This document provides an unabridged review of supervised machine learning regression and classification techniques. It begins with an introduction to machine learning and artificial intelligence. It then describes regression and classification techniques for supervised learning problems, including linear regression, logistic regression, k-nearest neighbors, naive bayes, decision trees, support vector machines, and random forests. Practical examples are provided using Python code for applying these techniques to housing price prediction and iris species classification problems. The document concludes that the primary goal was to provide an extensive review of supervised machine learning methods.
Introduction to Machine Learning in Python using Scikit-LearnAmol Agrawal
This document outlines a proposed workshop on machine learning in Python using the Scikit-Learn module. The workshop will introduce machine learning concepts and how to use Scikit-Learn to implement supervised and unsupervised machine learning algorithms for classification, regression, dimensionality reduction, and clustering. It will provide example code notebooks and exercises for participants to get hands-on experience applying machine learning to real-world examples and incorporating machine learning into their own work.
Scikit-Learn is a powerful machine learning library implemented in Python with numeric and scientific computing powerhouses Numpy, Scipy, and matplotlib for extremely fast analysis of small to medium sized data sets. It is open source, commercially usable and contains many modern machine learning algorithms for classification, regression, clustering, feature extraction, and optimization. For this reason Scikit-Learn is often the first tool in a Data Scientists toolkit for machine learning of incoming data sets.
The purpose of this one day course is to serve as an introduction to Machine Learning with Scikit-Learn. We will explore several clustering, classification, and regression algorithms for a variety of machine learning tasks and learn how to implement these tasks with our data using Scikit-Learn and Python. In particular, we will structure our machine learning models as though we were producing a data product, an actionable model that can be used in larger programs or algorithms; rather than as simply a research or investigation methodology.
Introduction to Machine Learning with Python and scikit-learnMatt Hagy
PyATL talk about machine learning. Provides both an intro to machine learning and how to do it with Python. Includes simple examples with code and results.
This is slides used at Arithmer seminar given by Dr. Masaaki Uesaka at Arithmer inc.
It is a summary of recent methods for quality assurance of machine learning model.
Arithmer Seminar is weekly held, where professionals from within our company give lectures on their respective expertise.
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.
EuroSciPy 2019: Visual diagnostics at scaleRebecca Bilbro
The hunt for the most effective machine learning model is hard enough with a modest dataset, and much more so as our data grow! As we search for the optimal combination of features, algorithm, and hyperparameters, we often use tools like histograms, heatmaps, embeddings, and other plots to make our processes more informed and effective. However, large, high-dimensional datasets can prove particularly challenging. In this talk, we’ll explore a suite of visual diagnostics, investigate their strengths and weaknesses in face of increasingly big data, and consider how we can steer the machine learning process, not only purposefully but at scale!
This document provides an introduction to machine learning concepts including regression analysis, similarity and metric learning, Bayes classifiers, clustering, and neural networks. It discusses techniques such as linear regression, K-means clustering, naive Bayes classification, and backpropagation in neural networks. Code examples and exercises are provided to help readers learn how to apply these machine learning algorithms.
(Py)testing the Limits of Machine LearningRebecca Bilbro
Despite the hype cycle, each day machine learning becomes a little less magic and a little more real. Predictions increasingly drive our everyday lives, embedded into more of our everyday applications. To support this creative surge, development teams are evolving, integrating novel open source software and state-of-the-art GPU hardware, and bringing on essential new teammates like data ethicists and machine learning engineers. Software teams are also now challenged to build and maintain codebases that are intentionally not fully deterministic.
This nondeterminism can manifest in a number of surprising and oftentimes very stressful ways! Successive runs of model training may produce slight but meaningful variations. Data wrangling pipelines turn out to be extremely sensitive to the order in which transformations are applied, and require thoughtful orchestration to avoid leakage. Model hyperparameters that can be tuned independently may have mutually exclusive conditions. Models can also degrade over time, producing increasingly unreliable predictions. Moreover, open source libraries are living, dynamic things; the latest release of your team's favorite library might cause your code to suddenly behave in unexpected ways.
Put simply, as ML becomes more of an expectation than an exception in our industry, testing has never been more important! Fortunately, we are lucky to have a rich open source ecosystem to support us in our journey to build the next generation of apps in a safe, stable way. In this talk we'll share some hard-won lessons, favorite open source packages, and reusable techniques for testing ML software components.
This document contains lecture notes from a data structures course taught by Sanjay Goel at JIIT in 2004. The notes summarize 7 lectures that covered topics like what is engineering, representing real-world objects in computer memory using data structures, designing data structures for storing polynomials and number sequences, and algorithms for operations like deleting elements from linked lists and updating matching indices between collections. The lectures included in-class exercises, questions, and programming assignments to reinforce the concepts through practical examples and problems.
Introduction to Machine Learning in Python using Scikit-LearnAmol Agrawal
This document outlines a proposed workshop on machine learning in Python using the Scikit-Learn module. The workshop will introduce machine learning concepts and how to use Scikit-Learn to implement supervised and unsupervised machine learning algorithms for classification, regression, dimensionality reduction, and clustering. It will provide example code notebooks and exercises for participants to get hands-on experience applying machine learning to real-world examples and incorporating machine learning into their own work.
Scikit-Learn is a powerful machine learning library implemented in Python with numeric and scientific computing powerhouses Numpy, Scipy, and matplotlib for extremely fast analysis of small to medium sized data sets. It is open source, commercially usable and contains many modern machine learning algorithms for classification, regression, clustering, feature extraction, and optimization. For this reason Scikit-Learn is often the first tool in a Data Scientists toolkit for machine learning of incoming data sets.
The purpose of this one day course is to serve as an introduction to Machine Learning with Scikit-Learn. We will explore several clustering, classification, and regression algorithms for a variety of machine learning tasks and learn how to implement these tasks with our data using Scikit-Learn and Python. In particular, we will structure our machine learning models as though we were producing a data product, an actionable model that can be used in larger programs or algorithms; rather than as simply a research or investigation methodology.
Introduction to Machine Learning with Python and scikit-learnMatt Hagy
PyATL talk about machine learning. Provides both an intro to machine learning and how to do it with Python. Includes simple examples with code and results.
This is slides used at Arithmer seminar given by Dr. Masaaki Uesaka at Arithmer inc.
It is a summary of recent methods for quality assurance of machine learning model.
Arithmer Seminar is weekly held, where professionals from within our company give lectures on their respective expertise.
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.
EuroSciPy 2019: Visual diagnostics at scaleRebecca Bilbro
The hunt for the most effective machine learning model is hard enough with a modest dataset, and much more so as our data grow! As we search for the optimal combination of features, algorithm, and hyperparameters, we often use tools like histograms, heatmaps, embeddings, and other plots to make our processes more informed and effective. However, large, high-dimensional datasets can prove particularly challenging. In this talk, we’ll explore a suite of visual diagnostics, investigate their strengths and weaknesses in face of increasingly big data, and consider how we can steer the machine learning process, not only purposefully but at scale!
This document provides an introduction to machine learning concepts including regression analysis, similarity and metric learning, Bayes classifiers, clustering, and neural networks. It discusses techniques such as linear regression, K-means clustering, naive Bayes classification, and backpropagation in neural networks. Code examples and exercises are provided to help readers learn how to apply these machine learning algorithms.
(Py)testing the Limits of Machine LearningRebecca Bilbro
Despite the hype cycle, each day machine learning becomes a little less magic and a little more real. Predictions increasingly drive our everyday lives, embedded into more of our everyday applications. To support this creative surge, development teams are evolving, integrating novel open source software and state-of-the-art GPU hardware, and bringing on essential new teammates like data ethicists and machine learning engineers. Software teams are also now challenged to build and maintain codebases that are intentionally not fully deterministic.
This nondeterminism can manifest in a number of surprising and oftentimes very stressful ways! Successive runs of model training may produce slight but meaningful variations. Data wrangling pipelines turn out to be extremely sensitive to the order in which transformations are applied, and require thoughtful orchestration to avoid leakage. Model hyperparameters that can be tuned independently may have mutually exclusive conditions. Models can also degrade over time, producing increasingly unreliable predictions. Moreover, open source libraries are living, dynamic things; the latest release of your team's favorite library might cause your code to suddenly behave in unexpected ways.
Put simply, as ML becomes more of an expectation than an exception in our industry, testing has never been more important! Fortunately, we are lucky to have a rich open source ecosystem to support us in our journey to build the next generation of apps in a safe, stable way. In this talk we'll share some hard-won lessons, favorite open source packages, and reusable techniques for testing ML software components.
This document contains lecture notes from a data structures course taught by Sanjay Goel at JIIT in 2004. The notes summarize 7 lectures that covered topics like what is engineering, representing real-world objects in computer memory using data structures, designing data structures for storing polynomials and number sequences, and algorithms for operations like deleting elements from linked lists and updating matching indices between collections. The lectures included in-class exercises, questions, and programming assignments to reinforce the concepts through practical examples and problems.
Yellowbrick: Steering machine learning with visual transformersRebecca Bilbro
In machine learning, model selection is a bit more nuanced than simply picking the 'right' or 'wrong' algorithm. In practice, the workflow includes (1) selecting and/or engineering the smallest and most predictive feature set, (2) choosing a set of algorithms from a model family, and (3) tuning the algorithm hyperparameters to optimize performance. Recently, much of this workflow has been automated through grid search methods, standardized APIs, and GUI-based applications. In practice, however, human intuition and guidance can more effectively hone in on quality models than exhaustive search.
This talk presents a new Python library, Yellowbrick, which extends the Scikit-Learn API with a visual transfomer (visualizer) that can incorporate visualizations of the model selection process into pipelines and modeling workflow. Yellowbrick is an open source, pure Python project that extends Scikit-Learn with visual analysis and diagnostic tools. The Yellowbrick API also wraps matplotlib to create publication-ready figures and interactive data explorations while still allowing developers fine-grain control of figures. For users, Yellowbrick can help evaluate the performance, stability, and predictive value of machine learning models, and assist in diagnosing problems throughout the machine learning workflow.
In this talk, we'll explore not only what you can do with Yellowbrick, but how it works under the hood (since we're always looking for new contributors!). We'll illustrate how Yellowbrick extends the Scikit-Learn and Matplotlib APIs with a new core object: the Visualizer. Visualizers allow visual models to be fit and transformed as part of the Scikit-Learn Pipeline process - providing iterative visual diagnostics throughout the transformation of high dimensional data.
In machine learning, model selection is a bit more nuanced than simply picking the 'right' or 'wrong' algorithm. In practice, the workflow includes (1) selecting and/or engineering the smallest and most predictive feature set, (2) choosing a set of algorithms from a model family, and (3) tuning the algorithm hyperparameters to optimize performance. Recently, much of this workflow has been automated through grid search methods, standardized APIs, and GUI-based applications. In practice, however, human intuition and guidance can more effectively hone in on quality models than exhaustive search.
This talk presents a new open source Python library, Yellowbrick (scikit-yb.org), which extends the Scikit-Learn API with a visual transfomer (visualizer) that can incorporate visualizations of the model selection process into pipelines and modeling workflow. Visualizers enable machine learning practitioners to visually interpret the model selection process, steer workflows toward more predictive models, and avoid common pitfalls and traps. For users, Yellowbrick can help evaluate the performance, stability, and predictive value of machine learning models, and assist in diagnosing problems throughout the machine learning workflow.
MachineLearning for dummies with Python
Have you heard that Machine Learning is the next big thing?
Are you a dummy in terms of Machine Learning, and think that is a topic for mathematics with black-magic skills?
If your response to both questions is 'Yes', we are in the same position.
Still, thanks to the Web, Python and OpenSource libraries, we can overcome this situation and do some interesting stuff with Machine Learning.
OPTIMIZATION AS A MODEL FOR FEW-SHOT LEARNINGMLReview
This document proposes using meta-learning and an LSTM model to learn an optimization algorithm for few-shot learning. The model, called a meta-learner, is trained on multiple datasets to learn how to efficiently train a learner network on new small datasets. The meta-learner LSTM models the parameter updates of the learner network during training, learning an initialization and update rule. The inputs to the meta-learner are the loss, parameters, and gradient, and it outputs updated parameters. This learned update rule can then be used to train the learner network on new small datasets, enabling few-shot learning using only a small amount of labeled data.
This document discusses XGBoost, an optimized distributed gradient boosting library. It begins by explaining what XGBoost can do, including binary classification, multiclass classification, regression, and learning to rank. It then discusses boosted trees and their variants like GBDT, GBRT, and MART. It explains how tree ensembles work by combining many decision trees to make predictions and describes XGBoost's additive training process of greedily adding trees to minimize loss. It also covers XGBoost's efficient splitting algorithm for growing trees and references for further information.
Start machine learning in 5 simple stepsRenjith M P
Simple steps to get started with machine learning.
The use case uses python programming. Target audience is expected to have a very basic python knowledge.
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.
Generative Adversarial Networks : Basic architecture and variantsananth
In this presentation we review the fundamentals behind GANs and look at different variants. We quickly review the theory such as the cost functions, training procedure, challenges and go on to look at variants such as CycleGAN, SAGAN etc.
Machine learning algorithms can be used to make predictions from data. There are several types of algorithms for supervised learning tasks like regression and classification, as well as unsupervised learning tasks like clustering and dimensionality reduction. The scikit-learn library provides popular machine learning algorithms and datasets that can be used to fit models to data and validate performance. Key steps in the machine learning process include getting data, selecting an algorithm, fitting the model to training data, and evaluating performance on test data to avoid overfitting or underfitting. Performance metrics like precision, recall, and F1 score are used to quantify how well models generalize to new data.
.NET Fest 2017. Игорь Кочетов. Классификация результатов тестирования произво...NETFest
В этом докладе мы обсудим базовые алгоритмы и области применения Machine Learning (ML), затем рассмотрим практический пример построения системы классификации результатов измерения производительности, получаемых в Unity с помощью внутренней системы Performance Test Framework, для поиска регрессий производительности или нестабильных тестов. Также попробуем разобраться в критериях, по которым можно оценивать производительность алгоритмов ML и способы их отладки.
The ABC of Implementing Supervised Machine Learning with Python.pptxRuby Shrestha
It is to our fact that machine learning has taken a significant height. However, knowing and understanding how small problems can be solved from a machine learning perspective is necessary to form a good base, appreciate the process of implementation and get started in this domain. Therefore, in this post, I would like to talk about the ABC of implementing Supervised Machine Learning with Python by navigating through a simple example, which is, adding two numbers. So, to put it in simple terms, I would like to make a machine learn to add. This can be put in other words; I would like to develop a predictive model that can add. Sounds simple, right? View the presentation for more details.
Have you heard that Machine Learning is the next big thing?
Are you a dummy in terms of Machine Learning, and think that is a topic for mathematicians with black-magic skills?
If your response to both questions is ‘Yes’, we are in the same position.
Still, thanks to the Web, Python and OpenSource libraries, we can overcome this situation and do some interesting stuff with Machine Learning.
In machine learning, model selection is a bit more nuanced than simply picking the 'right' or 'wrong' algorithm. In practice, the workflow includes (1) selecting and/or engineering the smallest and most predictive feature set, (2) choosing a set of algorithms from a model family, and (3) tuning the algorithm hyperparameters to optimize performance. Recently, much of this workflow has been automated through grid search methods, standardized APIs, and GUI-based applications. In practice, however, human intuition and guidance can more effectively hone in on quality models than exhaustive search.
This talk presents a new open source Python library, Yellowbrick, which extends the Scikit-Learn API with a visual transfomer (visualizer) that can incorporate visualizations of the model selection process into pipelines and modeling workflow. Visualizers enable machine learning practitioners to visually interpret the model selection process, steer workflows toward more predictive models, and avoid common pitfalls and traps. For users, Yellowbrick can help evaluate the performance, stability, and predictive value of machine learning models, and assist in diagnosing problems throughout the machine learning workflow.
This document provides an introduction to XGBoost, including:
1. XGBoost is an important machine learning library that is commonly used by winners of Kaggle competitions.
2. A quick example is shown using XGBoost to predict diabetes based on patient data, achieving good results with only 20 lines of simple code.
3. XGBoost works by creating an ensemble of decision trees through boosting, and focuses on explaining concepts at a high level rather than detailed algorithms.
This document discusses Python namespaces and modules. It explains that namespaces prevent name conflicts and modules are the basic unit of code reuse. Functions, classes, and modules each have their own namespace. Importing modules adds their names to the global namespace. The document recommends importing modules using 'import' to avoid potential name conflicts or namespace pollution. It also describes how scopes resolve which definition to use when the same name is defined in multiple scopes.
[Eestec] Machine Learning online seminar 1, 12 2016Grigoris C
This document summarizes a machine learning seminar that covers:
- An introduction to machine learning and real-world applications like self-driving cars and personalized assistants.
- Supervised learning methods including classification to assign labels and regression to estimate continuous outputs.
- The machine learning process including data acquisition, model selection, preprocessing, feature extraction, model learning, and evaluation.
- Practical tips for learning machine learning like online courses and books.
- A practical example of a machine learning project with a link to the full code.
Automated machine learning (AutoML) systems can find the optimal machine learning algorithm and hyperparameters for a given dataset without human intervention. AutoML addresses the skills gap in data science by allowing data scientists to build more models in less time. On average, tuning hyperparameters results in a 5-10% improvement in accuracy over default parameters. However, the best parameters vary across problems. AutoML tools like Auto-sklearn use techniques like Bayesian optimization and meta-learning to efficiently search the hyperparameter space. Auto-sklearn has won several AutoML challenges due to its ability to effectively optimize over 100 hyperparameters.
A brief review about Python for computer vision showing the different modules necessary to dive into computer vision.
The modules presented are NumPy, SciPy, and Matplotlib.
Model Drift Monitoring using Tensorflow Model AnalysisVivek Raja P S
This document discusses model drift monitoring using TensorFlow Model Analysis (TFMA). It begins with an introduction to the presenter and an overview of monitoring machine learning models in production. It then defines model drift as changes in the statistical structure of data over time which can degrade a model's performance. The production point of view for model monitoring is discussed as defining thresholds, detecting violations, and safeguarding systems. TFMA is introduced as a library for evaluating models on large datasets in a distributed manner and comparing metrics over time and across data slices for monitoring model quality and performance. The presentation concludes with a code demo and Q&A.
1. Machine learning is the use and development of computer systems that are able to learn and adapt without explicit instructions by using algorithms and statistical models to analyze patterns in data.
2. The document provides examples of machine learning applications like facial recognition, voice recognition in healthcare, weather forecasting, and more. It also discusses the process of machine learning and popular machine learning algorithms.
3. The document demonstrates machine learning using a decision tree algorithm on music purchase data to predict whether a customer is male or female based on attributes like age and number of songs purchased. It imports relevant Python libraries and splits the data into training and test sets to evaluate the model's performance.
The document provides an overview of machine learning, including definitions, types of machine learning algorithms, and the machine learning process. It defines machine learning as using algorithms to learn from data and make predictions. The main types discussed are supervised learning (classification, regression), unsupervised learning (clustering, association rules), and deep learning using neural networks. The machine learning process involves gathering data, feature engineering, splitting data into training/test sets, selecting an algorithm, training a model, validating it on a validation set, and testing it on a held-out test set. Key enablers of machine learning like large datasets and computing power are also mentioned.
Yellowbrick: Steering machine learning with visual transformersRebecca Bilbro
In machine learning, model selection is a bit more nuanced than simply picking the 'right' or 'wrong' algorithm. In practice, the workflow includes (1) selecting and/or engineering the smallest and most predictive feature set, (2) choosing a set of algorithms from a model family, and (3) tuning the algorithm hyperparameters to optimize performance. Recently, much of this workflow has been automated through grid search methods, standardized APIs, and GUI-based applications. In practice, however, human intuition and guidance can more effectively hone in on quality models than exhaustive search.
This talk presents a new Python library, Yellowbrick, which extends the Scikit-Learn API with a visual transfomer (visualizer) that can incorporate visualizations of the model selection process into pipelines and modeling workflow. Yellowbrick is an open source, pure Python project that extends Scikit-Learn with visual analysis and diagnostic tools. The Yellowbrick API also wraps matplotlib to create publication-ready figures and interactive data explorations while still allowing developers fine-grain control of figures. For users, Yellowbrick can help evaluate the performance, stability, and predictive value of machine learning models, and assist in diagnosing problems throughout the machine learning workflow.
In this talk, we'll explore not only what you can do with Yellowbrick, but how it works under the hood (since we're always looking for new contributors!). We'll illustrate how Yellowbrick extends the Scikit-Learn and Matplotlib APIs with a new core object: the Visualizer. Visualizers allow visual models to be fit and transformed as part of the Scikit-Learn Pipeline process - providing iterative visual diagnostics throughout the transformation of high dimensional data.
In machine learning, model selection is a bit more nuanced than simply picking the 'right' or 'wrong' algorithm. In practice, the workflow includes (1) selecting and/or engineering the smallest and most predictive feature set, (2) choosing a set of algorithms from a model family, and (3) tuning the algorithm hyperparameters to optimize performance. Recently, much of this workflow has been automated through grid search methods, standardized APIs, and GUI-based applications. In practice, however, human intuition and guidance can more effectively hone in on quality models than exhaustive search.
This talk presents a new open source Python library, Yellowbrick (scikit-yb.org), which extends the Scikit-Learn API with a visual transfomer (visualizer) that can incorporate visualizations of the model selection process into pipelines and modeling workflow. Visualizers enable machine learning practitioners to visually interpret the model selection process, steer workflows toward more predictive models, and avoid common pitfalls and traps. For users, Yellowbrick can help evaluate the performance, stability, and predictive value of machine learning models, and assist in diagnosing problems throughout the machine learning workflow.
MachineLearning for dummies with Python
Have you heard that Machine Learning is the next big thing?
Are you a dummy in terms of Machine Learning, and think that is a topic for mathematics with black-magic skills?
If your response to both questions is 'Yes', we are in the same position.
Still, thanks to the Web, Python and OpenSource libraries, we can overcome this situation and do some interesting stuff with Machine Learning.
OPTIMIZATION AS A MODEL FOR FEW-SHOT LEARNINGMLReview
This document proposes using meta-learning and an LSTM model to learn an optimization algorithm for few-shot learning. The model, called a meta-learner, is trained on multiple datasets to learn how to efficiently train a learner network on new small datasets. The meta-learner LSTM models the parameter updates of the learner network during training, learning an initialization and update rule. The inputs to the meta-learner are the loss, parameters, and gradient, and it outputs updated parameters. This learned update rule can then be used to train the learner network on new small datasets, enabling few-shot learning using only a small amount of labeled data.
This document discusses XGBoost, an optimized distributed gradient boosting library. It begins by explaining what XGBoost can do, including binary classification, multiclass classification, regression, and learning to rank. It then discusses boosted trees and their variants like GBDT, GBRT, and MART. It explains how tree ensembles work by combining many decision trees to make predictions and describes XGBoost's additive training process of greedily adding trees to minimize loss. It also covers XGBoost's efficient splitting algorithm for growing trees and references for further information.
Start machine learning in 5 simple stepsRenjith M P
Simple steps to get started with machine learning.
The use case uses python programming. Target audience is expected to have a very basic python knowledge.
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.
Generative Adversarial Networks : Basic architecture and variantsananth
In this presentation we review the fundamentals behind GANs and look at different variants. We quickly review the theory such as the cost functions, training procedure, challenges and go on to look at variants such as CycleGAN, SAGAN etc.
Machine learning algorithms can be used to make predictions from data. There are several types of algorithms for supervised learning tasks like regression and classification, as well as unsupervised learning tasks like clustering and dimensionality reduction. The scikit-learn library provides popular machine learning algorithms and datasets that can be used to fit models to data and validate performance. Key steps in the machine learning process include getting data, selecting an algorithm, fitting the model to training data, and evaluating performance on test data to avoid overfitting or underfitting. Performance metrics like precision, recall, and F1 score are used to quantify how well models generalize to new data.
.NET Fest 2017. Игорь Кочетов. Классификация результатов тестирования произво...NETFest
В этом докладе мы обсудим базовые алгоритмы и области применения Machine Learning (ML), затем рассмотрим практический пример построения системы классификации результатов измерения производительности, получаемых в Unity с помощью внутренней системы Performance Test Framework, для поиска регрессий производительности или нестабильных тестов. Также попробуем разобраться в критериях, по которым можно оценивать производительность алгоритмов ML и способы их отладки.
The ABC of Implementing Supervised Machine Learning with Python.pptxRuby Shrestha
It is to our fact that machine learning has taken a significant height. However, knowing and understanding how small problems can be solved from a machine learning perspective is necessary to form a good base, appreciate the process of implementation and get started in this domain. Therefore, in this post, I would like to talk about the ABC of implementing Supervised Machine Learning with Python by navigating through a simple example, which is, adding two numbers. So, to put it in simple terms, I would like to make a machine learn to add. This can be put in other words; I would like to develop a predictive model that can add. Sounds simple, right? View the presentation for more details.
Have you heard that Machine Learning is the next big thing?
Are you a dummy in terms of Machine Learning, and think that is a topic for mathematicians with black-magic skills?
If your response to both questions is ‘Yes’, we are in the same position.
Still, thanks to the Web, Python and OpenSource libraries, we can overcome this situation and do some interesting stuff with Machine Learning.
In machine learning, model selection is a bit more nuanced than simply picking the 'right' or 'wrong' algorithm. In practice, the workflow includes (1) selecting and/or engineering the smallest and most predictive feature set, (2) choosing a set of algorithms from a model family, and (3) tuning the algorithm hyperparameters to optimize performance. Recently, much of this workflow has been automated through grid search methods, standardized APIs, and GUI-based applications. In practice, however, human intuition and guidance can more effectively hone in on quality models than exhaustive search.
This talk presents a new open source Python library, Yellowbrick, which extends the Scikit-Learn API with a visual transfomer (visualizer) that can incorporate visualizations of the model selection process into pipelines and modeling workflow. Visualizers enable machine learning practitioners to visually interpret the model selection process, steer workflows toward more predictive models, and avoid common pitfalls and traps. For users, Yellowbrick can help evaluate the performance, stability, and predictive value of machine learning models, and assist in diagnosing problems throughout the machine learning workflow.
This document provides an introduction to XGBoost, including:
1. XGBoost is an important machine learning library that is commonly used by winners of Kaggle competitions.
2. A quick example is shown using XGBoost to predict diabetes based on patient data, achieving good results with only 20 lines of simple code.
3. XGBoost works by creating an ensemble of decision trees through boosting, and focuses on explaining concepts at a high level rather than detailed algorithms.
This document discusses Python namespaces and modules. It explains that namespaces prevent name conflicts and modules are the basic unit of code reuse. Functions, classes, and modules each have their own namespace. Importing modules adds their names to the global namespace. The document recommends importing modules using 'import' to avoid potential name conflicts or namespace pollution. It also describes how scopes resolve which definition to use when the same name is defined in multiple scopes.
[Eestec] Machine Learning online seminar 1, 12 2016Grigoris C
This document summarizes a machine learning seminar that covers:
- An introduction to machine learning and real-world applications like self-driving cars and personalized assistants.
- Supervised learning methods including classification to assign labels and regression to estimate continuous outputs.
- The machine learning process including data acquisition, model selection, preprocessing, feature extraction, model learning, and evaluation.
- Practical tips for learning machine learning like online courses and books.
- A practical example of a machine learning project with a link to the full code.
Automated machine learning (AutoML) systems can find the optimal machine learning algorithm and hyperparameters for a given dataset without human intervention. AutoML addresses the skills gap in data science by allowing data scientists to build more models in less time. On average, tuning hyperparameters results in a 5-10% improvement in accuracy over default parameters. However, the best parameters vary across problems. AutoML tools like Auto-sklearn use techniques like Bayesian optimization and meta-learning to efficiently search the hyperparameter space. Auto-sklearn has won several AutoML challenges due to its ability to effectively optimize over 100 hyperparameters.
A brief review about Python for computer vision showing the different modules necessary to dive into computer vision.
The modules presented are NumPy, SciPy, and Matplotlib.
Model Drift Monitoring using Tensorflow Model AnalysisVivek Raja P S
This document discusses model drift monitoring using TensorFlow Model Analysis (TFMA). It begins with an introduction to the presenter and an overview of monitoring machine learning models in production. It then defines model drift as changes in the statistical structure of data over time which can degrade a model's performance. The production point of view for model monitoring is discussed as defining thresholds, detecting violations, and safeguarding systems. TFMA is introduced as a library for evaluating models on large datasets in a distributed manner and comparing metrics over time and across data slices for monitoring model quality and performance. The presentation concludes with a code demo and Q&A.
1. Machine learning is the use and development of computer systems that are able to learn and adapt without explicit instructions by using algorithms and statistical models to analyze patterns in data.
2. The document provides examples of machine learning applications like facial recognition, voice recognition in healthcare, weather forecasting, and more. It also discusses the process of machine learning and popular machine learning algorithms.
3. The document demonstrates machine learning using a decision tree algorithm on music purchase data to predict whether a customer is male or female based on attributes like age and number of songs purchased. It imports relevant Python libraries and splits the data into training and test sets to evaluate the model's performance.
The document provides an overview of machine learning, including definitions, types of machine learning algorithms, and the machine learning process. It defines machine learning as using algorithms to learn from data and make predictions. The main types discussed are supervised learning (classification, regression), unsupervised learning (clustering, association rules), and deep learning using neural networks. The machine learning process involves gathering data, feature engineering, splitting data into training/test sets, selecting an algorithm, training a model, validating it on a validation set, and testing it on a held-out test set. Key enablers of machine learning like large datasets and computing power are also mentioned.
Workshop: Your first machine learning projectAlex Austin
Tutorial to help you create your first machine learning project. The goal was to make this straightforward even someone who's never written a line of code. We gave the workshop to MBA students at UC Berkeley and had a lot of fun learning together - don't be intimidated, anyone can do it!
The document discusses artificial intelligence and provides an overview of key topics including:
1. Natural language processing techniques like text vectorization, seq2seq modeling, attention mechanisms, and transformers.
2. The use of AI in physics and responsible AI approaches like explainable, safe, and fair AI.
3. An introduction to foundational AI concepts like the four paradigms of science, types of machine learning, deep learning models, and applications of AI in areas such as computer vision and robotics.
This document provides information about an internship in artificial intelligence using Python. It includes definitions of common AI abbreviations and compares human organs to AI tools. It also discusses basics of AI, concepts in AI like machine learning and neural networks, qualities of humans and AI, important IDE software, useful Python packages, types of AI and machine learning, supervised and unsupervised machine learning algorithms, and the methodology for an image classification project including preprocessing data and extracting features from images.
This document provides information about an internship in artificial intelligence using Python. It includes abbreviations commonly used in AI and machine learning and compares human organs to AI tools. It also discusses basics of AI, concepts in AI like machine learning and neural networks, qualities of humans and AI, important software for AI like Anaconda and TensorFlow, and types of machine learning algorithms. The document provides an overview of the topics that will be covered in the internship.
Water Quality Index Calculation of River Ganga using Decision Tree AlgorithmIRJET Journal
This document discusses using machine learning algorithms to calculate the water quality index of the Ganga River in India. Specifically, it aims to analyze water quality data collected from various cities along the Ganga Riverbed in different seasons (summer, monsoon, winter) and assess whether the river water is potable or not. The researchers designed a machine learning model using the decision tree algorithm that calculates the water quality index based on 9 physicochemical parameters. It will be implemented as a Python-based web application using the Flask framework. The model is trained on collected datasets to predict water quality and determine if it is safe for drinking.
This document summarizes an internship project on machine learning. It introduces machine learning concepts like supervised and unsupervised learning. It describes using Python for machine learning and exploring data through visualization and statistics. Regression algorithms like linear regression and logistic regression are covered as well as decision trees, ensemble models, and clustering. The project involves building an OMR evaluator to automatically grade answer sheets using computer vision and machine learning, reducing the manual workload for teachers. The user interface allows uploading student response pictures and setting answers to automatically generate results.
IRJET- Sentimental Analysis for Online Reviews using Machine Learning AlgorithmsIRJET Journal
The document discusses sentiment analysis of online product reviews using machine learning algorithms. It first provides background on sentiment analysis and its uses. It then describes preprocessing customer review data and extracting features using count and TF-IDF vectorization. Three machine learning algorithms are tested - support vector machine (SVM), random forest, and XGBoost classifier. The results show that XGBoost achieved higher accuracy than SVM and random forest for sentiment classification of the product review data.
Jose Leiva, data scientist at Ets Asset Management Factory, gives an accurate and simple introduction to Machine Learning. He explains some of the problems that quantitative managers have to get alpha in the markets, and how to face them using Deep Learning.
This document outlines the objectives and experiments for a Machine Learning laboratory course. The course aims to enable students to implement machine learning algorithms and apply them to datasets without using built-in libraries. The 10 experiments cover algorithms like decision trees, neural networks, naive Bayes classifier, k-means clustering, and locally weighted regression. Students will code the algorithms from scratch in Java or Python and evaluate them on standard datasets. The document provides details on each experiment, such as reading data from CSV files and calculating accuracy metrics.
How to implement artificial intelligence solutionsCarlos Toxtli
The document provides an overview of how to implement artificial intelligence solutions. It discusses getting started in AI by either creating new techniques as a scientist or implementing existing techniques as an engineer. It then covers various machine learning algorithms like linear regression, decision trees, random forests, naive bayes, k-nearest neighbors, k-means, and support vector machines. Finally, it introduces deep learning concepts like artificial neural networks, neurons, layers, gradients, optimizers, overfitting, and regularization. The document serves as a guide for implementing both machine learning and deep learning techniques for AI applications.
Machine learning for sensor Data AnalyticsMATLABISRAEL
במצגת זאת נראה כיצד עושים Machine Learning בסביבת MATLAB. נציג מספר יכולות ואפליקציות מובנות ההופכות את תהליך למידת המכונה ליעיל ומהיר יותר – כלים כמו ה-Classification Learner, ה-Regression Learner ו-Bayesian Optimization. בהסתמך על מידע המתקבל מחיישני סמארטפון, נבנה מערכת סיווג המזהה את הפעילות שמבצע המשתמש – הליכה, טיפוס במדרגות, שכיבה, וכו'
Machine Learning Laboratory set of experiments, including ANN, Backpropagation, K-Means, Hierarchical Clustering, Linear Regression, Multivariate Regression, Fuzzy Logic.
This document summarizes a research project that aims to develop an application to predict airline ticket prices using machine learning techniques. The researchers collected over 10,000 records of flight data including features like source, destination, date, time, number of stops, and price. They preprocessed the data, selected important features, and applied machine learning algorithms like linear regression, decision trees, and random forests to build predictive models. The random forest model provided the most accurate predictions according to performance metrics like MAE, MSE, and RMSE. The researchers propose deploying the best model in a web application using Flask for the backend and Bootstrap for the frontend so users can input flight details and receive predicted price outputs.
Machine Learning : why we should know and how it worksKevin Lee
This document provides an overview of machine learning, including:
- An introduction to machine learning and why it is important.
- The main types of machine learning algorithms: supervised learning, unsupervised learning, and deep neural networks.
- Examples of how machine learning algorithms work, such as logistic regression, support vector machines, and k-means clustering.
- How machine learning is being applied in various industries like healthcare, commerce, and more.
Machine Learning with Python discusses machine learning concepts and the Python tools used for machine learning. It introduces machine learning terminology and different types of learning. It describes the Pandas, Matplotlib and scikit-learn frameworks for data analysis and machine learning in Python. Examples show simple programs for supervised learning using linear regression and unsupervised learning using K-means clustering.
The document describes developing a model to predict house prices using deep learning techniques. It proposes using a dataset with house features without labels and applying regression algorithms like K-nearest neighbors, support vector machine, and artificial neural networks. The models are trained and tested on split data, with the artificial neural network achieving the lowest mean absolute percentage error of 18.3%, indicating it is the most accurate model for predicting house prices based on the data.
Similar to IRJET- Unabridged Review of Supervised Machine Learning Regression and Classification Technique with Practical Task (20)
TUNNELING IN HIMALAYAS WITH NATM METHOD: A SPECIAL REFERENCES TO SUNGAL TUNNE...IRJET Journal
1) The document discusses the Sungal Tunnel project in Jammu and Kashmir, India, which is being constructed using the New Austrian Tunneling Method (NATM).
2) NATM involves continuous monitoring during construction to adapt to changing ground conditions, and makes extensive use of shotcrete for temporary tunnel support.
3) The methodology section outlines the systematic geotechnical design process for tunnels according to Austrian guidelines, and describes the various steps of NATM tunnel construction including initial and secondary tunnel support.
STUDY THE EFFECT OF RESPONSE REDUCTION FACTOR ON RC FRAMED STRUCTUREIRJET Journal
This study examines the effect of response reduction factors (R factors) on reinforced concrete (RC) framed structures through nonlinear dynamic analysis. Three RC frame models with varying heights (4, 8, and 12 stories) were analyzed in ETABS software under different R factors ranging from 1 to 5. The results showed that displacement increased as the R factor decreased, indicating less linear behavior for lower R factors. Drift also decreased proportionally with increasing R factors from 1 to 5. Shear forces in the frames decreased with higher R factors. In general, R factors of 3 to 5 produced more satisfactory performance with less displacement and drift. The displacement variations between different building heights were consistent at different R factors. This study evaluated how R factors influence
A COMPARATIVE ANALYSIS OF RCC ELEMENT OF SLAB WITH STARK STEEL (HYSD STEEL) A...IRJET Journal
This study compares the use of Stark Steel and TMT Steel as reinforcement materials in a two-way reinforced concrete slab. Mechanical testing is conducted to determine the tensile strength, yield strength, and other properties of each material. A two-way slab design adhering to codes and standards is executed with both materials. The performance is analyzed in terms of deflection, stability under loads, and displacement. Cost analyses accounting for material, durability, maintenance, and life cycle costs are also conducted. The findings provide insights into the economic and structural implications of each material for reinforcement selection and recommendations on the most suitable material based on the analysis.
Effect of Camber and Angles of Attack on Airfoil CharacteristicsIRJET Journal
This document discusses a study analyzing the effect of camber, position of camber, and angle of attack on the aerodynamic characteristics of airfoils. Sixteen modified asymmetric NACA airfoils were analyzed using computational fluid dynamics (CFD) by varying the camber, camber position, and angle of attack. The results showed the relationship between these parameters and the lift coefficient, drag coefficient, and lift to drag ratio. This provides insight into how changes in airfoil geometry impact aerodynamic performance.
A Review on the Progress and Challenges of Aluminum-Based Metal Matrix Compos...IRJET Journal
This document reviews the progress and challenges of aluminum-based metal matrix composites (MMCs), focusing on their fabrication processes and applications. It discusses how various aluminum MMCs have been developed using reinforcements like borides, carbides, oxides, and nitrides to improve mechanical and wear properties. These composites have gained prominence for their lightweight, high-strength and corrosion resistance properties. The document also examines recent advancements in fabrication techniques for aluminum MMCs and their growing applications in industries such as aerospace and automotive. However, it notes that challenges remain around issues like improper mixing of reinforcements and reducing reinforcement agglomeration.
Dynamic Urban Transit Optimization: A Graph Neural Network Approach for Real-...IRJET Journal
This document discusses research on using graph neural networks (GNNs) for dynamic optimization of public transportation networks in real-time. GNNs represent transit networks as graphs with nodes as stops and edges as connections. The GNN model aims to optimize networks using real-time data on vehicle locations, arrival times, and passenger loads. This helps increase mobility, decrease traffic, and improve efficiency. The system continuously trains and infers to adapt to changing transit conditions, providing decision support tools. While research has focused on performance, more work is needed on security, socio-economic impacts, contextual generalization of models, continuous learning approaches, and effective real-time visualization.
Structural Analysis and Design of Multi-Storey Symmetric and Asymmetric Shape...IRJET Journal
This document summarizes a research project that aims to compare the structural performance of conventional slab and grid slab systems in multi-story buildings using ETABS software. The study will analyze both symmetric and asymmetric building models under various loading conditions. Parameters like deflections, moments, shears, and stresses will be examined to evaluate the structural effectiveness of each slab type. The results will provide insights into the comparative behavior of conventional and grid slabs to help engineers and architects select appropriate slab systems based on building layouts and design requirements.
A Review of “Seismic Response of RC Structures Having Plan and Vertical Irreg...IRJET Journal
This document summarizes and reviews a research paper on the seismic response of reinforced concrete (RC) structures with plan and vertical irregularities, with and without infill walls. It discusses how infill walls can improve or reduce the seismic performance of RC buildings, depending on factors like wall layout, height distribution, connection to the frame, and relative stiffness of walls and frames. The reviewed research paper analyzes the behavior of infill walls, effects of vertical irregularities, and seismic performance of high-rise structures under linear static and dynamic analysis. It studies response characteristics like story drift, deflection and shear. The document also provides literature on similar research investigating the effects of infill walls, soft stories, plan irregularities, and different
This document provides a review of machine learning techniques used in Advanced Driver Assistance Systems (ADAS). It begins with an abstract that summarizes key applications of machine learning in ADAS, including object detection, recognition, and decision-making. The introduction discusses the integration of machine learning in ADAS and how it is transforming vehicle safety. The literature review then examines several research papers on topics like lightweight deep learning models for object detection and lane detection models using image processing. It concludes by discussing challenges and opportunities in the field, such as improving algorithm robustness and adaptability.
Long Term Trend Analysis of Precipitation and Temperature for Asosa district,...IRJET Journal
The document analyzes temperature and precipitation trends in Asosa District, Benishangul Gumuz Region, Ethiopia from 1993 to 2022 based on data from the local meteorological station. The results show:
1) The average maximum and minimum annual temperatures have generally decreased over time, with maximum temperatures decreasing by a factor of -0.0341 and minimum by -0.0152.
2) Mann-Kendall tests found the decreasing temperature trends to be statistically significant for annual maximum temperatures but not for annual minimum temperatures.
3) Annual precipitation in Asosa District showed a statistically significant increasing trend.
The conclusions recommend development planners account for rising summer precipitation and declining temperatures in
P.E.B. Framed Structure Design and Analysis Using STAAD ProIRJET Journal
This document discusses the design and analysis of pre-engineered building (PEB) framed structures using STAAD Pro software. It provides an overview of PEBs, including that they are designed off-site with building trusses and beams produced in a factory. STAAD Pro is identified as a key tool for modeling, analyzing, and designing PEBs to ensure their performance and safety under various load scenarios. The document outlines modeling structural parts in STAAD Pro, evaluating structural reactions, assigning loads, and following international design codes and standards. In summary, STAAD Pro is used to design and analyze PEB framed structures to ensure safety and code compliance.
A Review on Innovative Fiber Integration for Enhanced Reinforcement of Concre...IRJET Journal
This document provides a review of research on innovative fiber integration methods for reinforcing concrete structures. It discusses studies that have explored using carbon fiber reinforced polymer (CFRP) composites with recycled plastic aggregates to develop more sustainable strengthening techniques. It also examines using ultra-high performance fiber reinforced concrete to improve shear strength in beams. Additional topics covered include the dynamic responses of FRP-strengthened beams under static and impact loads, and the performance of preloaded CFRP-strengthened fiber reinforced concrete beams. The review highlights the potential of fiber composites to enable more sustainable and resilient construction practices.
Survey Paper on Cloud-Based Secured Healthcare SystemIRJET Journal
This document summarizes a survey on securing patient healthcare data in cloud-based systems. It discusses using technologies like facial recognition, smart cards, and cloud computing combined with strong encryption to securely store patient data. The survey found that healthcare professionals believe digitizing patient records and storing them in a centralized cloud system would improve access during emergencies and enable more efficient care compared to paper-based systems. However, ensuring privacy and security of patient data is paramount as healthcare incorporates these digital technologies.
Review on studies and research on widening of existing concrete bridgesIRJET Journal
This document summarizes several studies that have been conducted on widening existing concrete bridges. It describes a study from China that examined load distribution factors for a bridge widened with composite steel-concrete girders. It also outlines challenges and solutions for widening a bridge in the UAE, including replacing bearings and stitching the new and existing structures. Additionally, it discusses two bridge widening projects in New Zealand that involved adding precast beams and stitching to connect structures. Finally, safety measures and challenges for strengthening a historic bridge in Switzerland under live traffic are presented.
React based fullstack edtech web applicationIRJET Journal
The document describes the architecture of an educational technology web application built using the MERN stack. It discusses the frontend developed with ReactJS, backend with NodeJS and ExpressJS, and MongoDB database. The frontend provides dynamic user interfaces, while the backend offers APIs for authentication, course management, and other functions. MongoDB enables flexible data storage. The architecture aims to provide a scalable, responsive platform for online learning.
A Comprehensive Review of Integrating IoT and Blockchain Technologies in the ...IRJET Journal
This paper proposes integrating Internet of Things (IoT) and blockchain technologies to help implement objectives of India's National Education Policy (NEP) in the education sector. The paper discusses how blockchain could be used for secure student data management, credential verification, and decentralized learning platforms. IoT devices could create smart classrooms, automate attendance tracking, and enable real-time monitoring. Blockchain would ensure integrity of exam processes and resource allocation, while smart contracts automate agreements. The paper argues this integration has potential to revolutionize education by making it more secure, transparent and efficient, in alignment with NEP goals. However, challenges like infrastructure needs, data privacy, and collaborative efforts are also discussed.
A REVIEW ON THE PERFORMANCE OF COCONUT FIBRE REINFORCED CONCRETE.IRJET Journal
This document provides a review of research on the performance of coconut fibre reinforced concrete. It summarizes several studies that tested different volume fractions and lengths of coconut fibres in concrete mixtures with varying compressive strengths. The studies found that coconut fibre improved properties like tensile strength, toughness, crack resistance, and spalling resistance compared to plain concrete. Volume fractions of 2-5% and fibre lengths of 20-50mm produced the best results. The document concludes that using a 4-5% volume fraction of coconut fibres 30-40mm in length with M30-M60 grade concrete would provide benefits based on previous research.
Optimizing Business Management Process Workflows: The Dynamic Influence of Mi...IRJET Journal
The document discusses optimizing business management processes through automation using Microsoft Power Automate and artificial intelligence. It provides an overview of Power Automate's key components and features for automating workflows across various apps and services. The document then presents several scenarios applying automation solutions to common business processes like data entry, monitoring, HR, finance, customer support, and more. It estimates the potential time and cost savings from implementing automation for each scenario. Finally, the conclusion emphasizes the transformative impact of AI and automation tools on business processes and the need for ongoing optimization.
Multistoried and Multi Bay Steel Building Frame by using Seismic DesignIRJET Journal
The document describes the seismic design of a G+5 steel building frame located in Roorkee, India according to Indian codes IS 1893-2002 and IS 800. The frame was analyzed using the equivalent static load method and response spectrum method, and its response in terms of displacements and shear forces were compared. Based on the analysis, the frame was designed as a seismic-resistant steel structure according to IS 800:2007. The software STAAD Pro was used for the analysis and design.
Cost Optimization of Construction Using Plastic Waste as a Sustainable Constr...IRJET Journal
This research paper explores using plastic waste as a sustainable and cost-effective construction material. The study focuses on manufacturing pavers and bricks using recycled plastic and partially replacing concrete with plastic alternatives. Initial results found that pavers and bricks made from recycled plastic demonstrate comparable strength and durability to traditional materials while providing environmental and cost benefits. Additionally, preliminary research indicates incorporating plastic waste as a partial concrete replacement significantly reduces construction costs without compromising structural integrity. The outcomes suggest adopting plastic waste in construction can address plastic pollution while optimizing costs, promoting more sustainable building practices.
Discover the latest insights on Data Driven Maintenance with our comprehensive webinar presentation. Learn about traditional maintenance challenges, the right approach to utilizing data, and the benefits of adopting a Data Driven Maintenance strategy. Explore real-world examples, industry best practices, and innovative solutions like FMECA and the D3M model. This presentation, led by expert Jules Oudmans, is essential for asset owners looking to optimize their maintenance processes and leverage digital technologies for improved efficiency and performance. Download now to stay ahead in the evolving maintenance landscape.
Applications of artificial Intelligence in Mechanical Engineering.pdfAtif Razi
Historically, mechanical engineering has relied heavily on human expertise and empirical methods to solve complex problems. With the introduction of computer-aided design (CAD) and finite element analysis (FEA), the field took its first steps towards digitization. These tools allowed engineers to simulate and analyze mechanical systems with greater accuracy and efficiency. However, the sheer volume of data generated by modern engineering systems and the increasing complexity of these systems have necessitated more advanced analytical tools, paving the way for AI.
AI offers the capability to process vast amounts of data, identify patterns, and make predictions with a level of speed and accuracy unattainable by traditional methods. This has profound implications for mechanical engineering, enabling more efficient design processes, predictive maintenance strategies, and optimized manufacturing operations. AI-driven tools can learn from historical data, adapt to new information, and continuously improve their performance, making them invaluable in tackling the multifaceted challenges of modern mechanical engineering.
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024Sinan KOZAK
Sinan from the Delivery Hero mobile infrastructure engineering team shares a deep dive into performance acceleration with Gradle build cache optimizations. Sinan shares their journey into solving complex build-cache problems that affect Gradle builds. By understanding the challenges and solutions found in our journey, we aim to demonstrate the possibilities for faster builds. The case study reveals how overlapping outputs and cache misconfigurations led to significant increases in build times, especially as the project scaled up with numerous modules using Paparazzi tests. The journey from diagnosing to defeating cache issues offers invaluable lessons on maintaining cache integrity without sacrificing functionality.
Rainfall intensity duration frequency curve statistical analysis and modeling...bijceesjournal
Using data from 41 years in Patna’ India’ the study’s goal is to analyze the trends of how often it rains on a weekly, seasonal, and annual basis (1981−2020). First, utilizing the intensity-duration-frequency (IDF) curve and the relationship by statistically analyzing rainfall’ the historical rainfall data set for Patna’ India’ during a 41 year period (1981−2020), was evaluated for its quality. Changes in the hydrologic cycle as a result of increased greenhouse gas emissions are expected to induce variations in the intensity, length, and frequency of precipitation events. One strategy to lessen vulnerability is to quantify probable changes and adapt to them. Techniques such as log-normal, normal, and Gumbel are used (EV-I). Distributions were created with durations of 1, 2, 3, 6, and 24 h and return times of 2, 5, 10, 25, and 100 years. There were also mathematical correlations discovered between rainfall and recurrence interval.
Findings: Based on findings, the Gumbel approach produced the highest intensity values, whereas the other approaches produced values that were close to each other. The data indicates that 461.9 mm of rain fell during the monsoon season’s 301st week. However, it was found that the 29th week had the greatest average rainfall, 92.6 mm. With 952.6 mm on average, the monsoon season saw the highest rainfall. Calculations revealed that the yearly rainfall averaged 1171.1 mm. Using Weibull’s method, the study was subsequently expanded to examine rainfall distribution at different recurrence intervals of 2, 5, 10, and 25 years. Rainfall and recurrence interval mathematical correlations were also developed. Further regression analysis revealed that short wave irrigation, wind direction, wind speed, pressure, relative humidity, and temperature all had a substantial influence on rainfall.
Originality and value: The results of the rainfall IDF curves can provide useful information to policymakers in making appropriate decisions in managing and minimizing floods in the study area.
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
An improved modulation technique suitable for a three level flying capacitor ...IJECEIAES
This research paper introduces an innovative modulation technique for controlling a 3-level flying capacitor multilevel inverter (FCMLI), aiming to streamline the modulation process in contrast to conventional methods. The proposed
simplified modulation technique paves the way for more straightforward and
efficient control of multilevel inverters, enabling their widespread adoption and
integration into modern power electronic systems. Through the amalgamation of
sinusoidal pulse width modulation (SPWM) with a high-frequency square wave
pulse, this controlling technique attains energy equilibrium across the coupling
capacitor. The modulation scheme incorporates a simplified switching pattern
and a decreased count of voltage references, thereby simplifying the control
algorithm.
Design and optimization of ion propulsion dronebjmsejournal
Electric propulsion technology is widely used in many kinds of vehicles in recent years, and aircrafts are no exception. Technically, UAVs are electrically propelled but tend to produce a significant amount of noise and vibrations. Ion propulsion technology for drones is a potential solution to this problem. Ion propulsion technology is proven to be feasible in the earth’s atmosphere. The study presented in this article shows the design of EHD thrusters and power supply for ion propulsion drones along with performance optimization of high-voltage power supply for endurance in earth’s atmosphere.