Pytorch Deep Learning By Example

by Benjamin Young

Summary Pytoch is a quite powerful, flexible and yet popular deep learning framework, but the learning curve could be steep if you do not have much deep learning background or are simply from keras background. This book will easy the pain and help you learn and grasp latest pytorch deep learning technology from ground zero with many interesting real world examples. It could also be used as a quick guide on how to use and understand deep learning in the real life. Description Artificial Intelligence (AI), Machine Learning especially Deep Learning has made tremendous progress in recent years. It starts to spread to all industries. Unless you are a refresh graduated student with AI/deep learning major, many of us do not have a formal machine learning/deep learning training before, so it is time to keep updated with latest technology. Pytoch is a quite powerful, flexible and yet popular deep learning framework, but the learning curve could be steep if you do not have much deep learning background. This book will easy the pain and help you learn and grasp latest pytorch deep learning technology from ground zero with many interesting real world examples. It covers many state-of-art deep learning technologies, e.g. : Convoluational neural network (CNN), Recurrent neural network (RNN), Seq2Seq model, word emedding, Connectionist temporal calssification (CTC ) , Auto-encoder, Dynamic Memrory Network (DMN), Deep-Q-learning(DQN/DDQN), Monte Carlo Tree search (MCTS), Alphago/Alphazero etc. This book could also be used as a quick guide on how to use and understand deep learning in the real life. Readers should have basic knowledge of python, scripting etc, and can bear with imperfect English from the author. Any constructive feedback is welcome. Free lifetime upgrade for later editions ( as an electronic copy ). Please contact author for this. Table of Contents a keras/tensorflow version of this book Deep Learning with Keras from Scratch could be bought at

387 pages, Paperback

Published April 30, 2019