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.
Presentation in Vietnam Japan AI Community in 2019-05-26.
The presentation summarizes what I've learned about Regularization in Deep Learning.
Disclaimer: The presentation is given in a community event, so it wasn't thoroughly reviewed or revised.
Machine Learning With Logistic RegressionKnoldus Inc.
Machine learning is the subfield of computer science that gives computers the ability to learn without being programmed. Logistic Regression is a type of classification algorithm, based on linear regression to evaluate output and to minimize the error.
This document summarizes various optimization techniques for deep learning models, including gradient descent, stochastic gradient descent, and variants like momentum, Nesterov's accelerated gradient, AdaGrad, RMSProp, and Adam. It provides an overview of how each technique works and comparisons of their performance on image classification tasks using MNIST and CIFAR-10 datasets. The document concludes by encouraging attendees to try out the different optimization methods in Keras and provides resources for further deep learning topics.
The document discusses hyperparameters and hyperparameter tuning in deep learning models. It defines hyperparameters as parameters that govern how the model parameters (weights and biases) are determined during training, in contrast to model parameters which are learned from the training data. Important hyperparameters include the learning rate, number of layers and units, and activation functions. The goal of training is for the model to perform optimally on unseen test data. Model selection, such as through cross-validation, is used to select the optimal hyperparameters. Training, validation, and test sets are also discussed, with the validation set used for model selection and the test set providing an unbiased evaluation of the fully trained model.
The document discusses bagging, an ensemble machine learning method. Bagging (bootstrap aggregating) uses multiple models fitted on random subsets of a dataset to improve stability and accuracy compared to a single model. It works by training base models in parallel on random samples with replacement of the original dataset and aggregating their predictions. Key benefits are reduced variance, easier implementation through libraries like scikit-learn, and improved performance over single models. However, bagging results in less interpretable models compared to a single model.
Ensemble methods combine multiple machine learning models to obtain better predictive performance than from any individual model. There are two main types of ensemble methods: sequential (e.g AdaBoost) where models are generated one after the other, and parallel (e.g Random Forest) where models are generated independently. Popular ensemble methods include bagging, boosting, and stacking. Bagging averages predictions from models trained on random samples of the data, while boosting focuses on correcting previous models' errors. Stacking trains a meta-model on predictions from other models to produce a final prediction.
Talk on Optimization for Deep Learning, which gives an overview of gradient descent optimization algorithms and highlights some current research directions.
Data Analysis: Evaluation Metrics for Supervised Learning Models of Machine L...Md. Main Uddin Rony
This document discusses various machine learning evaluation metrics for supervised learning models. It covers classification, regression, and ranking metrics. For classification, it describes accuracy, confusion matrix, log-loss, and AUC. For regression, it discusses RMSE and quantiles of errors. For ranking, it explains precision-recall, precision-recall curves, F1 score, and NDCG. The document provides examples and visualizations to illustrate how these metrics are calculated and used to evaluate model performance.
Presentation in Vietnam Japan AI Community in 2019-05-26.
The presentation summarizes what I've learned about Regularization in Deep Learning.
Disclaimer: The presentation is given in a community event, so it wasn't thoroughly reviewed or revised.
Machine Learning With Logistic RegressionKnoldus Inc.
Machine learning is the subfield of computer science that gives computers the ability to learn without being programmed. Logistic Regression is a type of classification algorithm, based on linear regression to evaluate output and to minimize the error.
This document summarizes various optimization techniques for deep learning models, including gradient descent, stochastic gradient descent, and variants like momentum, Nesterov's accelerated gradient, AdaGrad, RMSProp, and Adam. It provides an overview of how each technique works and comparisons of their performance on image classification tasks using MNIST and CIFAR-10 datasets. The document concludes by encouraging attendees to try out the different optimization methods in Keras and provides resources for further deep learning topics.
The document discusses hyperparameters and hyperparameter tuning in deep learning models. It defines hyperparameters as parameters that govern how the model parameters (weights and biases) are determined during training, in contrast to model parameters which are learned from the training data. Important hyperparameters include the learning rate, number of layers and units, and activation functions. The goal of training is for the model to perform optimally on unseen test data. Model selection, such as through cross-validation, is used to select the optimal hyperparameters. Training, validation, and test sets are also discussed, with the validation set used for model selection and the test set providing an unbiased evaluation of the fully trained model.
The document discusses bagging, an ensemble machine learning method. Bagging (bootstrap aggregating) uses multiple models fitted on random subsets of a dataset to improve stability and accuracy compared to a single model. It works by training base models in parallel on random samples with replacement of the original dataset and aggregating their predictions. Key benefits are reduced variance, easier implementation through libraries like scikit-learn, and improved performance over single models. However, bagging results in less interpretable models compared to a single model.
Ensemble methods combine multiple machine learning models to obtain better predictive performance than from any individual model. There are two main types of ensemble methods: sequential (e.g AdaBoost) where models are generated one after the other, and parallel (e.g Random Forest) where models are generated independently. Popular ensemble methods include bagging, boosting, and stacking. Bagging averages predictions from models trained on random samples of the data, while boosting focuses on correcting previous models' errors. Stacking trains a meta-model on predictions from other models to produce a final prediction.
Talk on Optimization for Deep Learning, which gives an overview of gradient descent optimization algorithms and highlights some current research directions.
Data Analysis: Evaluation Metrics for Supervised Learning Models of Machine L...Md. Main Uddin Rony
This document discusses various machine learning evaluation metrics for supervised learning models. It covers classification, regression, and ranking metrics. For classification, it describes accuracy, confusion matrix, log-loss, and AUC. For regression, it discusses RMSE and quantiles of errors. For ranking, it explains precision-recall, precision-recall curves, F1 score, and NDCG. The document provides examples and visualizations to illustrate how these metrics are calculated and used to evaluate model performance.
Decision tree is a type of supervised learning algorithm (having a pre-defined target variable) that is mostly used in classification problems. It is a tree in which each branch node represents a choice between a number of alternatives, and each leaf node represents a decision.
Supervised and Unsupervised Learning In Machine Learning | Machine Learning T...Simplilearn
This document provides an overview of machine learning, including:
- Machine learning allows computers to learn from data without being explicitly programmed, through processes like analyzing data, training models on past data, and making predictions.
- The main types of machine learning are supervised learning, which uses labeled training data to predict outputs, and unsupervised learning, which finds patterns in unlabeled data.
- Common supervised learning tasks include classification (like spam filtering) and regression (like weather prediction). Unsupervised learning includes clustering, like customer segmentation, and association, like market basket analysis.
- Supervised and unsupervised learning are used in many areas like risk assessment, image classification, fraud detection, customer analytics, and more
Part 2 of the Deep Learning Fundamentals Series, this session discusses Tuning Training (including hyperparameters, overfitting/underfitting), Training Algorithms (including different learning rates, backpropagation), Optimization (including stochastic gradient descent, momentum, Nesterov Accelerated Gradient, RMSprop, Adaptive algorithms - Adam, Adadelta, etc.), and a primer on Convolutional Neural Networks. The demos included in these slides are running on Keras with TensorFlow backend on Databricks.
▸ Machine Learning / Deep Learning models require to set the value of many hyperparameters
▸ Common examples: regularization coefficients, dropout rate, or number of neurons per layer in a Neural Network
▸ Instead of relying on some "expert advice", this presentation shows how to automatically find optimal hyperparameters
▸ Exhaustive Search, Monte Carlo Search, Bayesian Optimization, and Evolutionary Algorithms are explained with concrete examples
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.
This document summarizes a machine learning workshop on feature selection. It discusses typical feature selection methods like single feature evaluation using metrics like mutual information and Gini indexing. It also covers subset selection techniques like sequential forward selection and sequential backward selection. Examples are provided showing how feature selection improves performance for logistic regression on large datasets with more features than samples. The document outlines the workshop agenda and provides details on when and why feature selection is important for machine learning models.
Logistic regression is a machine learning classification algorithm that predicts the probability of a categorical dependent variable. It models the probability of the dependent variable being in one of two possible categories, as a function of the independent variables. The model transforms the linear combination of the independent variables using the logistic sigmoid function to output a probability between 0 and 1. Logistic regression is optimized using maximum likelihood estimation to find the coefficients that maximize the probability of the observed outcomes in the training data. Like linear regression, it makes assumptions about the data being binary classified with no noise or highly correlated independent variables.
An overview of gradient descent optimization algorithms Hakky St
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.
Temporal-difference (TD) learning combines ideas from Monte Carlo and dynamic programming methods. It updates estimates based in part on other estimates, like dynamic programming, but uses sampling experiences to estimate expected returns, like Monte Carlo. TD learning is model-free, incremental, and can be applied to continuing tasks. The TD error is the difference between the target value and estimated value, which is used to update value estimates through methods like Sarsa and Q-learning. N-step TD and TD(λ) generalize the idea by incorporating returns and eligibility traces over multiple steps.
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.
Why should you care about Markov Chain Monte Carlo methods?
→ They are in the list of "Top 10 Algorithms of 20th Century"
→ They allow you to make inference with Bayesian Networks
→ They are used everywhere in Machine Learning and Statistics
Markov Chain Monte Carlo methods are a class of algorithms used to sample from complicated distributions. Typically, this is the case of posterior distributions in Bayesian Networks (Belief Networks).
These slides cover the following topics.
→ Motivation and Practical Examples (Bayesian Networks)
→ Basic Principles of MCMC
→ Gibbs Sampling
→ Metropolis–Hastings
→ Hamiltonian Monte Carlo
→ Reversible-Jump Markov Chain Monte Carlo
This is a deep learning presentation based on Deep Neural Network. It reviews the deep learning concept, related works and specific application areas.It describes a use case scenario of deep learning and highlights the current trends and research issues of deep learning
Supervised vs Unsupervised vs Reinforcement Learning | EdurekaEdureka!
YouTube: https://youtu.be/xtOg44r6dsE
(** Python Data Science Training: https://www.edureka.co/python **)
In this PPT on Supervised vs Unsupervised vs Reinforcement learning, we’ll be discussing the types of machine learning and we’ll differentiate them based on a few key parameters. The following topics are covered in this session:
1. Introduction to Machine Learning
2. Types of Machine Learning
3. Supervised vs Unsupervised vs Reinforcement learning
4. Use Cases
Python Training Playlist: https://goo.gl/Na1p9G
Python Blog Series: https://bit.ly/2RVzcVE
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
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The document discusses machine learning algorithms and provides descriptions of the top 10 algorithms. It begins by explaining the types of machine learning algorithms: supervised, unsupervised, and reinforcement learning. It then provides brief overviews of some of the most commonly used algorithms, including Naive Bayes, K-means clustering, support vector machines, Apriori, and others. For each algorithm, it gives a short description and links to additional resources.
Neural networks can be used for tasks like time-series forecasting, algorithmic trading, and credit risk modeling. They contain layers of interconnected nodes called perceptrons that are similar to multiple linear regression models. Optimization algorithms like gradient descent are used to minimize losses during neural network training by adjusting weights. Stochastic gradient descent makes updates using small random samples rather than the whole dataset, helping address issues with gradient descent like becoming stuck in local minima. Momentum can be added to gradient descent to help it build inertia and overcome flat spots during optimization. Adaptive learning methods like AdaGrad dynamically adjust the learning rate for each parameter. Fuzzy logic systems use degrees of membership rather than binary values, allowing approximate reasoning. They have components
https://github.com/telecombcn-dl/dlmm-2017-dcu
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.
Decision tree is a type of supervised learning algorithm (having a pre-defined target variable) that is mostly used in classification problems. It is a tree in which each branch node represents a choice between a number of alternatives, and each leaf node represents a decision.
Supervised and Unsupervised Learning In Machine Learning | Machine Learning T...Simplilearn
This document provides an overview of machine learning, including:
- Machine learning allows computers to learn from data without being explicitly programmed, through processes like analyzing data, training models on past data, and making predictions.
- The main types of machine learning are supervised learning, which uses labeled training data to predict outputs, and unsupervised learning, which finds patterns in unlabeled data.
- Common supervised learning tasks include classification (like spam filtering) and regression (like weather prediction). Unsupervised learning includes clustering, like customer segmentation, and association, like market basket analysis.
- Supervised and unsupervised learning are used in many areas like risk assessment, image classification, fraud detection, customer analytics, and more
Part 2 of the Deep Learning Fundamentals Series, this session discusses Tuning Training (including hyperparameters, overfitting/underfitting), Training Algorithms (including different learning rates, backpropagation), Optimization (including stochastic gradient descent, momentum, Nesterov Accelerated Gradient, RMSprop, Adaptive algorithms - Adam, Adadelta, etc.), and a primer on Convolutional Neural Networks. The demos included in these slides are running on Keras with TensorFlow backend on Databricks.
▸ Machine Learning / Deep Learning models require to set the value of many hyperparameters
▸ Common examples: regularization coefficients, dropout rate, or number of neurons per layer in a Neural Network
▸ Instead of relying on some "expert advice", this presentation shows how to automatically find optimal hyperparameters
▸ Exhaustive Search, Monte Carlo Search, Bayesian Optimization, and Evolutionary Algorithms are explained with concrete examples
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.
This document summarizes a machine learning workshop on feature selection. It discusses typical feature selection methods like single feature evaluation using metrics like mutual information and Gini indexing. It also covers subset selection techniques like sequential forward selection and sequential backward selection. Examples are provided showing how feature selection improves performance for logistic regression on large datasets with more features than samples. The document outlines the workshop agenda and provides details on when and why feature selection is important for machine learning models.
Logistic regression is a machine learning classification algorithm that predicts the probability of a categorical dependent variable. It models the probability of the dependent variable being in one of two possible categories, as a function of the independent variables. The model transforms the linear combination of the independent variables using the logistic sigmoid function to output a probability between 0 and 1. Logistic regression is optimized using maximum likelihood estimation to find the coefficients that maximize the probability of the observed outcomes in the training data. Like linear regression, it makes assumptions about the data being binary classified with no noise or highly correlated independent variables.
An overview of gradient descent optimization algorithms Hakky St
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.
Temporal-difference (TD) learning combines ideas from Monte Carlo and dynamic programming methods. It updates estimates based in part on other estimates, like dynamic programming, but uses sampling experiences to estimate expected returns, like Monte Carlo. TD learning is model-free, incremental, and can be applied to continuing tasks. The TD error is the difference between the target value and estimated value, which is used to update value estimates through methods like Sarsa and Q-learning. N-step TD and TD(λ) generalize the idea by incorporating returns and eligibility traces over multiple steps.
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.
Why should you care about Markov Chain Monte Carlo methods?
→ They are in the list of "Top 10 Algorithms of 20th Century"
→ They allow you to make inference with Bayesian Networks
→ They are used everywhere in Machine Learning and Statistics
Markov Chain Monte Carlo methods are a class of algorithms used to sample from complicated distributions. Typically, this is the case of posterior distributions in Bayesian Networks (Belief Networks).
These slides cover the following topics.
→ Motivation and Practical Examples (Bayesian Networks)
→ Basic Principles of MCMC
→ Gibbs Sampling
→ Metropolis–Hastings
→ Hamiltonian Monte Carlo
→ Reversible-Jump Markov Chain Monte Carlo
This is a deep learning presentation based on Deep Neural Network. It reviews the deep learning concept, related works and specific application areas.It describes a use case scenario of deep learning and highlights the current trends and research issues of deep learning
Supervised vs Unsupervised vs Reinforcement Learning | EdurekaEdureka!
YouTube: https://youtu.be/xtOg44r6dsE
(** Python Data Science Training: https://www.edureka.co/python **)
In this PPT on Supervised vs Unsupervised vs Reinforcement learning, we’ll be discussing the types of machine learning and we’ll differentiate them based on a few key parameters. The following topics are covered in this session:
1. Introduction to Machine Learning
2. Types of Machine Learning
3. Supervised vs Unsupervised vs Reinforcement learning
4. Use Cases
Python Training Playlist: https://goo.gl/Na1p9G
Python Blog Series: https://bit.ly/2RVzcVE
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
The document discusses machine learning algorithms and provides descriptions of the top 10 algorithms. It begins by explaining the types of machine learning algorithms: supervised, unsupervised, and reinforcement learning. It then provides brief overviews of some of the most commonly used algorithms, including Naive Bayes, K-means clustering, support vector machines, Apriori, and others. For each algorithm, it gives a short description and links to additional resources.
Neural networks can be used for tasks like time-series forecasting, algorithmic trading, and credit risk modeling. They contain layers of interconnected nodes called perceptrons that are similar to multiple linear regression models. Optimization algorithms like gradient descent are used to minimize losses during neural network training by adjusting weights. Stochastic gradient descent makes updates using small random samples rather than the whole dataset, helping address issues with gradient descent like becoming stuck in local minima. Momentum can be added to gradient descent to help it build inertia and overcome flat spots during optimization. Adaptive learning methods like AdaGrad dynamically adjust the learning rate for each parameter. Fuzzy logic systems use degrees of membership rather than binary values, allowing approximate reasoning. They have components
https://github.com/telecombcn-dl/dlmm-2017-dcu
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.
The document provides an overview of regression problems in machine learning. It discusses the different types of regression including simple linear regression, multiple linear regression, and polynomial regression. It explains concepts like error, metrics like R-squared, MAE, and MSE. It also covers model performance issues like underfitting and overfitting, and techniques to address them such as regularization, early stopping, gradient descent, and cross-validation. The goal is to help learners understand regression problems and how to develop and evaluate regression models.
Regression takes a group of random variables, thought to be predicting Y, and tries to find a mathematical relationship between them. This relationship is typically in the form of a straight line (linear regression) that best approximates all the individual data points.
A presentation about NGBoost (Natural Gradient Boosting) which I presented in the Information Theory and Probabilistic Programming course at the University of Oklahoma.
This document provides an overview of deep learning concepts including neural networks, supervised and unsupervised learning, and key terms. It explains that deep learning uses neural networks with many hidden layers to learn features directly from raw data. Supervised learning algorithms learn from labeled examples to perform classification or regression on unseen data. Unsupervised learning finds patterns in unlabeled data. Key terms defined include neurons, activation functions, loss functions, optimizers, epochs, batches, and hyperparameters.
Optimization is considered to be one of the pillars of statistical learning and also plays a major role in the design and development of intelligent systems such as search engines, recommender systems, and speech and image recognition software. Machine Learning is the study that gives the computers the ability to learn and also the ability to think without being explicitly programmed. A computer is said to learn from an experience with respect to a specified task and its performance related to that task. The machine learning algorithms are applied to the problems to reduce efforts. Machine learning algorithms are used for manipulating the data and predict the output for the new data with high precision and low uncertainty. The optimization algorithms are used to make rational decisions in an environment of uncertainty and imprecision. In this paper a methodology is presented to use the efficient optimization algorithm as an alternative for the gradient descent machine learning algorithm as an optimization algorithm.
The document provides guidelines for training deep neural networks (DNNs). It discusses obtaining large, clean training datasets and using data augmentation. It recommends tanh or ReLU activation functions to avoid problems with sigmoid functions. The number of hidden units and layers should be optimized, and weights initialized randomly. Learning rates can use adaptive methods like Adam. Hyperparameter tuning is best done with random search instead of grid search. Mini-batch training provides faster learning than stochastic methods. Dropout helps prevent overfitting.
The document discusses key concepts in neural networks including units, layers, batch normalization, cost/loss functions, regularization techniques, activation functions, backpropagation, learning rates, and optimization methods. It provides definitions and explanations of these concepts at a high level. For example, it defines units as the activation function that transforms inputs via a nonlinear function, and hidden layers as layers other than the input and output layers that receive weighted input and pass transformed values to the next layer. It also summarizes common cost functions, regularization approaches like dropout, and optimization methods like gradient descent and stochastic gradient descent.
Lecture 7 - Bias, Variance and Regularization, a lecture in subject module St...Maninda Edirisooriya
Bias and Variance are the deepest concepts in ML which drives the decision making of a ML project. Regularization is a solution for the high variance problem. This was one of the lectures of a full course I taught in University of Moratuwa, Sri Lanka on 2023 second half of the year.
Copy of CRICKET MATCH WIN PREDICTOR USING LOGISTIC ...PATHALAMRAJESH
This project uses logistic regression to build a cricket match win predictor. It analyzes match and ball-by-ball data to extract important features, performs exploratory data analysis to derive additional predictive features, and fits a logistic regression model to predict the winning probability of teams based on the game situation. The model achieves an accuracy of 86% on the test data. Future work includes predicting the winner based only on the first innings and adding a user interface to allow custom predictions.
L1 and L2 regularization are techniques to prevent overfitting in machine learning models. L1 regularization adds a penalty term to the loss function based on the absolute values of the model's parameters, encouraging sparsity. L2 regularization uses the squared values instead, which does not induce sparsity but helps prevent overfitting by keeping parameter values small. The degree of regularization is controlled by the hyperparameter lambda. L1 regularization is useful for feature selection with high-dimensional data, while L2 regularization produces simpler, more robust models.
The document provides an introduction to supervised learning. It discusses how supervised learning models are trained on labelled datasets containing both input data and corresponding results or labels. The model learns from these examples to predict accurate results for new, unseen data. Common applications of supervised learning mentioned include sentiment analysis, recommendations, and spam filtration. Decision trees and K-nearest neighbors are discussed as examples of supervised learning algorithms. Decision trees use a top-down approach to split the dataset into more homogeneous subsets. K-nearest neighbors classifies new data based on similarity to labelled examples in the training set.
A Framework for Scene Recognition Using Convolutional Neural Network as Featu...Tahmid Abtahi
This document presents a framework for scene recognition using convolutional neural networks (CNNs) as feature extractors and machine learning kernels as classifiers. The framework uses a VGG dataset containing 678 images across 3 categories (highway, open country, streets). CNNs perform feature extraction via convolution and max pooling operations to reduce dimensionality by 10x. The extracted features are then classified using perceptrons and support vector machines (SVMs) in a parallel implementation. Results show SVMs achieve higher accuracy than perceptrons and accuracy increases with more training data. Future work involves task-level parallelism, increasing data size and categories, and comparing CNN features to PCA.
Everything You Wanted to Know About Optimizationindico data
Presented by Madison May, co-founder and machine learning architect at indico, at the Boston ML meetup.
Overview:
In recent years the use of adaptive momentum methods like Adam and RMSProp has become popular in reducing the sensitivity of machine learning models to optimization hyperparameters and increasing the rate of convergence for complex models. However, past research has shown when properly tuned, using simple SGD + momentum produces better generalization properties and better validation losses at the later stages of training. In a wave of papers submitted in early 2018, researchers have suggested justifications for this unexpected behavior and proposed practical solutions to the problem. This talk will first provide a primer on optimization for machine learning, then summarize the results of these papers and propose practical approaches to applying these findings.
Random forest is an ensemble machine learning algorithm that combines multiple decision trees to improve predictive accuracy. It works by constructing many decision trees during training and outputting the class that is the mode of the classes of the individual trees. Random forest can be used for both classification and regression problems and provides high accuracy even with large datasets.
Deep learning involves core components like parameters, layers, activation functions, loss functions, and optimization methods. Loss functions measure how incorrect a model's predictions are and include types like squared error loss and cross-entropy loss. Squared error loss assesses the quality of a predictor or estimator by measuring the mean squared error. Hyperparameters like the learning rate, regularization, momentum, sparsity, and optimization method also impact deep learning models. The learning rate affects how much model parameters are adjusted with each iteration, and optimization methods like gradient descent, RMSprop, and Adam are used to update parameters to minimize the loss function.
The document discusses linear programming models and optimization techniques. It covers sensitivity analysis and duality analysis to determine parameter values where a linear programming solution remains valid. It also discusses solving linear programming problems with integer constraints and using network models to solve transportation problems. The document then provides an example of using the simplex method and sensitivity analysis to solve a linear programming problem to maximize profit based on production capacity constraints.
Paper review: Learned Optimizers that Scale and Generalize.Wuhyun Rico Shin
The paper proposes a novel hierarchical RNN architecture for a learned optimizer that aims to address scalability and generalization issues. The architecture uses a hierarchical structure of parameter, tensor, and global RNNs to enable coordination of updates across parameters with low memory and computation costs. It also incorporates features inspired by hand-designed optimizers like computing gradients at attended locations and dynamic input scaling to provide the learned optimizer with useful information. The optimizer is meta-trained on diverse small problems and can generalize to optimizing new problem types, though it struggles on very large models. Ablation studies show the importance of the paper's design choices for the learned optimizer's performance.
Similaire à Methods of Optimization in Machine Learning (20)
Terratest - Automation testing of infrastructureKnoldus Inc.
TerraTest is a testing framework specifically designed for testing infrastructure code written with HashiCorp's Terraform. It helps validate that your Terraform configurations create the desired infrastructure, and it can be used for both unit testing and integration testing.
Getting Started with Apache Spark (Scala)Knoldus Inc.
In this session, we are going to cover Apache Spark, the architecture of Apache Spark, Data Lineage, Direct Acyclic Graph(DAG), and many more concepts. Apache Spark is a multi-language engine for executing data engineering, data science, and machine learning on single-node machines or clusters.
Secure practices with dot net services.pptxKnoldus Inc.
Securing .NET services is paramount for protecting applications and data. Employing encryption, strong authentication, and adherence to best coding practices ensures resilience against potential threats, enhancing overall cybersecurity posture.
Distributed Cache with dot microservicesKnoldus Inc.
A distributed cache is a cache shared by multiple app servers, typically maintained as an external service to the app servers that access it. A distributed cache can improve the performance and scalability of an ASP.NET Core app, especially when the app is hosted by a cloud service or a server farm. Here we will look into implementation of Distributed Caching Strategy with Redis in Microservices Architecture focusing on cache synchronization, eviction policies, and cache consistency.
Introduction to gRPC Presentation (Java)Knoldus Inc.
gRPC, which stands for Remote Procedure Call, is an open-source framework developed by Google. It is designed for building efficient and scalable distributed systems. gRPC enables communication between client and server applications by defining a set of services and message types using Protocol Buffers (protobuf) as the interface definition language. gRPC provides a way for applications to call methods on a remote server as if they were local procedures, making it a powerful tool for building distributed and microservices-based architectures.
Using InfluxDB for real-time monitoring in JmeterKnoldus Inc.
Explore the integration of InfluxDB with JMeter for real-time performance monitoring. This session will cover setting up InfluxDB to capture JMeter metrics, configuring JMeter to send data to InfluxDB, and visualizing the results using Grafana. Learn how to leverage this powerful combination to gain real-time insights into your application's performance, enabling proactive issue detection and faster resolution.
Intoduction to KubeVela Presentation (DevOps)Knoldus Inc.
KubeVela is an open-source platform for modern application delivery and operation on Kubernetes. It is designed to simplify the deployment and management of applications in a Kubernetes environment. KubeVela is a modern software delivery platform that makes deploying and operating applications across today's hybrid, multi-cloud environments easier, faster and more reliable. KubeVela is infrastructure agnostic, programmable, yet most importantly, application-centric. It allows you to build powerful software, and deliver them anywhere!
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During the hour, we’ll take you through:
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5th LF Energy Power Grid Model Meet-up SlidesDanBrown980551
5th Power Grid Model Meet-up
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Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
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What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
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Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
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Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
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- Verstehen des DLAU-Tools und wie man es am besten nutzt
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- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
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Methods of Optimization in Machine Learning
1. Presented By: Aayush Srivastava
& Divyank Saxena
Methods of
Optimization in
Machine Learning
2. Lack of etiquette and manners is a huge turn off.
KnolX Etiquettes
Punctuality
Join the session 5 minutes prior to
the session start time. We start on
time and conclude on time!
Feedback
Make sure to submit a constructive
feedback for all sessions as it is
very helpful for the presenter.
Silent Mode
Keep your mobile devices in silent
mode, feel free to move out of
session in case you need to attend
an urgent call.
Avoid Disturbance
Avoid unwanted chit chat during
the session.
3. Our Agenda
01 What is Optimization in
Machine Learning
02 What is Gradient Descent
03
What is Minibatch Stochastic
Gradient
04
What is Adam optimization
05
Demo
05
06
What is Stochastic Gradient
Descent
4. .
What is Optimization in ML
● Optimization in Machine Learning is a technique used to find the best set of parameters for a given
model to minimize a loss function and improve its performance. It is an essential step in the training
process of a machine learning model.
● The goal of optimization is to find the best weights and biases for the model, so that it can make
accurate predictions.
● Optimization is used in machine learning because models typically have many parameters, and finding
the best values for those parameters can be a challenging task.
● With optimization techniques, the model can automatically search for the best parameters, rather than
relying on manual tuning by the user.
5. .
What is Cost Function
● A cost function is a function which measures the error between predictions and their actual values
across the whole dataset.
● Minimizing the cost function helps the learning algorithm find the optimal set of parameters, such as
weights and biases, that produce the best predictions.
● Cost function is a measure of how wrong the model is in estimating the relationship between X(input)
and Y(output) Parameter
- m is the number of samples
- Sum from i to m,
- The actual calculation is just the hypothesis value for h(x)
minus the actual value of y. Then you square whatever you get.
6. .
What is Cost Function
● Let’s run through the calculation for best_fit_1.
1.The hypothesis is 0.50. This is the h_the ha(x(i)) part
what we think is the correct value.
2.The actual value for the sample data is 1.00.
So we are left with (0.50 — 1.00)^2 , which is 0.25.
3.Let’s add this result to an array called results and do the same for all three points
4.Results = [0.25, 2.25, 4.00]
5.Finally, we add them all up and multiply by ⅙ .We get the cost for best_fit1 = 1.083
7. .
What is Cost Function
● COST: best_fit_1: 1.083
best_fit_2: 0.083
best_fit_3: 0.25
● A low costs represents a smaller difference.
8. .
What is Loss Function
● A loss function, also known objective function, is a mathematical measure of how well a model is able
to make predictions that match the true values.
● A loss function measures the error between a single prediction and the corresponding actual value.
● Loss and cost functions are methods of measuring the error in machine learning predictions. Loss
functions measure the error per observation, whilst cost functions measure the error over all
observations.
Types:
1.Mean Squared Error (MSE): This loss function measures the average squared difference between the
predicted values and the true values.
2.Mean Absolute Error (MAE): This loss function measures the average absolute difference between the
predicted values and the true values.
9. ● Gradient, in plain terms means slope or slant of a surface. So gradient descent literally means
descending a slope to reach the lowest point on that surface
● Gradient descent enables a model to learn the gradient or direction that the model should take in
order to reduce errors (differences between actual y and predicted y).
● This algorithm that tries to find a minimum of a function iteratively
What is Gradient Descent
10. .
What is Learning Rate
● Learning Rate:
The learning rate is a hyperparameter in machine learning that determines the step size at which the
optimization algorithm updates the model's parameters. It is used to control the speed at which the
model learns.
11. .
Limitation of Gradient Descent
● Some limitations and drawbacks that can affect its performance and efficiency.
● Local Minima: Gradient Descent can get stuck in a local minimum, which may not be the global
minimum, and therefore, the optimization will not produce the best result.
● Vanishing gradient: When training deep neural networks, the gradients can become very small,
leading to the vanishing gradient problem, which can slow down or prevent convergence.
12. ● Stochastic Gradient Descent (SGD) is a variant of Gradient Descent optimization algorithm, that is
used to update the parameters of a model in a more efficient and faster way.
● “Stochastic” in plain terms means “random”
● In SGD, at each step, the algorithm calculates the gradient for one observation picked at random,
instead of calculating the gradient for the entire dataset..
● So, let’s have a dataset that contains 1000 rows, and when we apply SGD it will update the model
parameters 1000 times in one complete cycle of a dataset instead of one time as in Gradient Descent.
What is Stochastic Gradient Descent
13. ● In the left diagram of the above picture, we have SGD (where 1 per step time) we take a Gradient
Descent step for each example and on the right diagram is GD(1 step per entire training set).
● This represents a significant performance improvement, when the dataset contains millions of
observations.
What is Stochastic Gradient Descent
14. Advantages of Stochastic Gradient Descent
● It is easier to fit into memory due to a single training sample being processed by the network
● For larger datasets it can converge faster as it causes updates to the parameters more frequently
● Due to frequent updates the steps taken towards the minima of the loss function have oscillations
which can help getting out of local minimums of the loss function
What is Stochastic Gradient Descent
15. ● So far we encountered two extremes in the approach to gradient-based learning:
● First Gradient Descent uses the full dataset to compute gradients and to update parameters, one
pass at a time. And Conversely, Stochastic Gradient Descent processes one training example at a
time to make progress. Either of them has its own drawbacks.
● Gradient descent is not particularly data efficient whenever data is very similar. Stochastic gradient
descent is not particularly computationally efficient since CPUs and GPUs cannot exploit the full
power of vectorization.
● This suggests that there might be something in between, and in fact, that is what we have been using
so far in the examples we discussed.
What is Minibatch Stochastic Gradient
16. ● Mini Batch Gradient Descent is considered to be the cross-over between GD and SGD. In this
approach instead of iterating through the entire dataset or one observation, we split the dataset into
small subsets (batches) and compute the gradients for each batch.
● Steps involved in Mini-batch stochastic gradient:
1. Pick a mini-batch
2. Feed it to Neural Network
3. Calculate the mean gradient of the mini-batch
4. Use the mean gradient we calculated in step 3 to update the weights
5. Repeat steps 1–4 for the mini-batches we created
What is Minibatch Stochastic Gradient
17. ● Minibatch stochastic gradient descent is able to trade-off convergence speed and computation
efficiency. A minibatch size of 10 is more efficient than stochastic gradient descent; a minibatch size
of 100 even outperforms GD in terms of runtime.
What is Minibatch Stochastic Gradient
18. Advantages of Mini-Batch Gradient Descent:
● Reduces variance of the parameter update and hence lead to stable convergence
● Speeds the learning
● Helpful to estimate the approximate location of the actual minimum
Disadvantages of Mini Batch Gradient Descent:
● Loss is computed for each mini batch and hence total loss needs to be accumulated across all mini
batches
Advantages and Disadvantages
19. The Adam optimization algorithm is an extension to stochastic gradient descent that has recently
seen broader adoption for deep learning applications in computer vision and natural language
processing.
The method is really efficient when working with large problem involving a lot of data or parameters.
Adam is an adaptive learning rate method, which means, it computes individual learning rates for
different parameters. Its name is derived from adaptive moment estimation
What is Adam Optimizer
20. The method computes individual adaptive learning rates for different parameters from estimates of
first and second moments of the gradients.
Adam optimizer involves a combination of two gradient descent methodologies:
1. Momentum:
This algorithm is used to accelerate the gradient descent algorithm by taking into consideration
the ‘exponentially weighted average’ of the gradients. Using averages makes the algorithm
converge towards the minima in a faster pace.
2. Root Mean Square Propagation (RMSP):
It maintains per-parameter learning rates that are adapted based on the average of recent
magnitudes of the gradients for the weight (e.g. how quickly it is changing). This means the
algorithm does well on online and non-stationary problems (e.g. noisy).
How Adam Optimizer Work
21. List of attractive benefits of using Adam, as follows:
● Straightforward to implement.
● Computationally efficient.
● Less memory requirements.
● Well suited for problems that are large in terms of data and/or parameters.
● Appropriate for problems with very noisy/or sparse gradients.
● Hyper-parameters have intuitive interpretation and typically require little tuning.
Benefits of Adam Optimizer