内容简介

Deep Learning with PyTorch teaches you how to implement deep learning algorithms with Python and PyTorch. This book takes you into a fascinating case study: building an algorithm capable of detecting malignant lung tumors using CT scans. As the authors guide you through this real example, you'll discover just how effective and fun PyTorch can be. After a quick introduction to the deep learning landscape, you'll explore the use of pre-trained networks and start sharpening your skills on working with tensors. You'll find out how to represent the most common types of data with tensors and how to build and train neural networks from scratch on practical examples, focusing on images and sequences.

After covering the basics, the book will take you on a journey through larger projects. The centerpiece of the book is a neural network designed for cancer detection. You'll discover ways for training networks with limited inputs and start processing data to get some results. You'll sift through the unreliable initial results and focus on how to diagnose and fix the problems in your neural network. Finally, you'll look at ways to improve your results by training with augmented data, make improvements to the model architecture, and perform other fine tuning.

what's inside

Using the PyTorch tensor API

Understanding automatic differentiation in PyTorch

Training deep neural networks

Monitoring training and visualizing results

Implementing modules and loss functions

Loading data in Python for PyTorch

Interoperability with NumPy

Deploying a PyTorch model for inference

作者简介

Eli Stevens has worked in Silicon Valley for the past 15 years as a software engineer, and the past 7 years as Chief Technical Officer of a startup making medical device software.

Luca Antiga is co-founder and CEO of an AI engineering company located in Bergamo, Italy, and a regular contributor to PyTorch.

精彩摘录

我们写本书的目的是为大家介绍PyTorch深度学习的基础知识，并以一个实际项目来展示。我们力图介绍深度学习底层的核心思想，并向读者展示PyTorch如何将其实现。在本书中，我们试图提供直观印象以帮助大家进一步探索，同时，我们选择性地深入细节，以解剖其背后的奥妙。本书并不是一本参考书，相反，它是一本概念性的指南，旨在引导你在网上独立探索更高级的材料。因此，我们关注的是PyTorch提供的一部分特性，最值得注意的是循环神经网络，但PyTorchAPI的其他部分也同样值得重视。本书适用于那些已成为或打算成为深度学习实践者以及想了解PyTorch的开发人员。我们假设本书的读者是一些计算机科学家、数据科学家、软件工程师、大学生或以后会学习相关课程的学生。由于我们并不要求读者有深度学习的先验知识，因此本书前半部分的某些内容可能对有经验的实践者来说是一些已经了解的概念，对这些读者来说，我们希望本书能够提供一个已知主题稍有不同的视角。我们希望读者具备命令式编程和面向对象编程的基本知识。由于本书使用的编程语言是Python，因此大家需要熟悉Python的语法和操作环境，了解如何在所选择的平台上安装Python包和运行脚本。熟悉C++、Java、JavaScript、Ruby或其他类似语言的读者应该可以轻松上手，但是需要在本书之外做一些补充。同样，如果读者熟悉NumPy也很有用，但这并不是强制要求的。我们也希望读者熟悉线性代数的一些基础知识，如知道什么是矩阵、向量和点积。本书的组织结构：路线图本书由3个部分组成。第1部分介绍基础知识；第2部分在第1部分的基础上介绍一个端到端的项目，并增加更高级的概念；简短的第3部分以PyTorch部署之旅结束本书，大家可能会注意到各部分的写作风格和图片风格不同。尽管本书是无数小时的协同计划，讨论和编辑的结果，但写作和绘图的工作被分成了几部...

——引自章节：第1部分　PyTorch核心