How to learn PyTorch at its best?

Learn PyTorch for implementing cutting-edge deep learning algorithms.
Train your neural networks for higher speed and flexibility and learn how to implement them in various scenarios;
Cover various advanced neural network architecture such as ResNet, Inception, DenseNet and more with practical examples;

Who This Book Is For

This book is for machine learning engineers, data analysts, data scientists interested in deep learning and are looking to explore implementing advanced algorithms in PyTorch. Some knowledge of machine learning is helpful but not a mandatory need. Working knowledge of Python programming is expected.

What You Will Learn

Use PyTorch for GPU-accelerated tensor computations
Build custom datasets and data loaders for images and test the models using torch vision and torch text
Build an image classifier by implementing CNN architectures using PyTorch
Build systems that do text classification and language modeling using RNN, LSTM, and GRU
Learn advanced CNN architectures such as ResNet, Inception, Densenet, and learn how to use them for transfer learning
Learn how to mix multiple models for a powerful ensemble model
Generate new images using GAN's and generate artistic images using style transfer

PyTorch Deep Learning Hands-On: Build CNNs, RNNs, GANs, reinforcement learning, and more, quickly and easily Kindle Editionby Sherin Thomas

Hands-on projects cover all the key deep learning methods built step-by-step in PyTorch

Key Features

Internals and principles of PyTorch
Implement key deep learning methods in PyTorch: CNNs, GANs, RNNs, reinforcement learning, and more
Build deep learning workflows and take deep learning models from prototyping to production

What you will learn

Use PyTorch to build:

Simple Neural Networks - build neural networks the PyTorch way, with high-level functions, optimizers, and more

Convolutional Neural Networks - create advanced computer vision systems

Recurrent Neural Networks - work with sequential data such as natural language and audio

Generative Adversarial Networks - create new content with models including SimpleGAN and CycleGAN

Reinforcement Learning - develop systems that can solve complex problems such as driving or game playing

Deep Learning workflows - move effectively from ideation to production with proper deep learning workflow using PyTorch and its utility packages

Production-ready models - package your models for high-performance production environments

Concepts and Programming in PyTorch Paperback- 2018

by Chitra Vasudevan

The book has been written in such a way that the concepts are explained in detail, giving adequate emphasis on examples. To make clarity of the programming examples, logic is explained properly as well as discussed by using comments in the program itself. The book covers the topics right from the start of the software by using coding in software and writing programs into it. The book features more on practical approach with more examples covering topics from simple to complex ones addressing many of the core concepts and advanced topics also.

Highly self-explanatory and user-friendly approach this book will "need to have" title for various reasons as articulated below. Gaining customers by adopting and implementing PyTorch in/ projects/ programs and in research departments. Help in sustaining customer relationships as the core of all successful working relationships are two essential characteristics: trust and commitment. To demonstrate their trustworthiness and commitment to customers, progressive suppliers periodically provide evidence to customers of their accomplishments. Help in delivering "superior value and getting an equitable return" as an understanding value in business markets and doing business based on value delivered gives suppliers the means to get an equitable return for their efforts. The essence of customer value management is to deliver superior value and get an equitable return for it, both of which depend on the value of assessment. Contents introduction to PyTorch linear regression convolution neural network (CNN) recurrent neural networks (rnn) PyTorch datasets.

Source: HOB

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