Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and dee

Description: Machine Learning with PyTorch and Scikit-Learn by Sebastian Raschka, Yuxi Liu, Vahid Mirjalili, Dmytro Dzhulgakov Packed with clear explanations, visualizations, and working examples, the book covers essential machine learning techniques in depth, along with two cutting-edge machine learning techniques: transformers and graph neural networks. FORMAT Paperback LANGUAGE English CONDITION Brand New Publisher Description This book of the bestselling and widely acclaimed Python Machine Learning series is a comprehensive guide to machine and deep learning using PyTorch s simple to code framework.Purchase of the print or Kindle book includes a free eBook in PDF format.Key FeaturesLearn applied machine learning with a solid foundation in theoryClear, intuitive explanations take you deep into the theory and practice of Python machine learningFully updated and expanded to cover PyTorch, transformers, XGBoost, graph neural networks, and best practicesBook DescriptionMachine Learning with PyTorch and Scikit-Learn is a comprehensive guide to machine learning and deep learning with PyTorch. It acts as both a step-by-step tutorial and a reference youll keep coming back to as you build your machine learning systems.Packed with clear explanations, visualizations, and examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, we teach the principles allowing you to build models and applications for yourself.Why PyTorch?PyTorch is the Pythonic way to learn machine learning, making it easier to learn and simpler to code with. This book explains the essential parts of PyTorch and how to create models using popular libraries, such as PyTorch Lightning and PyTorch Geometric.You will also learn about generative adversarial networks (GANs) for generating new data and training intelligent agents with reinforcement learning. Finally, this new edition is expanded to cover the latest trends in deep learning, including graph neural networks and large-scale transformers used for natural language processing (NLP).This PyTorch book is your companion to machine learning with Python, whether youre a Python developer new to machine learning or want to deepen your knowledge of the latest developments.What you will learnExplore frameworks, models, and techniques for machines to learn from dataUse scikit-learn for machine learning and PyTorch for deep learningTrain machine learning classifiers on images, text, and moreBuild and train neural networks, transformers, and boosting algorithmsDiscover best practices for evaluating and tuning modelsPredict continuous target outcomes using regression analysisDig deeper into textual and social media data using sentiment analysisWho this book is forIf you have a good grasp of Python basics and want to start learning about machine learning and deep learning, then this is the book for you. This is an essential resource written for developers and data scientists who want to create practical machine learning and deep learning applications using scikit-learn and PyTorch.Before you get started with this book, youll need a good understanding of calculus, as well as linear algebra. Author Biography Sebastian Raschka is an Assistant Professor of Statistics at the University of Wisconsin-Madison focusing on machine learning and deep learning research. As Lead AI Educator at Grid AI, Sebastian plans to continue following his passion for helping people get into machine learning and artificial intelligence. Yuxi (Hayden) Liu was a Machine Learning Software Engineer at Google. With a wealth of experience from his tenure as a machine learning scientist, he has applied his expertise across data-driven domains and applied his ML expertise in computational advertising, cybersecurity, and information retrieval.He is the author of a series of influential machine learning books and an education enthusiast. His debut book, also the first edition of Python Machine Learning by Example, ranked the #1 bestseller in Amazon and has been translated into many different languages. Vahid Mirjalili is a deep learning researcher focusing on CV applications. Vahid received a Ph.D. degree in both Mechanical Engineering and Computer Science from Michigan State University. Table of Contents Table of ContentsGiving Computers the Ability to Learn from DataTraining Simple Machine Learning Algorithms for ClassificationA Tour of Machine Learning Classifiers Using Scikit-LearnBuilding Good Training Datasets – Data PreprocessingCompressing Data via Dimensionality ReductionLearning Best Practices for Model Evaluation and Hyperparameter TuningCombining Different Models for Ensemble LearningApplying Machine Learning to Sentiment AnalysisPredicting Continuous Target Variables with Regression AnalysisWorking with Unlabeled Data – Clustering AnalysisImplementing a Multilayer Artificial Neural Network from ScratchParallelizing Neural Network Training with PyTorchGoing Deeper – The Mechanics of PyTorchClassifying Images with Deep Convolutional Neural NetworksModeling Sequential Data Using Recurrent Neural NetworksTransformers – Improving Natural Language Processing with Attention MechanismsGenerative Adversarial Networks for Synthesizing New DataGraph Neural Networks for Capturing Dependencies in Graph Structured Data Reinforcement Learning for Decision Making in Complex Environments Review "Im confident that you will find this book invaluable both as a broad overview of the exciting field of machine learning and as a treasure of practical insights. I hope it inspires you to apply machine learning for the greater good in your problem area, whatever it might be."--Dmytro Dzhulgakov, PyTorch Core Maintainer"This 700-page book covers most of todays widely used machine learning algorithms, and will be especially useful to anybody who wants to understand modern machine learning through examples of working code. It covers a variety of approaches, from basic algorithms such as logistic regression to very recent topics in deep learning such as BERT and GPT language models and generative adversarial networks. The book provides examples of nearly every algorithm it discusses in the convenient form of downloadable Jupyter notebooks that provide both code and access to datasets. Importantly, the book also provides clear instructions on how to download and start using state-of-the-art software packages that take advantage of GPU processors, including PyTorch and Google Colab."--Tom Mitchell, Professor CMU, Founder of CMUs Machine Learning Department Details ISBN1801819319 Author Dmytro Dzhulgakov Short Title Machine Learning with Pytorch and Scikit-Learn Language English Year 2022 ISBN-10 1801819319 ISBN-13 9781801819312 Format Paperback Publisher Packt Publishing Limited Imprint Packt Publishing Limited Place of Publication Birmingham Country of Publication United Kingdom Subtitle Develop machine learning and deep learning models with Python Pages 774 AU Release Date 2022-12-23 NZ Release Date 2022-12-23 DEWEY 006.31 Audience Professional & Vocational Publication Date 2022-02-25 UK Release Date 2022-02-25 We've got this At The Nile, if you're looking for it, we've got it. 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