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Pytorch Deep Learning by Example (2nd Edition): Grasp deep Learning from scratch like AlphaGo Zero within 40 days
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. Any constructive feedback is welcome.
Free lifetime upgrade for later editions ( as an electronic copy ). Readers who bought the 1st Edition book, please contact author for a free electrical copy.
Table of Contents
Introduction
What is deep learning
Deep neural network basic concepts
Deep learning development environments
Python and Tensor basic
Pytorch deep learning basic
MNIST CNN example: A deep dive of how to handle image data
Pre-trained model, transfer learning and fine-tuning
Recurrent neural network - how to handle sequences data
Natural Langauge Processing
Optical character recognition
Audio processing, speech processing
Autoencoder network
Deep reinforcement learning
Learning from scratch (self-play) AlphaZero
How to deploy deep learning model.
Note:
a keras/tensorflow version of this book Deep Learning with Keras from Scratch could be bought at https: //www.amazon.com/Learning-Keras-Scratch...
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. Any constructive feedback is welcome.
Free lifetime upgrade for later editions ( as an electronic copy ). Readers who bought the 1st Edition book, please contact author for a free electrical copy.
Table of Contents
Introduction
What is deep learning
Deep neural network basic concepts
Deep learning development environments
Python and Tensor basic
Pytorch deep learning basic
MNIST CNN example: A deep dive of how to handle image data
Pre-trained model, transfer learning and fine-tuning
Recurrent neural network - how to handle sequences data
Natural Langauge Processing
Optical character recognition
Audio processing, speech processing
Autoencoder network
Deep reinforcement learning
Learning from scratch (self-play) AlphaZero
How to deploy deep learning model.
Note:
a keras/tensorflow version of this book Deep Learning with Keras from Scratch could be bought at https: //www.amazon.com/Learning-Keras-Scratch...
412 pages, Paperback
Published August 17, 2019
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