Deep Learning with Python: Learn Best Practices of Deep Learning Models with PyT

Description: Deep Learning with Python by Nikhil Ketkar, Jojo Moolayil This new edition focuses on the practical aspects of implementing deep learning solutions with PyTorch, a platform developed by Facebooks Artificial Intelligence Research Group with a hands-on approach to understanding both theory and practice. FORMAT Paperback LANGUAGE English CONDITION Brand New Publisher Description Master the practical aspects of implementing deep learning solutions with PyTorch, using a hands-on approach to understanding both theory and practice. This updated edition will prepare you for applying deep learning to real world problems with a sound theoretical foundation and practical know-how with PyTorch, a platform developed by Facebooks Artificial Intelligence Research Group.Youll start with a perspective on how and why deep learning with PyTorch has emerged as an path-breaking framework with a set of tools and techniques to solve real-world problems. Next, the book will ground you with the mathematical fundamentals of linear algebra, vector calculus, probability and optimization. Having established this foundation, youll move on to key components and functionality of PyTorch including layers, loss functions and optimization algorithms. Youll also gain an understanding of Graphical Processing Unit (GPU) based computation, which is essential for training deep learning models. All the key architectures in deep learning are covered, including feedforward networks, convolution neural networks, recurrent neural networks, long short-term memory networks, autoencoders and generative adversarial networks. Backed by a number of tricks of the trade for training and optimizing deep learning models, this edition of Deep Learning with Python explains the best practices in taking these models to production with PyTorch.What Youll LearnReview machine learning fundamentals such as overfitting, underfitting, and regularization.Understand deep learning fundamentals such as feed-forward networks, convolution neural networks, recurrent neural networks, automatic differentiation, and stochastic gradient descent.Apply in-depth linear algebra with PyTorchExplore PyTorch fundamentals andits building blocksWork with tuning and optimizing models Who This Book Is ForBeginners with a working knowledge of Python who want to understand Deep Learning in a practical, hands-on manner. Back Cover Master the practical aspects of implementing deep learning solutions with PyTorch, using a hands-on approach to understanding both theory and practice. This updated edition will prepare you for applying deep learning to real world problems with a sound theoretical foundation and practical know-how with PyTorch, a platform developed by Facebooks Artificial Intelligence Research Group. Youll start with a perspective on how and why deep learning with PyTorch has emerged as an path-breaking framework with a set of tools and techniques to solve real-world problems. Next, the book will ground you with the mathematical fundamentals of linear algebra, vector calculus, probability and optimization. Having established this foundation, youll move on to key components and functionality of PyTorch including layers, loss functions and optimization algorithms. Youll also gain an understanding of Graphical Processing Unit (GPU) based computation, which is essential for training deep learning models. All the key architectures in deep learning are covered, including feedforward networks, convolution neural networks, recurrent neural networks, long short-term memory networks, autoencoders and generative adversarial networks. Backed by a number of tricks of the trade for training and optimizing deep learning models, this edition of Deep Learning with Python explains the best practices in taking these models to production with PyTorch. You will: Review machine learning fundamentals such as overfitting, underfitting, and regularization. Understand deep learning fundamentals such as feed-forward networks, convolution neural networks, recurrent neural networks, automatic differentiation, and stochastic gradient descent. Apply in-depth linear algebra with PyTorch Explore PyTorch fundamentals and its building blocks Work with tuning and optimizing models Author Biography Nikhil S. Ketkar currently leads the Machine Learning Platform team at Flipkart, Indias largest e-commerce company. He received his Ph.D. from Washington State University. Following that he conducted postdoctoral research at University of North Carolina at Charlotte, which was followed by a brief stint in high frequency trading at Transmaket in Chicago. More recently he led the data mining team in Guavus, a startup doing big data analytics in the telecom domain and Indix, a startup doing data science in the e-commerce domain. His research interests include machine learning and graph theory.Jojo Moolayil is an artificial intelligence, deep learning, machine learning, and decision science professional with over five years of industrial experience and is a published author of the book Smarter Decisions – The Intersection of IoT and Decision Science. He has worked with several industry leaders on high-impact and critical data science and machine learning projects across multiple verticals. He is currently associated with Amazon Web Services as a research scientist. He was born and raised in Pune, India and graduated from the University of Pune with a major in Information Technology Engineering. He started his career with Mu Sigma Inc., the worlds largest pure-play analytics provider and worked with the leaders of many Fortune 50 clients. He later worked with Flutura – an IoT analytics startup and GE. He currently resides in Vancouver, BC. Apart from writing books on decision science and IoT, Jojo has also been a technical reviewer for various books on machine learning, deep learning and business analytics with Apress and Packt publications. He is an active data science tutor and maintains a blog at Table of Contents Chapter 1 – Introduction Deep Learning.- Chapter 2 – Introduction to PyTorch.- Chapter 3- Feed Forward Networks.- Chapter 4 – Automatic Differentiation in Deep Learning.- Chapter 5 – Training Deep Neural Networks.- Chapter 6 – Convolutional Neural Networks.- Chapter 7 – Recurrent Neural Networks.- Chapter 8 – Recent advances in Deep Learning. Feature Offers a sound theoretical/mathematical foundation and practical programming techniques using PyTorch Covers deep learning with multiple GPUs and optimizing deep learning models Reviews best practices of taking deep learning models to production with PyTorch Details ISBN1484253639 Publisher APress ISBN-10 1484253639 ISBN-13 9781484253632 Format Paperback Imprint APress Place of Publication Berkley Country of Publication United States Subtitle Learn Best Practices of Deep Learning Models with PyTorch Short Title Practical Deep Learning with Pytorch Language English DOI 10.1007/978-1-4842-5364-9 Year 2021 Edition 2nd Pages 306 Publication Date 2021-04-10 AU Release Date 2021-04-10 NZ Release Date 2021-04-10 US Release Date 2021-04-10 UK Release Date 2021-04-10 Author Jojo Moolayil Illustrations 82 Illustrations, black and white; XVII, 306 p. 82 illus. Edition Description 2nd ed. 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