on this dataset starts to increase
Nathalie Vialaneix & Hyphen | Introduction to neural networks 32/41
95. Strategies to avoid overfitting
Properly tune Q with a CV or a bootstrap estimation of the
generalization ability of the method
Early stopping: for Q large enough, use a part of the data as a
validation set and stops the training (gradient descent) when the
empirical error computed on this dataset starts to increase
Weight decay: for Q large enough, penalize the empirical risk with a
function of the weights, e.g.,
ˆLn(w) + λw w
Nathalie Vialaneix & Hyphen | Introduction to neural networks 32/41
96. Strategies to avoid overfitting
Properly tune Q with a CV or a bootstrap estimation of the
generalization ability of the method
Early stopping: for Q large enough, use a part of the data as a
validation set and stops the training (gradient descent) when the
empirical error computed on this dataset starts to increase
Weight decay: for Q large enough, penalize the empirical risk with a
function of the weights, e.g.,
ˆLn(w) + λw w
Noise injection: modify the input data with a random noise during the
training
Nathalie Vialaneix & Hyphen | Introduction to neural networks 32/41
97. Outline
1 Statistical / machine / automatic learning
Background and notations
Underfitting / Overfitting
Consistency
Avoiding overfitting
2 (non deep) Neural networks
Presentation of multi-layer perceptrons
Theoretical properties of perceptrons
Learning perceptrons
Learning in practice
3 Deep neural networks
Overview
CNN
Nathalie Vialaneix & Hyphen | Introduction to neural networks 33/41
98. What is “deep learning”?
INPUTS
= (x(1)
, . . . , x(p)
) Layer 1 Layer 2 y
OUTPUTS
2 hidden layer MLP
Learning with neural networks except that...
the number of neurons is huge!!!
Image taken from https://towardsdatascience.com/
Nathalie Vialaneix & Hyphen | Introduction to neural networks 34/41
99. Main types of deep NN
deep MLP
Nathalie Vialaneix & Hyphen | Introduction to neural networks 35/41
100. Main types of deep NN
deep MLP
CNN (Convolutional Neural Network): used in image processing
Images taken from [Krizhevsky et al., 2012, Zeiler and Fergus, 2014]
Nathalie Vialaneix & Hyphen | Introduction to neural networks 35/41
101. Main types of deep NN
deep MLP
CNN (Convolutional Neural Network): used in image processing
RNN (Recurrent Neural Network): used for data with a temporal
sequence (in natural language processing for instance)
Nathalie Vialaneix & Hyphen | Introduction to neural networks 35/41
102. Why such a big buzz around deep learning?...
ImageNet results http://www.image-net.org/challenges/LSVRC/
Image by courtesy of Stéphane Canu
See also: http://karpathy.github.io/2014/09/02/what-i-learned-from-competing-against-a-convnet-on-imagenet/
Nathalie Vialaneix & Hyphen | Introduction to neural networks 36/41
103. Improvements in learning parameters
optimization algorithm: gradient descent is used but choice of the
descent step (µ(t)) has been improved [Ruder, 2016] and mini-batches
are also often used to speed the learning (ex: ADAM,
[Kingma and Ba, 2017])
Nathalie Vialaneix & Hyphen | Introduction to neural networks 37/41
104. Improvements in learning parameters
optimization algorithm: gradient descent is used but choice of the
descent step (µ(t)) has been improved [Ruder, 2016] and mini-batches
are also often used to speed the learning (ex: ADAM,
[Kingma and Ba, 2017])
avoid overfitting with dropouts: randomly removing a fraction p of the
neurons (and their connexions) during each step of the training step
(larger number of iterations to converge but faster update at each
iteration) [Srivastava et al., 2014]
INPUTS
x = (x(1)
, . . . , x(p)
) Layer 1 Layer 2 y
OUTPUTS
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105. Improvements in learning parameters
optimization algorithm: gradient descent is used but choice of the
descent step (µ(t)) has been improved [Ruder, 2016] and mini-batches
are also often used to speed the learning (ex: ADAM,
[Kingma and Ba, 2017])
avoid overfitting with dropouts: randomly removing a fraction p of the
neurons (and their connexions) during each step of the training step
(larger number of iterations to converge but faster update at each
iteration) [Srivastava et al., 2014]
INPUTS
x = (x(1)
, . . . , x(p)
) Layer 1 Layer 2 y
OUTPUTS
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106. Main available implementations
Theano
Tensor-
Flow
Caffe
PyTorch
(and
autograd)
Keras
Date 2008-2017 2015-... 2013-... 2016-... 2017-...
Main dev
Montreal
University
(Y. Bengio)
Google
Brain
Berkeley
University
community
driven
(Facebook,
Google, ...)
François
Chollet /
RStudio
Lan-
guage(s)
Python
C++/
Python
C++/
Python/
Matlab
Python
(with
strong
GPU
accelera-
tions)
high level
API on top
of Tensor-
Flow (and
CNTK,
Theano...):
Python, R
Nathalie Vialaneix & Hyphen | Introduction to neural networks 38/41
107. General architecture of CNN
Image taken from the article https://doi.org/10.3389/fpls.2017.02235
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108. Convolutional layer
Images taken from https://towardsdatascience.com
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109. Convolutional layer
Images taken from https://towardsdatascience.com
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110. Convolutional layer
Images taken from https://towardsdatascience.com
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111. Convolutional layer
Images taken from https://towardsdatascience.com
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112. Convolutional layer
Images taken from https://towardsdatascience.com
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113. Pooling layer
Basic principle: select a subsample of the previous layer values
Generally used: max pooling
Image taken from Wikimedia Commons, by Aphex34
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114. References
Barron, A. (1994).
Approximation and estimation bounds for artificial neural networks.
Machine Learning, 14:115–133.
Bishop, C. (1995).
Neural Networks for Pattern Recognition.
Oxford University Press, New York, USA.
Devroye, L., Györfi, L., and Lugosi, G. (1996).
A Probabilistic Theory for Pattern Recognition.
Springer-Verlag, New York, NY, USA.
Farago, A. and Lugosi, G. (1993).
Strong universal consistency of neural network classifiers.
IEEE Transactions on Information Theory, 39(4):1146–1151.
Györfi, L., Kohler, M., Krzy˙zak, A., and Walk, H. (2002).
A Distribution-Free Theory of Nonparametric Regression.
Springer-Verlag, New York, NY, USA.
Hornik, K. (1991).
Approximation capabilities of multilayer feedfoward networks.
Neural Networks, 4(2):251–257.
Hornik, K. (1993).
Some new results on neural network approximation.
Neural Networks, 6(8):1069–1072.
Kingma, D. P. and Ba, J. (2017).
Adam: a method for stochastic optimization.
arXiv preprint arXiv: 1412.6980.
Krizhevsky, A., Sutskever, I., and Hinton, Geoffrey, E. (2012).
Nathalie Vialaneix & Hyphen | Introduction to neural networks 41/41
115. ImageNet classification with deep convolutional neural networks.
In Pereira, F., Burges, C., Bottou, L., and Weinberger, K., editors, Proceedings of Neural Information Processing Systems (NIPS
2012), volume 25.
Mc Culloch, W. and Pitts, W. (1943).
A logical calculus of ideas immanent in nervous activity.
Bulletin of Mathematical Biophysics, 5(4):115–133.
McCaffrey, D. and Gallant, A. (1994).
Convergence rates for single hidden layer feedforward networks.
Neural Networks, 7(1):115–133.
Pinkus, A. (1999).
Approximation theory of the MLP model in neural networks.
Acta Numerica, 8:143–195.
Ripley, B. (1996).
Pattern Recognition and Neural Networks.
Cambridge University Press.
Rosenblatt, F. (1958).
The perceptron: a probabilistic model for information storage and organization in the brain.
Psychological Review, 65:386–408.
Rosenblatt, F. (1962).
Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms.
Spartan Books, Washington, DC, USA.
Ruder, S. (2016).
An overview of gradient descent optimization algorithms.
arXiv preprint arXiv: 1609.04747.
Rumelhart, D. and Mc Clelland, J. (1986).
Parallel Distributed Processing: Exploration in the MicroStructure of Cognition.
Nathalie Vialaneix & Hyphen | Introduction to neural networks 41/41
116. MIT Press, Cambridge, MA, USA.
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R. (2014).
Dropout: a simple way to prevent neural networks from overfitting.
Journal of Machine Learning Research, 15:1929–1958.
Stinchcombe, M. (1999).
Neural network approximation of continuous functionals and continuous functions on compactifications.
Neural Network, 12(3):467–477.
White, H. (1990).
Connectionist nonparametric regression: multilayer feedforward networks can learn arbitraty mappings.
Neural Networks, 3:535–549.
White, H. (1991).
Nonparametric estimation of conditional quantiles using neural networks.
In Proceedings of the 23rd Symposium of the Interface: Computing Science and Statistics, pages 190–199, Alexandria, VA,
USA. American Statistical Association.
Zeiler, M. D. and Fergus, R. (2014).
Visualizing and understanding convolutional networks.
In Proceedings of European Conference on Computer Vision (ECCV 2014).
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