PyTorch Artificial Intelligence Fundamentals

Mathew, Jibin

  • 出版商: Packt Publishing
  • 出版日期: 2020-02-28
  • 售價: $1,550
  • 貴賓價: 9.5$1,473
  • 語言: 英文
  • 頁數: 200
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 1838557040
  • ISBN-13: 9781838557041
  • 相關分類: DeepLearning人工智慧
  • 海外代購書籍(需單獨結帳)


Artificial Intelligence (AI) continues to grow in popularity and disrupt a wide range of domains, but it is a complex and daunting topic. In this book, you'll get to grips with building deep learning apps, and how you can use PyTorch for research and solving real-world problems.


This book uses a recipe-based approach, starting with the basics of tensor manipulation, before covering Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) in PyTorch. Once you are well-versed with these basic networks, you'll build a medical image classifier using deep learning. Next, you'll use TensorBoard for visualizations. You'll also delve into Generative Adversarial Networks (GANs) and Deep Reinforcement Learning (DRL) before finally deploying your models to production at scale. You'll discover solutions to common problems faced in machine learning, deep learning, and reinforcement learning. You'll learn to implement AI tasks and tackle real-world problems in computer vision, natural language processing (NLP), and other real-world domains.


By the end of this book, you'll have the foundations of the most important and widely used techniques in AI using the PyTorch framework.


Jibin Mathew is a senior data scientist and machine learning researcher who has worked in the AI domain for more than 7 years. He is a serial entrepreneur and has founded multiple AI start-ups. He has a strong software engineering background and understands the complete workflow, from research to scalable production deployment. He has built solutions in the fields of healthcare, environment, finance, industrial monitoring, and retail. He has been an adviser to various companies in their AI endeavors. He was the winner of Singularity University's Global Impact Challenge 2018 and has been part of various global platforms. He is an active contributor to the community and shares his knowledge by authoring content and through blog posts.


  1. Working with Tensors Using PyTorch
  2. Dealing with Neural Networks
  3. Convolutional Neural Networks for Computer Vision
  4. Recurrent neural networks for NLP
  5. Transfer Learning and TensorBoard
  6. Exploring Generative Adversarial Networks
  7. Deep Reinforcement Learning
  8. Productionizing AI models in PyTorch