000			03782cam a2200301zu 4500
001			88924189
003			FRCYB88924189
005			20250107235133.0
006			m o d
007			cr un
008			250108s2022 fr | o|||||0|0|||eng d
020			_a9781801819312
035			_aFRCYB88924189
040			_aFR-PaCSA _ben _c _erda
100	1		_aRaschka, Sebastian
245	0	1	_aMachine Learning with PyTorch and Scikit-Learn _c['Raschka, Sebastian', 'Liu, Yuxi (Hayden)', 'Mirjalili, Vahid']
264		1	_bPackt Publishing _c2022
300			_a p.
336			_btxt _2rdacontent
337			_bc _2rdamdedia
338			_bc _2rdacarrier
650		0	_a
700	0		_aRaschka, Sebastian
700	0		_aLiu, Yuxi (Hayden)
700	0		_aMirjalili, Vahid
856	4	0	_2Cyberlibris _uhttps://international.scholarvox.com/netsen/book/88924189 _qtext/html _a
520			_aThis 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 frameworkKey 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 you'll 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 you're 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 know some Python and you want to use machine learning and deep learning, pick up this book. Whether you want to start from scratch or extend your machine learning knowledge, this is an essential resource.Written for developers and data scientists who want to create practical machine learning with Python and PyTorch deep learning code. This Python book is ideal for anyone who wants to teach computers how to learn from data.Working knowledge of the Python programming language, along with a good understanding of calculus and linear algebra is a must.
999			_c74735 _d74735