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An explanation of fundamental concepts of features and models in machine learning, building on our geometric intuition of high dimensional spaces.

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A* Search Algorithm

Artificial Intelligence: Introduction, Typical Applications. State Space Search: Depth Bounded
DFS, Depth First Iterative Deepening. Heuristic Search: Heuristic Functions, Best First Search,
Hill Climbing, Variable Neighborhood Descent, Beam Search, Tabu Search. Optimal Search: A
*
algorithm, Iterative Deepening A*
, Recursive Best First Search, Pruning the CLOSED and OPEN
Lists

Deep Learning: Introduction & Chapter 5 Machine Learning Basics

Given lecture for Deep Learning 101 study group with Frank Wu on Dec. 9th, 2016.
Reference: https://www.deeplearningbook.org/
Initiated by Taiwan AI Group (https://www.facebook.com/groups/Taiwan.AI.Group/)

Heuristic search

Best-first search is a heuristic search algorithm that expands the most promising node first. It uses an evaluation function f(n) that estimates the cost to reach the goal from each node n. Nodes are ordered in the fringe by increasing f(n). A* search is a special case of best-first search that uses an admissible heuristic function h(n) and is guaranteed to find the optimal solution.

Heuristic Search Techniques {Artificial Intelligence}

FellowBuddy.com is an innovative platform that brings students together to share notes, exam papers, study guides, project reports and presentation for upcoming exams.
We connect Students who have an understanding of course material with Students who need help.
Benefits:-
# Students can catch up on notes they missed because of an absence.
# Underachievers can find peer developed notes that break down lecture and study material in a way that they can understand
# Students can earn better grades, save time and study effectively
Our Vision & Mission – Simplifying Students Life
Our Belief – “The great breakthrough in your life comes when you realize it, that you can learn anything you need to learn; to accomplish any goal that you have set for yourself. This means there are no limits on what you can be, have or do.”
Like Us - https://www.facebook.com/FellowBuddycom

Optimization for Deep Learning

Talk on Optimization for Deep Learning, which gives an overview of gradient descent optimization algorithms and highlights some current research directions.

Gradient descent method

This method gives the Artificial Neural Network, its much required tradeoff between cost function and processing powers

Alpaydin - Chapter 2

This document introduces machine learning and supervised learning. It discusses learning a classifier from labeled examples to predict a target variable. The key points covered are:
- Supervised learning involves learning a function that maps inputs to outputs from example input-output pairs.
- The goal is to learn a hypothesis h that has low error on the training set and generalizes well to new examples.
- The version space is the set of all hypotheses consistent with the training data.
- Controlling the complexity of the hypothesis class H via measures like VC dimension can improve generalization.
- For classification, multiple target classes are handled by learning one hypothesis per class. Regression learns a real-valued target function.
- There is a trade

What is the Expectation Maximization (EM) Algorithm?

Review of Do and Batzoglou. "What is the expectation maximization algorith?" Nat. Biotechnol. 2008;26:897. Also covers the Data Augmentation and Stan implementation. Resources at https://github.com/kaz-yos/em_da_repo

A* Search Algorithm

Artificial Intelligence: Introduction, Typical Applications. State Space Search: Depth Bounded
DFS, Depth First Iterative Deepening. Heuristic Search: Heuristic Functions, Best First Search,
Hill Climbing, Variable Neighborhood Descent, Beam Search, Tabu Search. Optimal Search: A
*
algorithm, Iterative Deepening A*
, Recursive Best First Search, Pruning the CLOSED and OPEN
Lists

Deep Learning: Introduction & Chapter 5 Machine Learning Basics

Given lecture for Deep Learning 101 study group with Frank Wu on Dec. 9th, 2016.
Reference: https://www.deeplearningbook.org/
Initiated by Taiwan AI Group (https://www.facebook.com/groups/Taiwan.AI.Group/)

Heuristic search

Best-first search is a heuristic search algorithm that expands the most promising node first. It uses an evaluation function f(n) that estimates the cost to reach the goal from each node n. Nodes are ordered in the fringe by increasing f(n). A* search is a special case of best-first search that uses an admissible heuristic function h(n) and is guaranteed to find the optimal solution.

Heuristic Search Techniques {Artificial Intelligence}

FellowBuddy.com is an innovative platform that brings students together to share notes, exam papers, study guides, project reports and presentation for upcoming exams.
We connect Students who have an understanding of course material with Students who need help.
Benefits:-
# Students can catch up on notes they missed because of an absence.
# Underachievers can find peer developed notes that break down lecture and study material in a way that they can understand
# Students can earn better grades, save time and study effectively
Our Vision & Mission – Simplifying Students Life
Our Belief – “The great breakthrough in your life comes when you realize it, that you can learn anything you need to learn; to accomplish any goal that you have set for yourself. This means there are no limits on what you can be, have or do.”
Like Us - https://www.facebook.com/FellowBuddycom

Optimization for Deep Learning

Talk on Optimization for Deep Learning, which gives an overview of gradient descent optimization algorithms and highlights some current research directions.

Gradient descent method

This method gives the Artificial Neural Network, its much required tradeoff between cost function and processing powers

Alpaydin - Chapter 2

This document introduces machine learning and supervised learning. It discusses learning a classifier from labeled examples to predict a target variable. The key points covered are:
- Supervised learning involves learning a function that maps inputs to outputs from example input-output pairs.
- The goal is to learn a hypothesis h that has low error on the training set and generalizes well to new examples.
- The version space is the set of all hypotheses consistent with the training data.
- Controlling the complexity of the hypothesis class H via measures like VC dimension can improve generalization.
- For classification, multiple target classes are handled by learning one hypothesis per class. Regression learns a real-valued target function.
- There is a trade

What is the Expectation Maximization (EM) Algorithm?

Review of Do and Batzoglou. "What is the expectation maximization algorith?" Nat. Biotechnol. 2008;26:897. Also covers the Data Augmentation and Stan implementation. Resources at https://github.com/kaz-yos/em_da_repo

03 Machine Learning Linear Algebra

The document provides an introduction to linear algebra concepts for machine learning. It defines vectors as ordered tuples of numbers that express magnitude and direction. Vector spaces are sets that contain all linear combinations of vectors. Linear independence and basis of vector spaces are discussed. Norms measure the magnitude of a vector, with examples given of the 1-norm and 2-norm. Inner products measure the correlation between vectors. Matrices can represent linear operators between vector spaces. Key linear algebra concepts such as trace, determinant, and matrix decompositions are outlined for machine learning applications.

HML: Historical View and Trends of Deep Learning

The document provides a historical view and trends of deep learning. It discusses that deep learning models have evolved in several waves since the 1940s, with key developments including the backpropagation algorithm in 1986 and deep belief networks with pretraining in 2006. Current trends include growing datasets, increasing numbers of neurons and connections per neuron, and higher accuracy on tasks involving vision, NLP and games. Research trends focus on generative models, domain alignment, meta-learning, using graphs as inputs, and program induction.

Optimization in deep learning

PDF version of slides explains the various optimization algorithms used in deep learning and a comparison between them. It also has a brief about the ICML papers "Descending through a Crowded Valley — Benchmarking Deep Learning Optimizers" and "Optimizer Benchmarking Needs to Account for Hyperparameter Tuning."
If you have any queries, you can reach out to me at @RakshithSathish on Twitter or rakshith-sathish on LinkedIn.

Stuart russell and peter norvig artificial intelligence - a modern approach...

This document provides publishing information for the book "Artificial Intelligence: A Modern Approach". It lists the editorial staff and production team, including the Vice President and Editorial Director, Editor-in-Chief, Executive Editor, and others. It also provides copyright information, acknowledging that the content is protected and requires permission for reproduction. Finally, it is dedicated to the authors' families and includes a preface giving an overview of the book.

Methods of Optimization in Machine Learning

In this session we will discuss about various methods to optimise a machine learning model and, how we can adjust the hyper-parameters to minimise the cost function.

Introduction to machine learning

Introduction to machine learning. Basics of machine learning. Overview of machine learning. Linear regression. logistic regression. cost function. Gradient descent. sensitivity, specificity. model selection.

Brief Introduction to Deep Learning + Solving XOR using ANNs

This presentation gives a very simple introduction to deep learning in addition to a step-by-step example showing how to solve the XOR non-linear problem using multi-layer artificial neural networks that has both input, hidden, and output layers.
Deep learning is based on artificial neural networks and it aims to analyze large amounts of data that are not easily analyzed using conventional models. It creates a large neural network with several hidden layers and several neurons within each layer and usually may take days for its learning.
Many beginners in artificial neural networks have a problem in understanding how hidden layers are useful and what is the best number of hidden layers and best number of neurons or nodes within each layer.
أحمد فوزي جاد Ahmed Fawzy Gad
قسم تكنولوجيا المعلومات Information Technology (IT) Department
كلية الحاسبات والمعلومات Faculty of Computers and Information (FCI)
جامعة المنوفية, مصر Menoufia University, Egypt
Teaching Assistant/Demonstrator
ahmed.fawzy@ci.menofia.edu.eg
:
AFCIT
http://www.afcit.xyz
YouTube
https://www.youtube.com/channel/UCuewOYbBXH5gwhfOrQOZOdw
Google Plus
https://plus.google.com/u/0/+AhmedGadIT
SlideShare
https://www.slideshare.net/AhmedGadFCIT
LinkedIn
https://www.linkedin.com/in/ahmedfgad/
ResearchGate
https://www.researchgate.net/profile/Ahmed_Gad13
Academia
https://menofia.academia.edu/Gad
Google Scholar
https://scholar.google.com.eg/citations?user=r07tjocAAAAJ&hl=en
Mendelay
https://www.mendeley.com/profiles/ahmed-gad12/
ORCID
https://orcid.org/0000-0003-1978-8574
StackOverFlow
http://stackoverflow.com/users/5426539/ahmed-gad
Twitter
https://twitter.com/ahmedfgad
Facebook
https://www.facebook.com/ahmed.f.gadd
Pinterest
https://www.pinterest.com/ahmedfgad/

Huffman Coding

The document provides an overview of Huffman coding, a lossless data compression algorithm. It begins with a simple example to illustrate the basic idea of assigning shorter codes to more frequent symbols. It then defines key terms like entropy and describes the Huffman coding algorithm, which constructs an optimal prefix code from the frequency of symbols in the data. The document discusses how Huffman coding can be applied to image compression by first predicting pixel values and then encoding the residuals. It notes some disadvantages of Huffman coding and describes variations like adaptive Huffman coding.

Classification and Regression

This document discusses machine learning concepts like supervised and unsupervised learning. It explains that supervised learning uses known inputs and outputs to learn rules while unsupervised learning deals with unknown inputs and outputs. Classification and regression are described as types of supervised learning problems. Classification involves categorizing data into classes while regression predicts continuous, real-valued outputs. Examples of classification and regression problems are provided. Classification models like heuristic, separation, regression and probabilistic models are also mentioned. The document encourages learning more about classification algorithms in upcoming videos.

2.mathematics for machine learning

This document provides an overview of key mathematical concepts relevant to machine learning, including linear algebra (vectors, matrices, tensors), linear models and hyperplanes, dot and outer products, probability and statistics (distributions, samples vs populations), and resampling methods. It also discusses solving systems of linear equations and the statistical analysis of training data distributions.

MATLAB Code + Description : Very Simple Automatic English Optical Character R...

This file contains a simple description about what I have created about how to recognize characters using feed forward back propagation neural network as a pattern recognition project when being undergraduate student at 2013.
The MATLAB code of the system is also available in the document.
Find me on:
AFCIT
http://www.afcit.xyz
YouTube
https://www.youtube.com/channel/UCuewOYbBXH5gwhfOrQOZOdw
Google Plus
https://plus.google.com/u/0/+AhmedGadIT
SlideShare
https://www.slideshare.net/AhmedGadFCIT
LinkedIn
https://www.linkedin.com/in/ahmedfgad/
ResearchGate
https://www.researchgate.net/profile/Ahmed_Gad13
Academia
https://www.academia.edu/
Google Scholar
https://scholar.google.com.eg/citations?user=r07tjocAAAAJ&hl=en
Mendelay
https://www.mendeley.com/profiles/ahmed-gad12/
ORCID
https://orcid.org/0000-0003-1978-8574
StackOverFlow
http://stackoverflow.com/users/5426539/ahmed-gad
Twitter
https://twitter.com/ahmedfgad
Facebook
https://www.facebook.com/ahmed.f.gadd
Pinterest
https://www.pinterest.com/ahmedfgad/

Neural Networks: Multilayer Perceptron

This document provides an overview of multilayer perceptrons (MLPs) and the backpropagation algorithm. It defines MLPs as neural networks with multiple hidden layers that can solve nonlinear problems. The backpropagation algorithm is introduced as a method for training MLPs by propagating error signals backward from the output to inner layers. Key steps include calculating the error at each neuron, determining the gradient to update weights, and using this to minimize overall network error through iterative weight adjustment.

Explainable AI

Slide for Arithmer Seminar given by Dr. Daisuke Sato (Arithmer) at Arithmer inc.
The topic is on "explainable AI".
"Arithmer Seminar" is weekly held, where professionals from within and outside our company give lectures on their respective expertise.
The slides are made by the lecturer from outside our company, and shared here with his/her permission.
Arithmer株式会社は東京大学大学院数理科学研究科発の数学の会社です。私達は現代数学を応用して、様々な分野のソリューションに、新しい高度AIシステムを導入しています。AIをいかに上手に使って仕事を効率化するか、そして人々の役に立つ結果を生み出すのか、それを考えるのが私たちの仕事です。
Arithmer began at the University of Tokyo Graduate School of Mathematical Sciences. Today, our research of modern mathematics and AI systems has the capability of providing solutions when dealing with tough complex issues. At Arithmer we believe it is our job to realize the functions of AI through improving work efficiency and producing more useful results for society.

An overview of gradient descent optimization algorithms

This document provides an overview of various gradient descent optimization algorithms that are commonly used for training deep learning models. It begins with an introduction to gradient descent and its variants, including batch gradient descent, stochastic gradient descent (SGD), and mini-batch gradient descent. It then discusses challenges with these algorithms, such as choosing the learning rate. The document proceeds to explain popular optimization algorithms used to address these challenges, including momentum, Nesterov accelerated gradient, Adagrad, Adadelta, RMSprop, and Adam. It provides visualizations and intuitive explanations of how these algorithms work. Finally, it discusses strategies for parallelizing and optimizing SGD and concludes with a comparison of optimization algorithms.

Shap

Machine Learning and computing power have made huge improvements in the last decade. It’s now possible to unlock complex problems in multidimensional space with ensemble, brute force algorithms or deep neural networks, with performances that were unthinkable a few years ago. However the use of black box models is still frown upon in a business setting. In fact the decision functions of those models are often impossible to interpret for humans, can be biased or just based on absurd assumption. What if your risk model denies loans to people on ethnic ground? SHAP comes as an innovative framework to obtain local explanations for the output of a model, making the black box much more transparent.

Explainable AI - making ML and DL models more interpretable

The document discusses explainable AI (XAI) and making machine learning and deep learning models more interpretable. It covers the necessity and principles of XAI, popular model-agnostic XAI methods for ML and DL models, frameworks like LIME, SHAP, ELI5 and SKATER, and research questions around evolving XAI to be understandable by non-experts. The key topics covered are model-agnostic XAI, surrogate models, influence methods, visualizations and evaluating descriptive accuracy of explanations.

Computer Vision - Image Filters

1. The document discusses various image filtering techniques, including correlation filtering, convolution, averaging filters, and Gaussian filters.
2. Gaussian filters are commonly used for smoothing images as they remove high-frequency components while maintaining edges. The scale parameter σ controls the amount of smoothing.
3. Median filters can reduce noise in images by selecting the median value in a local neighborhood, unlike mean filters which are susceptible to outliers.

Machine Learning Project

This document summarizes a machine learning project for Homesite to predict customer quote conversions. The team members are Jack, Harry, and Abhishek. Homesite wants to predict the likelihood of customers purchasing insurance contracts based on their quote. The training data has 261k rows and 298 predictors, while the test data has 200k rows and the same 298 columns. Some key steps included data cleaning, using gradient boosting and random forests, and calculating the AUC (area under the ROC curve) metric to evaluate model performance. The team's model achieved an AUC of 0.95, indicating it does not overfit and has little bias.

Variational Autoencoder

Youtube:
https://www.youtube.com/playlist?list=PLeeHDpwX2Kj55He_jfPojKrZf22HVjAZY
Paper review of "Auto-Encoding Variational Bayes"

Deep Learning Explained

This document summarizes Melanie Swan's presentation on deep learning. It began with defining key deep learning concepts and techniques, including neural networks, supervised vs. unsupervised learning, and convolutional neural networks. It then explained how deep learning works by using multiple processing layers to extract higher-level features from data and make predictions. Deep learning has various applications like image recognition and speech recognition. The presentation concluded by discussing how deep learning is inspired by concepts from physics and statistical mechanics.

The How and Why of Feature Engineering

Feature engineering--the underdog of machine learning. This deck provides an overview of feature generation methods for text, image, audio, feature cleaning and transformation methods, how well they work and why.

Feature Engineering - Getting most out of data for predictive models

How should data be preprocessed for use in machine learning algorithms? How to identify the most predictive attributes of a dataset? What features can generate to improve the accuracy of a model?
Feature Engineering is the process of extracting and selecting, from raw data, features that can be used effectively in predictive models. As the quality of the features greatly influences the quality of the results, knowing the main techniques and pitfalls will help you to succeed in the use of machine learning in your projects.
In this talk, we will present methods and techniques that allow us to extract the maximum potential of the features of a dataset, increasing flexibility, simplicity and accuracy of the models. The analysis of the distribution of features and their correlations, the transformation of numeric attributes (such as scaling, normalization, log-based transformation, binning), categorical attributes (such as one-hot encoding, feature hashing, Temporal (date / time), and free-text attributes (text vectorization, topic modeling).
Python, Python, Scikit-learn, and Spark SQL examples will be presented and how to use domain knowledge and intuition to select and generate features relevant to predictive models.

03 Machine Learning Linear Algebra

The document provides an introduction to linear algebra concepts for machine learning. It defines vectors as ordered tuples of numbers that express magnitude and direction. Vector spaces are sets that contain all linear combinations of vectors. Linear independence and basis of vector spaces are discussed. Norms measure the magnitude of a vector, with examples given of the 1-norm and 2-norm. Inner products measure the correlation between vectors. Matrices can represent linear operators between vector spaces. Key linear algebra concepts such as trace, determinant, and matrix decompositions are outlined for machine learning applications.

HML: Historical View and Trends of Deep Learning

The document provides a historical view and trends of deep learning. It discusses that deep learning models have evolved in several waves since the 1940s, with key developments including the backpropagation algorithm in 1986 and deep belief networks with pretraining in 2006. Current trends include growing datasets, increasing numbers of neurons and connections per neuron, and higher accuracy on tasks involving vision, NLP and games. Research trends focus on generative models, domain alignment, meta-learning, using graphs as inputs, and program induction.

Optimization in deep learning

PDF version of slides explains the various optimization algorithms used in deep learning and a comparison between them. It also has a brief about the ICML papers "Descending through a Crowded Valley — Benchmarking Deep Learning Optimizers" and "Optimizer Benchmarking Needs to Account for Hyperparameter Tuning."
If you have any queries, you can reach out to me at @RakshithSathish on Twitter or rakshith-sathish on LinkedIn.

Stuart russell and peter norvig artificial intelligence - a modern approach...

This document provides publishing information for the book "Artificial Intelligence: A Modern Approach". It lists the editorial staff and production team, including the Vice President and Editorial Director, Editor-in-Chief, Executive Editor, and others. It also provides copyright information, acknowledging that the content is protected and requires permission for reproduction. Finally, it is dedicated to the authors' families and includes a preface giving an overview of the book.

Methods of Optimization in Machine Learning

In this session we will discuss about various methods to optimise a machine learning model and, how we can adjust the hyper-parameters to minimise the cost function.

Introduction to machine learning

Introduction to machine learning. Basics of machine learning. Overview of machine learning. Linear regression. logistic regression. cost function. Gradient descent. sensitivity, specificity. model selection.

Brief Introduction to Deep Learning + Solving XOR using ANNs

This presentation gives a very simple introduction to deep learning in addition to a step-by-step example showing how to solve the XOR non-linear problem using multi-layer artificial neural networks that has both input, hidden, and output layers.
Deep learning is based on artificial neural networks and it aims to analyze large amounts of data that are not easily analyzed using conventional models. It creates a large neural network with several hidden layers and several neurons within each layer and usually may take days for its learning.
Many beginners in artificial neural networks have a problem in understanding how hidden layers are useful and what is the best number of hidden layers and best number of neurons or nodes within each layer.
أحمد فوزي جاد Ahmed Fawzy Gad
قسم تكنولوجيا المعلومات Information Technology (IT) Department
كلية الحاسبات والمعلومات Faculty of Computers and Information (FCI)
جامعة المنوفية, مصر Menoufia University, Egypt
Teaching Assistant/Demonstrator
ahmed.fawzy@ci.menofia.edu.eg
:
AFCIT
http://www.afcit.xyz
YouTube
https://www.youtube.com/channel/UCuewOYbBXH5gwhfOrQOZOdw
Google Plus
https://plus.google.com/u/0/+AhmedGadIT
SlideShare
https://www.slideshare.net/AhmedGadFCIT
LinkedIn
https://www.linkedin.com/in/ahmedfgad/
ResearchGate
https://www.researchgate.net/profile/Ahmed_Gad13
Academia
https://menofia.academia.edu/Gad
Google Scholar
https://scholar.google.com.eg/citations?user=r07tjocAAAAJ&hl=en
Mendelay
https://www.mendeley.com/profiles/ahmed-gad12/
ORCID
https://orcid.org/0000-0003-1978-8574
StackOverFlow
http://stackoverflow.com/users/5426539/ahmed-gad
Twitter
https://twitter.com/ahmedfgad
Facebook
https://www.facebook.com/ahmed.f.gadd
Pinterest
https://www.pinterest.com/ahmedfgad/

Huffman Coding

The document provides an overview of Huffman coding, a lossless data compression algorithm. It begins with a simple example to illustrate the basic idea of assigning shorter codes to more frequent symbols. It then defines key terms like entropy and describes the Huffman coding algorithm, which constructs an optimal prefix code from the frequency of symbols in the data. The document discusses how Huffman coding can be applied to image compression by first predicting pixel values and then encoding the residuals. It notes some disadvantages of Huffman coding and describes variations like adaptive Huffman coding.

Classification and Regression

This document discusses machine learning concepts like supervised and unsupervised learning. It explains that supervised learning uses known inputs and outputs to learn rules while unsupervised learning deals with unknown inputs and outputs. Classification and regression are described as types of supervised learning problems. Classification involves categorizing data into classes while regression predicts continuous, real-valued outputs. Examples of classification and regression problems are provided. Classification models like heuristic, separation, regression and probabilistic models are also mentioned. The document encourages learning more about classification algorithms in upcoming videos.

2.mathematics for machine learning

This document provides an overview of key mathematical concepts relevant to machine learning, including linear algebra (vectors, matrices, tensors), linear models and hyperplanes, dot and outer products, probability and statistics (distributions, samples vs populations), and resampling methods. It also discusses solving systems of linear equations and the statistical analysis of training data distributions.

MATLAB Code + Description : Very Simple Automatic English Optical Character R...

This file contains a simple description about what I have created about how to recognize characters using feed forward back propagation neural network as a pattern recognition project when being undergraduate student at 2013.
The MATLAB code of the system is also available in the document.
Find me on:
AFCIT
http://www.afcit.xyz
YouTube
https://www.youtube.com/channel/UCuewOYbBXH5gwhfOrQOZOdw
Google Plus
https://plus.google.com/u/0/+AhmedGadIT
SlideShare
https://www.slideshare.net/AhmedGadFCIT
LinkedIn
https://www.linkedin.com/in/ahmedfgad/
ResearchGate
https://www.researchgate.net/profile/Ahmed_Gad13
Academia
https://www.academia.edu/
Google Scholar
https://scholar.google.com.eg/citations?user=r07tjocAAAAJ&hl=en
Mendelay
https://www.mendeley.com/profiles/ahmed-gad12/
ORCID
https://orcid.org/0000-0003-1978-8574
StackOverFlow
http://stackoverflow.com/users/5426539/ahmed-gad
Twitter
https://twitter.com/ahmedfgad
Facebook
https://www.facebook.com/ahmed.f.gadd
Pinterest
https://www.pinterest.com/ahmedfgad/

Neural Networks: Multilayer Perceptron

This document provides an overview of multilayer perceptrons (MLPs) and the backpropagation algorithm. It defines MLPs as neural networks with multiple hidden layers that can solve nonlinear problems. The backpropagation algorithm is introduced as a method for training MLPs by propagating error signals backward from the output to inner layers. Key steps include calculating the error at each neuron, determining the gradient to update weights, and using this to minimize overall network error through iterative weight adjustment.

Explainable AI

Slide for Arithmer Seminar given by Dr. Daisuke Sato (Arithmer) at Arithmer inc.
The topic is on "explainable AI".
"Arithmer Seminar" is weekly held, where professionals from within and outside our company give lectures on their respective expertise.
The slides are made by the lecturer from outside our company, and shared here with his/her permission.
Arithmer株式会社は東京大学大学院数理科学研究科発の数学の会社です。私達は現代数学を応用して、様々な分野のソリューションに、新しい高度AIシステムを導入しています。AIをいかに上手に使って仕事を効率化するか、そして人々の役に立つ結果を生み出すのか、それを考えるのが私たちの仕事です。
Arithmer began at the University of Tokyo Graduate School of Mathematical Sciences. Today, our research of modern mathematics and AI systems has the capability of providing solutions when dealing with tough complex issues. At Arithmer we believe it is our job to realize the functions of AI through improving work efficiency and producing more useful results for society.

An overview of gradient descent optimization algorithms

This document provides an overview of various gradient descent optimization algorithms that are commonly used for training deep learning models. It begins with an introduction to gradient descent and its variants, including batch gradient descent, stochastic gradient descent (SGD), and mini-batch gradient descent. It then discusses challenges with these algorithms, such as choosing the learning rate. The document proceeds to explain popular optimization algorithms used to address these challenges, including momentum, Nesterov accelerated gradient, Adagrad, Adadelta, RMSprop, and Adam. It provides visualizations and intuitive explanations of how these algorithms work. Finally, it discusses strategies for parallelizing and optimizing SGD and concludes with a comparison of optimization algorithms.

Shap

Machine Learning and computing power have made huge improvements in the last decade. It’s now possible to unlock complex problems in multidimensional space with ensemble, brute force algorithms or deep neural networks, with performances that were unthinkable a few years ago. However the use of black box models is still frown upon in a business setting. In fact the decision functions of those models are often impossible to interpret for humans, can be biased or just based on absurd assumption. What if your risk model denies loans to people on ethnic ground? SHAP comes as an innovative framework to obtain local explanations for the output of a model, making the black box much more transparent.

Explainable AI - making ML and DL models more interpretable

The document discusses explainable AI (XAI) and making machine learning and deep learning models more interpretable. It covers the necessity and principles of XAI, popular model-agnostic XAI methods for ML and DL models, frameworks like LIME, SHAP, ELI5 and SKATER, and research questions around evolving XAI to be understandable by non-experts. The key topics covered are model-agnostic XAI, surrogate models, influence methods, visualizations and evaluating descriptive accuracy of explanations.

Computer Vision - Image Filters

1. The document discusses various image filtering techniques, including correlation filtering, convolution, averaging filters, and Gaussian filters.
2. Gaussian filters are commonly used for smoothing images as they remove high-frequency components while maintaining edges. The scale parameter σ controls the amount of smoothing.
3. Median filters can reduce noise in images by selecting the median value in a local neighborhood, unlike mean filters which are susceptible to outliers.

Machine Learning Project

This document summarizes a machine learning project for Homesite to predict customer quote conversions. The team members are Jack, Harry, and Abhishek. Homesite wants to predict the likelihood of customers purchasing insurance contracts based on their quote. The training data has 261k rows and 298 predictors, while the test data has 200k rows and the same 298 columns. Some key steps included data cleaning, using gradient boosting and random forests, and calculating the AUC (area under the ROC curve) metric to evaluate model performance. The team's model achieved an AUC of 0.95, indicating it does not overfit and has little bias.

Variational Autoencoder

Youtube:
https://www.youtube.com/playlist?list=PLeeHDpwX2Kj55He_jfPojKrZf22HVjAZY
Paper review of "Auto-Encoding Variational Bayes"

Deep Learning Explained

This document summarizes Melanie Swan's presentation on deep learning. It began with defining key deep learning concepts and techniques, including neural networks, supervised vs. unsupervised learning, and convolutional neural networks. It then explained how deep learning works by using multiple processing layers to extract higher-level features from data and make predictions. Deep learning has various applications like image recognition and speech recognition. The presentation concluded by discussing how deep learning is inspired by concepts from physics and statistical mechanics.

03 Machine Learning Linear Algebra

03 Machine Learning Linear Algebra

HML: Historical View and Trends of Deep Learning

HML: Historical View and Trends of Deep Learning

Optimization in deep learning

Optimization in deep learning

Stuart russell and peter norvig artificial intelligence - a modern approach...

Stuart russell and peter norvig artificial intelligence - a modern approach...

Methods of Optimization in Machine Learning

Methods of Optimization in Machine Learning

Introduction to machine learning

Introduction to machine learning

Brief Introduction to Deep Learning + Solving XOR using ANNs

Brief Introduction to Deep Learning + Solving XOR using ANNs

Huffman Coding

Huffman Coding

Classification and Regression

Classification and Regression

2.mathematics for machine learning

2.mathematics for machine learning

MATLAB Code + Description : Very Simple Automatic English Optical Character R...

MATLAB Code + Description : Very Simple Automatic English Optical Character R...

Neural Networks: Multilayer Perceptron

Neural Networks: Multilayer Perceptron

Explainable AI

Explainable AI

An overview of gradient descent optimization algorithms

An overview of gradient descent optimization algorithms

Shap

Shap

Explainable AI - making ML and DL models more interpretable

Explainable AI - making ML and DL models more interpretable

Computer Vision - Image Filters

Computer Vision - Image Filters

Machine Learning Project

Machine Learning Project

Variational Autoencoder

Variational Autoencoder

Deep Learning Explained

Deep Learning Explained

The How and Why of Feature Engineering

Feature engineering--the underdog of machine learning. This deck provides an overview of feature generation methods for text, image, audio, feature cleaning and transformation methods, how well they work and why.

Feature Engineering - Getting most out of data for predictive models

How should data be preprocessed for use in machine learning algorithms? How to identify the most predictive attributes of a dataset? What features can generate to improve the accuracy of a model?
Feature Engineering is the process of extracting and selecting, from raw data, features that can be used effectively in predictive models. As the quality of the features greatly influences the quality of the results, knowing the main techniques and pitfalls will help you to succeed in the use of machine learning in your projects.
In this talk, we will present methods and techniques that allow us to extract the maximum potential of the features of a dataset, increasing flexibility, simplicity and accuracy of the models. The analysis of the distribution of features and their correlations, the transformation of numeric attributes (such as scaling, normalization, log-based transformation, binning), categorical attributes (such as one-hot encoding, feature hashing, Temporal (date / time), and free-text attributes (text vectorization, topic modeling).
Python, Python, Scikit-learn, and Spark SQL examples will be presented and how to use domain knowledge and intuition to select and generate features relevant to predictive models.

Horovod - Distributed TensorFlow Made Easy

Uber Engineering introduces Horovod, an open source framework that makes it faster and easier to train deep learning models with TensorFlow.

Transfer Learning and Fine Tuning for Cross Domain Image Classification with ...

Supporting code for my talk at Demystifying Deep Learning and AI event on November 19-20 2016 at Oakland CA.

Lessons from 2MM machine learning models

Kaggle is a community of almost 400K data scientists who have built almost 2MM machine learning models to participate in our competitions. Data scientists come to Kaggle to learn, collaborate and develop the state of the art in machine learning. This talk will cover some of the lessons we have learned from the Kaggle community.

Large-Scale Training with GPUs at Facebook

This document discusses large-scale distributed training with GPUs at Facebook using their Caffe2 framework. It describes how Facebook was able to train the ResNet-50 model on the ImageNet dataset in just 1 hour using 32 GPUs with 8 GPUs each. It explains how synchronous SGD was implemented in Caffe2 using Gloo for efficient all-reduce operations. Linear scaling of the learning rate with increased batch size was found to work best when gradually warming up the learning rate over the first few epochs. Nearly linear speedup was achieved using this approach on commodity hardware.

Parameter Server Approach for Online Learning at Twitter

Parameter Server approaches for online learning at Twitter allow models to be updated continuously based on new data and improve predictions in real-time. Version 1.0 decouples training and prediction to increase efficiency. Version 2.0 scales training by distributing it across servers. Version 3.0 will scale large complex models by sharding models and features across multiple servers. These approaches enable Twitter to perform online learning on massive datasets and complex models in real-time.

2017 10-10 (netflix ml platform meetup) learning item and user representation...

1) Learning user and item representations is challenging due to sparse data and shifting preferences in recommender systems.
2) The presentation outlines research at Google to address sparsity through two approaches: focused learning, which develops specialized models for subsets of data like genres or cold-start items, and factorized deep retrieval, which jointly embeds items and their features to predict preferences for fresh items.
3) The techniques have improved overall viewership and nomination of candidates, demonstrating their effectiveness in production recommender systems.

The How and Why of Feature Engineering

The How and Why of Feature Engineering

Feature Engineering - Getting most out of data for predictive models

Feature Engineering - Getting most out of data for predictive models

Horovod - Distributed TensorFlow Made Easy

Horovod - Distributed TensorFlow Made Easy

Transfer Learning and Fine Tuning for Cross Domain Image Classification with ...

Transfer Learning and Fine Tuning for Cross Domain Image Classification with ...

Lessons from 2MM machine learning models

Lessons from 2MM machine learning models

Large-Scale Training with GPUs at Facebook

Large-Scale Training with GPUs at Facebook

Parameter Server Approach for Online Learning at Twitter

Parameter Server Approach for Online Learning at Twitter

2017 10-10 (netflix ml platform meetup) learning item and user representation...

2017 10-10 (netflix ml platform meetup) learning item and user representation...

Understanding Feature Space in Machine Learning - Data Science Pop-up Seattle

Machine learning derives mathematical models from raw data. In the model building process, raw data is first processed into "features," then the features are given to algorithms to train a model. The process of turning raw data into features is sometimes called feature engineering, and it is a crucial step in model building. Good features lead to successful models with a lot of predictive power; bad features lead to a lot of headache and nowhere.
This talk aims to help the audience understand what is a feature space and why it is so important. We will go through some common feature space representations of English text and discuss what tasks they are suited for and why. Expect lots of pictures, whiteboard drawings and handwaving. We will exercise our power of imagination to visualize high dimensional feature spaces in our mind's eye. Presented by Alice Zheng Director of Data Science at Dato.

Maths in the PYP - A Journey through the Arts

This document outlines an agenda for a mathematical journey through the arts workshop. It includes an icebreaker activity, sharing beliefs about mathematics, exploring the connections between math and art, action planning, and reflection. During the workshop, participants will read stories with mathematical concepts and use manipulatives like ladybugs and caterpillars to develop their understanding of addition and subtraction. The document emphasizes building conceptual understanding through concrete and pictorial representations before introducing symbolic notation.

Introduction to LLMs, Prompt Engineering fundamentals,

- Prompt Engineering fundamentals
- Google Foundations Models
- Advanced Prompting Techniques
- ReAct prompting
- Prompting best practices
- Open source LLM
- Google Gemma
- EU Artificial Intelligence Act

[D2 COMMUNITY] Spark User Group - 머신러닝 인공지능 기법

1) The document discusses various approaches and techniques in artificial intelligence including symbolic logic, planning, expert systems, fuzzy logic, genetic algorithms, Bayesian networks, and more.
2) It provides examples of each technique including using logic to represent arguments, planning routes for a traveling salesman, building financial expert systems, applying fuzzy logic to tipping recommendations, and using Bayesian networks for medical diagnosis.
3) The key challenges of AI discussed are computational complexity, problems with first-order logic like undecidability and uncertainty, and the difficulty of non-symbolic approaches like uncertainty in real-world problems.

CO Quadratic Inequalties.pptx

This document provides information and instructions about quadratic inequalities. It begins with objectives to identify and describe quadratic inequalities using practical situations and mathematical expressions. It then defines quadratic inequalities as inequalities containing polynomials of degree 2. The standard form of quadratic inequalities is presented. Examples of quadratic inequalities in standard and non-standard form are given and worked through. Steps for solving quadratic inequalities are demonstrated. Activities include matching terms to definitions, describing examples, and completing a table with quadratic expressions and symbols. The document aims to build understanding of quadratic inequalities.

Latent dirichlet allocation_and_topic_modeling

1. LDA represents documents as mixtures of topics and topics as mixtures of words.
2. It assumes documents are generated by first choosing a topic distribution, then choosing words from that topic.
3. The algorithm estimates topic distributions for each document and word distributions for each topic that are most likely to have generated the observed document-word matrix.

Ml3

This document provides an overview of machine learning and feature engineering. It discusses how machine learning can be used for tasks like classification, regression, similarity matching, and clustering. It explains that feature engineering involves transforming raw data into numeric representations called features that machine learning models can use. Different techniques for feature engineering text and images are presented, such as bag-of-words and convolutional neural networks. Dimensionality reduction through principal component analysis is demonstrated. Finally, information is given about upcoming machine learning tutorials and Dato's machine learning platform.

Overview of Machine Learning and Feature Engineering

Machine Learning 101 Tutorial at Strata NYC, Sep 2015
Overview of machine learning models and features. Visualization of feature space and feature engineering methods.

Infrastructures et recommandations pour les Humanités Numériques - Big Data e...

Infrastructures et recommandations pour les Humanités Numériques - Big Data e...Patrice Bellot - Aix-Marseille Université / CNRS (LIS, INS2I)

Le développement du Web et des réseaux sociaux ou les numérisations massives de documents contribuent à un renouvellement des Sciences Humaines et Sociales, des études des patrimoines littéraires ou culturels, ou encore de la façon dont est exploitée la littérature scientifique en général.
Les humanités numériques, qui croisent diverses disciplines avec l’informatique, posent comme centrales les questions du volume des données, de leur diversité, de leur origine, de leur véracité ou de leur représentativité. Les informations sont véhiculées au sein de « documents » textuels (livres, pages Web, tweets...), audio, vidéo ou multimédia. Ils peuvent comporter des illustrations ou des graphiques.
Appréhender de telles ressources nécessite le développement d'approches informatiques robustes, capables de passer à l’échelle et adaptées à la nature fondamentalement ambiguë et variée des informations manipulées (langage naturel ou images à interpréter, points de vue multiples…).
Si les approches d’apprentissage statistique sont monnaie courante pour des tâches de classification ou d’extraction d’information, elles doivent faire face à des espaces vectoriels creux et de dimension très élevées (plusieurs millions), être en mesure d’exploiter des ressources (par exemple des lexiques ou des thesaurus) et tenir compte ou produire des annotations sémantiques qui devront pouvoir être réutilisées.
Pour faire face à ces enjeux, des infrastructures ont été créées telle HumaNum à l’échelle nationale, DARIAH ou CLARIN à l’échelle européenne et des recommandations établies à l’échelle mondiale telle que la TEI (Text Encoding Initiative). Des plateformes au service de l’information scientifique comme l’équipement d’excellence OpenEdition.org sont une autre brique essentielle pour la préservation et l’accès aux « Big Digital Humanities » mais aussi pour favoriser la reproductibilité et la compréhension des expérimentations et des résultats obtenus.Introduction to Search Systems - ScaleConf Colombia 2017

Often when a new user arrives on your website, the first place they go to find information is the search box! Whether they are searching for hotels on your travel site, products on your e-commerce site, or friends to connect with on your social media site, it is important to have fast, effective search in order to engage the user.

CSCE181 Big ideas in NLP

Introductory seminar on NLP for CS sophomores. Presented to Texas A&M's Fall 2022 CSCE181 class. Slides are a bit redundant due to compatibility issues :\

Peter Norvig - NYC Machine Learning 2013

The document discusses learning programming through MOOCs and machine learning. It provides data on a MOOC with over 160,000 students from 209 countries. It analyzes student error messages, submissions, and interactions to improve programming instructions. However, programming languages can be ambiguous and students struggle with different concepts. The document advocates for mastery learning through one-on-one tutoring and continual course improvements using data and machine learning.

syntherella feedback synthesizer

This presentation describes a mechanism for synthesizing meaningful concise descriptions for exploring virtual worlds using a screenreader.

Deep Learning Class #0 - You Can Do It

"You Can Do It" by Louis Monier (Altavista Co-Founder & CTO) & Gregory Renard (CTO & Artificial Intelligence Lead Architect at Xbrain) for Deep Learning keynote #0 at Holberton School (http://www.meetup.com/Holberton-School/events/228364522/)
If you want to assist to similar keynote for free, checkout http://www.meetup.com/Holberton-School/

DL Classe 0 - You can do it

Here are some key terms that are similar to "champagne":
- Sparkling wines
- French champagne
- Cognac
- Rosé
- White wine
- Sparkling wine
- Wine
- Burgundy
- Bordeaux
- Cava
- Prosecco
Some specific champagne brands that are similar terms include Moët, Veuve Clicquot, Dom Pérignon, Taittinger, and Bollinger. Grape varieties used in champagne production like Chardonnay and Pinot Noir could also be considered similar terms.

Word2vec ultimate beginner

word2vec beginner.
vector space, distributional semantics, word embedding, vector representation for word, word vector representation, sparse and dense representation, vector representation, Google word2vec, tensorflow

Edutalk f2013

1. The document discusses educational theory and concepts relevant to learning at hacker schools.
2. It promotes three main ideas: that learning is designable like coding, individual brains learn differently, and learning is not an isolated process but relies on community and collaboration.
3. Various learning theories are covered briefly, including cognitive apprenticeship and legitimate peripheral participation within a community of practice. Motivation, mindset, and overcoming challenges are also addressed.

Collegeteaching102

This document provides an overview of strategies for effective college teaching, including facilitating discussions, delivering lectures, assessing student comprehension through testing, and incorporating educational technologies. A variety of specific techniques are presented for each teaching method, with examples and suggestions for implementation. The goal is to help educators engage students and promote learning.

Using binary classifiers

The document provides an overview of machine learning and discusses various concepts related to applying machine learning to real-world problems. It covers topics such as feature extraction, encoding input data, classification vs regression, evaluating model performance, and challenges like overfitting and underfitting models to data. Examples are given for different types of learning problems, including text classification, sentiment analysis, and predicting stock prices.

Translation to QL Part 1

This document introduces the basics of translating statements from natural language to the formal language of Quantified Logic (QL). It explains that QL uses constants to represent singular terms, predicates represented by capital letters, and variables represented by lowercase letters. Quantifiers like "for all" and "there exists" are used to represent statements about properties of individuals or groups. To translate a statement to QL, one must identify whether quantifiers are used, what the universe of discourse is, any singular terms, and the relevant predicates to determine the proper representation using constants, predicates, variables, quantifiers and logical connectives.

Understanding Feature Space in Machine Learning - Data Science Pop-up Seattle

Understanding Feature Space in Machine Learning - Data Science Pop-up Seattle

Maths in the PYP - A Journey through the Arts

Maths in the PYP - A Journey through the Arts

Introduction to LLMs, Prompt Engineering fundamentals,

Introduction to LLMs, Prompt Engineering fundamentals,

[D2 COMMUNITY] Spark User Group - 머신러닝 인공지능 기법

[D2 COMMUNITY] Spark User Group - 머신러닝 인공지능 기법

CO Quadratic Inequalties.pptx

CO Quadratic Inequalties.pptx

Latent dirichlet allocation_and_topic_modeling

Latent dirichlet allocation_and_topic_modeling

Ml3

Ml3

Overview of Machine Learning and Feature Engineering

Overview of Machine Learning and Feature Engineering

Infrastructures et recommandations pour les Humanités Numériques - Big Data e...

Infrastructures et recommandations pour les Humanités Numériques - Big Data e...

Introduction to Search Systems - ScaleConf Colombia 2017

Introduction to Search Systems - ScaleConf Colombia 2017

CSCE181 Big ideas in NLP

CSCE181 Big ideas in NLP

Peter Norvig - NYC Machine Learning 2013

Peter Norvig - NYC Machine Learning 2013

syntherella feedback synthesizer

syntherella feedback synthesizer

Deep Learning Class #0 - You Can Do It

Deep Learning Class #0 - You Can Do It

DL Classe 0 - You can do it

DL Classe 0 - You can do it

Word2vec ultimate beginner

Word2vec ultimate beginner

Edutalk f2013

Edutalk f2013

Collegeteaching102

Collegeteaching102

Using binary classifiers

Using binary classifiers

Translation to QL Part 1

Translation to QL Part 1

Introduction_Ch_01_Biotech Biotechnology course .pptx

ntroduction_Ch_01_Biotech

fermented food science of sauerkraut.pptx

This ppt contains the production of a fermented food name - sauerkraut

Travis Hills of MN is Making Clean Water Accessible to All Through High Flux ...

By harnessing the power of High Flux Vacuum Membrane Distillation, Travis Hills from MN envisions a future where clean and safe drinking water is accessible to all, regardless of geographical location or economic status.

Methods of grain storage Structures in India.pdf

•Post-harvestlossesaccountforabout10%oftotalfoodgrainsduetounscientificstorage,insects,rodents,micro-organismsetc.,
•Totalfoodgrainproductioninindiais311milliontonnesandstorageis145mt.InIndia,annualstoragelosseshavebeenestimated14mtworthofRs.7,000croreinwhichinsectsaloneaccountfornearlyRs.1,300crores.
•InIndiaoutofthetotalproduction,about30%ismarketablesurplus
•Remaining70%isretainedandstoredbyfarmersforconsumption,seed,feed.Hence,growerneedstoragefacilitytoholdaportionofproducetosellwhenthemarketingpriceisfavourable
•TradersandCo-operativesatmarketcentresneedstoragestructurestoholdgrainswhenthetransportfacilityisinadequate

快速办理(UAM毕业证书)马德里自治大学毕业证学位证一模一样

学校原件一模一样【微信：741003700 】《(UAM毕业证书)马德里自治大学毕业证学位证》【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才

GBSN - Biochemistry (Unit 6) Chemistry of Proteins

Chemistry of Proteins

Direct Seeded Rice - Climate Smart Agriculture

Direct Seeded Rice - Climate Smart AgricultureInternational Food Policy Research Institute- South Asia Office

PPT on Direct Seeded Rice 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.
Describing and Interpreting an Immersive Learning Case with the Immersion Cub...

Current descriptions of immersive learning cases are often difficult or impossible to compare. This is due to a myriad of different options on what details to include, which aspects are relevant, and on the descriptive approaches employed. Also, these aspects often combine very specific details with more general guidelines or indicate intents and rationales without clarifying their implementation. In this paper we provide a method to describe immersive learning cases that is structured to enable comparisons, yet flexible enough to allow researchers and practitioners to decide which aspects to include. This method leverages a taxonomy that classifies educational aspects at three levels (uses, practices, and strategies) and then utilizes two frameworks, the Immersive Learning Brain and the Immersion Cube, to enable a structured description and interpretation of immersive learning cases. The method is then demonstrated on a published immersive learning case on training for wind turbine maintenance using virtual reality. Applying the method results in a structured artifact, the Immersive Learning Case Sheet, that tags the case with its proximal uses, practices, and strategies, and refines the free text case description to ensure that matching details are included. This contribution is thus a case description method in support of future comparative research of immersive learning cases. We then discuss how the resulting description and interpretation can be leveraged to change immersion learning cases, by enriching them (considering low-effort changes or additions) or innovating (exploring more challenging avenues of transformation). The method holds significant promise to support better-grounded research in immersive learning.

Summary Of transcription and Translation.pdf

Hello Everyone Here We are Sharing You with The process of protien Synthesis in very short points you will be Able to understand It Very well

11.1 Role of physical biological in deterioration of grains.pdf

Storagedeteriorationisanyformoflossinquantityandqualityofbio-materials.
Themajorcausesofdeteriorationinstorage
•Physical
•Biological
•Mechanical
•Chemical
Storageonlypreservesquality.Itneverimprovesquality.
Itisadvisabletostartstoragewithqualityfoodproduct.Productwithinitialpoorqualityquicklydepreciates

Authoring a personal GPT for your research and practice: How we created the Q...

Thematic analysis in qualitative research is a time-consuming and systematic task, typically done using teams. Team members must ground their activities on common understandings of the major concepts underlying the thematic analysis, and define criteria for its development. However, conceptual misunderstandings, equivocations, and lack of adherence to criteria are challenges to the quality and speed of this process. Given the distributed and uncertain nature of this process, we wondered if the tasks in thematic analysis could be supported by readily available artificial intelligence chatbots. Our early efforts point to potential benefits: not just saving time in the coding process but better adherence to criteria and grounding, by increasing triangulation between humans and artificial intelligence. This tutorial will provide a description and demonstration of the process we followed, as two academic researchers, to develop a custom ChatGPT to assist with qualitative coding in the thematic data analysis process of immersive learning accounts in a survey of the academic literature: QUAL-E Immersive Learning Thematic Analysis Helper. In the hands-on time, participants will try out QUAL-E and develop their ideas for their own qualitative coding ChatGPT. Participants that have the paid ChatGPT Plus subscription can create a draft of their assistants. The organizers will provide course materials and slide deck that participants will be able to utilize to continue development of their custom GPT. The paid subscription to ChatGPT Plus is not required to participate in this workshop, just for trying out personal GPTs during it.

Discovery of An Apparent Red, High-Velocity Type Ia Supernova at 𝐳 = 2.9 wi...

We present the JWST discovery of SN 2023adsy, a transient object located in a host galaxy JADES-GS
+
53.13485
−
27.82088
with a host spectroscopic redshift of
2.903
±
0.007
. The transient was identified in deep James Webb Space Telescope (JWST)/NIRCam imaging from the JWST Advanced Deep Extragalactic Survey (JADES) program. Photometric and spectroscopic followup with NIRCam and NIRSpec, respectively, confirm the redshift and yield UV-NIR light-curve, NIR color, and spectroscopic information all consistent with a Type Ia classification. Despite its classification as a likely SN Ia, SN 2023adsy is both fairly red (
�
(
�
−
�
)
∼
0.9
) despite a host galaxy with low-extinction and has a high Ca II velocity (
19
,
000
±
2
,
000
km/s) compared to the general population of SNe Ia. While these characteristics are consistent with some Ca-rich SNe Ia, particularly SN 2016hnk, SN 2023adsy is intrinsically brighter than the low-
�
Ca-rich population. Although such an object is too red for any low-
�
cosmological sample, we apply a fiducial standardization approach to SN 2023adsy and find that the SN 2023adsy luminosity distance measurement is in excellent agreement (
≲
1
�
) with
Λ
CDM. Therefore unlike low-
�
Ca-rich SNe Ia, SN 2023adsy is standardizable and gives no indication that SN Ia standardized luminosities change significantly with redshift. A larger sample of distant SNe Ia is required to determine if SN Ia population characteristics at high-
�
truly diverge from their low-
�
counterparts, and to confirm that standardized luminosities nevertheless remain constant with redshift.

HUMAN EYE By-R.M Class 10 phy best digital notes.pdf

Class 10 human eye notes physics
Handwritten best quality

Alternate Wetting and Drying - Climate Smart Agriculture

Alternate Wetting and Drying - Climate Smart AgricultureInternational Food Policy Research Institute- South Asia Office

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. Physiology of Nervous System presentation.pptx

physiology of nervous system

8.Isolation of pure cultures and preservation of cultures.pdf

Isolation of pure culture, its various method.

在线办理(salfor毕业证书)索尔福德大学毕业证毕业完成信一模一样

学校原件一模一样【微信：741003700 】《(salfor毕业证书)索尔福德大学毕业证》【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
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- 1. Understanding Feature Space in Machine Learning Alice Zheng, Dato September 9, 2015 1
- 2. 2 My journey so far Applied machine learning (Data science) Build ML tools Shortage of experts and good tools.
- 3. 3 Why machine learning? Model data. Make predictions. Build intelligent applications.
- 4. 4 The machine learning pipeline I fell in love the instant I laid my eyes on that puppy. His big eyes and playful tail, his soft furry paws, … Raw data Features Models Predictions Deploy in production
- 5. Feature = numeric representation of raw data
- 6. 6 Representing natural text It is a puppy and it is extremely cute. What’s important? Phrases? Specific words? Ordering? Subject, object, verb? Classify: puppy or not? Raw Text {“it”:2, “is”:2, “a”:1, “puppy”:1, “and”:1, “extremely”:1, “cute”:1 } Bag of Words
- 7. 7 Representing natural text It is a puppy and it is extremely cute. Classify: puppy or not? Raw Text Bag of Words it 2 they 0 I 1 am 0 how 0 puppy 1 and 1 cat 0 aardvark 0 cute 1 extremely 1 … … Sparse vector representation
- 8. 8 Representing images Image source: “Recognizing and learning object categories,” Li Fei-Fei, Rob Fergus, Anthony Torralba, ICCV 2005—2009. Raw image: millions of RGB triplets, one for each pixel Classify: person or animal? Raw Image Bag of Visual Words
- 9. 9 Representing images Classify: person or animal? Raw Image Deep learning features 3.29 -15 -5.24 48.3 1.36 47.1 - 1.92 36.5 2.83 95.4 -19 -89 5.09 37.8 Dense vector representation
- 10. 10 Feature space in machine learning • Raw data high dimensional vectors • Collection of data points point cloud in feature space • Model = geometric summary of point cloud • Feature engineering = creating features of the appropriate granularity for the task
- 11. Crudely speaking, mathematicians fall into two categories: the algebraists, who find it easiest to reduce all problems to sets of numbers and variables, and the geometers, who understand the world through shapes. -- Masha Gessen, “Perfect Rigor”
- 12. 12 Algebra vs. Geometry a b c a2 + b2 = c2 Algebra Geometry Pythagorean Theorem (Euclidean space)
- 13. 13 Visualizing a sphere in 2D x2 + y2 = 1 a b c Pythagorean theorem: a2 + b2 = c2 x y 1 1
- 14. 14 Visualizing a sphere in 3D x2 + y2 + z2 = 1 x y z 1 1 1
- 15. 15 Visualizing a sphere in 4D x2 + y2 + z2 + t2 = 1 x y z 1 1 1
- 16. 16 Why are we looking at spheres? = = = = Poincaré Conjecture: All physical objects without holes is “equivalent” to a sphere.
- 17. 17 The power of higher dimensions • A sphere in 4D can model the birth and death process of physical objects • Point clouds = approximate geometric shapes • High dimensional features can model many things
- 19. 19 The challenge of high dimension geometry • Feature space can have hundreds to millions of dimensions • In high dimensions, our geometric imagination is limited - Algebra comes to our aid
- 20. 20 Visualizing bag-of-words puppy cute 1 1 I have a puppy and it is extremely cute I have a puppy and it is extremely cute it 1 they 0 I 1 am 0 how 0 puppy 1 and 1 cat 0 aardvark 0 zebra 0 cute 1 extremely 1 … …
- 21. 21 Visualizing bag-of-words puppy cute 1 1 1 extremely I have a puppy and it is extremely cute I have an extremely cute cat I have a cute puppy
- 22. 22 Document point cloud word 1 word 2
- 23. 23 What is a model? • Model = mathematical “summary” of data • What’s a summary? - A geometric shape
- 24. 24 Classification model Feature 2 Feature 1 Decide between two classes
- 25. 25 Clustering model Feature 2 Feature 1 Group data points tightly
- 26. 26 Regression model Target Feature Fit the target values
- 28. 28 When does bag-of-words fail? puppy cat 2 1 1 have I have a puppy I have a cat I have a kitten Task: find a surface that separates documents about dogs vs. cats Problem: the word “have” adds fluff instead of information I have a dog and I have a pen 1
- 29. 29 Improving on bag-of-words • Idea: “normalize” word counts so that popular words are discounted • Term frequency (tf) = Number of times a terms appears in a document • Inverse document frequency of word (idf) = • N = total number of documents • Tf-idf count = tf x idf
- 30. 30 From BOW to tf-idf puppy cat 2 1 1 have I have a puppy I have a cat I have a kitten idf(puppy) = log 4 idf(cat) = log 4 idf(have) = log 1 = 0 I have a dog and I have a pen 1
- 31. 31 From BOW to tf-idf puppy cat1 have tfidf(puppy) = log 4 tfidf(cat) = log 4 tfidf(have) = 0 I have a dog and I have a pen, I have a kitten 1 log 4 log 4 I have a cat I have a puppy Decision surface Tf-idf flattens uninformative dimensions in the BOW point cloud
- 32. 32 Entry points of feature engineering • Start from data and task - What’s the best text representation for classification? • Start from modeling method - What kind of features does k-means assume? - What does linear regression assume about the data?
- 33. 33 That’s not all, folks! • There’s a lot more to feature engineering: - Feature normalization - Feature transformations - “Regularizing” models - Learning the right features • Dato is hiring! jobs@dato.com alicez@dato.com @RainyData

- Features sit between raw data and model. They can make or break an application.