Mastering PyTorch: Build powerful neural network architectures using advanced PyTorch 1.x features

Jha, Ashish Ranjan

  • 出版商: Packt Publishing
  • 出版日期: 2021-02-12
  • 售價: $1,320
  • 貴賓價: 9.5$1,254
  • 語言: 英文
  • 頁數: 450
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 1789614384
  • ISBN-13: 9781789614381
  • 相關分類: DeepLearning 深度學習

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Master advanced techniques and algorithms for deep learning with PyTorch using real-world examples

Key Features

  • Understand how to use PyTorch 1.x to build advanced neural network models
  • Learn to perform a wide range of tasks by implementing deep learning algorithms and techniques
  • Gain expertise in domains such as computer vision, NLP, Deep RL, Explainable AI, and much more

Book Description

Deep learning is driving the AI revolution, and PyTorch is making it easier than ever before for anyone to build deep learning applications. This PyTorch book will help you uncover expert techniques to get the most out of your data and build complex neural network models.

The book starts with a quick overview of PyTorch and explores using convolutional neural network (CNN) architectures for image classification. You'll then work with recurrent neural network (RNN) architectures and transformers for sentiment analysis. As you advance, you'll apply deep learning across different domains, such as music, text, and image generation using generative models and explore the world of generative adversarial networks (GANs). You'll not only build and train your own deep reinforcement learning models in PyTorch but also deploy PyTorch models to production using expert tips and techniques. Finally, you'll get to grips with training large models efficiently in a distributed manner, searching neural architectures effectively with AutoML, and rapidly prototyping models using PyTorch and

By the end of this PyTorch book, you'll be able to perform complex deep learning tasks using PyTorch to build smart artificial intelligence models.

What you will learn

  • Implement text and music generating models using PyTorch
  • Build a deep Q-network (DQN) model in PyTorch
  • Export universal PyTorch models using Open Neural Network Exchange (ONNX)
  • Become well-versed with rapid prototyping using PyTorch with
  • Perform neural architecture search effectively using AutoML
  • Easily interpret machine learning (ML) models written in PyTorch using Captum
  • Design ResNets, LSTMs, Transformers, and more using PyTorch
  • Find out how to use PyTorch for distributed training using the torch.distributed API

Who this book is for

This book is for data scientists, machine learning researchers, and deep learning practitioners looking to implement advanced deep learning paradigms using PyTorch 1.x. Working knowledge of deep learning with Python programming is required.


Ashish Ranjan Jha received his bachelor's degree in electrical engineering from IIT Roorkee (India), his master's degree in computer science from EPFL (Switzerland), and an MBA degree from the Quantic School of Business (Washington). He received distinctions in all of his degrees. He has worked for a variety of tech companies, including Oracle and Sony, and tech start ups, such as Revolut, as a machine learning engineer.

Aside from his years of work experience, Ashish is a freelance ML consultant, an author, and a blogger (datashines). He has worked on products/projects ranging from using sensor data for predicting vehicle types to detecting fraud in insurance claims. In his spare time, Ashish works on open source ML projects and is active on StackOverflow and kaggle (arj7192).


  1. Overview of Deep Learning Using PyTorch
  2. Combining CNNs and LSTMs
  3. Deep CNN Architectures
  4. Deep Recurrent Model Architectures
  5. Hybrid Advanced Models
  6. Music and Text Generation with PyTorch
  7. Neural Style Transfer
  8. Deep Convolutional GANs
  9. Deep Reinforcement Learning
  10. Operationalizing Pytorch Models into Production
  11. Distributed Training
  12. PyTorch and AutoML
  13. PyTorch and Explainable AI
  14. Rapid Prototyping with PyTorch