Deep Learning with PyTorch Step-by-Step: A Beginner’s Guide

Book Description

UPDATE (July, 19th, 2022): The Spanish version of Part I, Fundamentals, was published today:https://leanpub.com/pytorch_ES

UPDATE (February 23rd, 2022): The paperback edition is available now (the book had to be split into 3

volumes for printing). For more details, please check pytorchstepbystep.com.

UPDATE (February 13th, 2022): The latest revised edition (v1.1.1) was published today to address small changes to Chapters 9 and 10 that weren’t included in the previous revision.

UPDATE (January 23rd, 2022): The revised edition (v1.1) was published today – better graphics, improved formatting, larger page size (thus reducing page count from 1187 to 1045 pages – no content was removed!). If you already bought the book, you can download the new version at any time!

If you’re looking for a book where you can learn about Deep Learning and PyTorch without having to spend hours deciphering cryptic text and code, and that’s easy and enjoyable to read, this is it 🙂

The book covers from the basics of gradient descent all the way up to fine-tuning large NLP models (BERT and GPT-2) using HuggingFace. It is divided into four parts

Part I: Fundamentals (gradient descent, training linear and logistic regressions in PyTorch)
Part II: Computer Vision (deeper models and activation functions, convolutions, transfer learning, initialization schemes)
Part III: Sequences (RNN, GRU, LSTM, seq2seq models, attention, self-attention, transformers)
Part IV: Natural Language Processing (tokenization, embeddings, contextual word embeddings, ELMo, BERT, GPT-2)
This is not a typical book: most tutorials start with some nice and pretty image classification problem to illustrate how to use PyTorch. It may seem cool, but I believe it distracts you from the main goal: how PyTorch works? In this book, I present a structured, incremental, and from first principles approach to learn PyTorch (and get to the pretty image classification problem in due time).

Moreover, this is not a formal book in any way: I am writing this book as if I were having a conversation with you, the reader. I will ask you questions (and give you answers shortly afterward) and I will also make (silly) jokes.

My job here is to make you understand the topic, so I will avoid fancy mathematical notation as much as possible and spell it out in plain English.

In this book, I will guide you through the development of many models in PyTorch, showing you why PyTorch makes it much easier and more intuitive to build models in Python: autograd, dynamic computation graph, model classes and much, much more.

We will build, step-by-step, not only the models themselves but also your understanding as I show you both the reasoning behind the code and how to avoid some common pitfalls and errors along the way.

I wrote this book for beginners in general – not only PyTorch beginners. Every now and then I will spend some time explaining some fundamental concepts which I believe are key to have a proper understanding of what’s going on in the code.

Maybe you already know well some of those concepts: if this is the case, you can simply skip them, since I’ve made those explanations as independent as possible from the rest of the content.

中文：

书名：与PyTorch逐步深入学习: 初学者指南

更新 (7月，19日，2022): 第一部分，基础知识的西班牙语版本今天发布: https://leanpub.com/pytorch_ES

更新 (2022年2月23日): 平装本现已上市 (这本书必须分成3本

用于打印的卷)。有关更多详细信息，请检查k pytorchstepbystep.com。

更新 (2022年2月13日): 今天发布了最新修订版 (v1.1.1)，以解决对第9章和第10章的小更改，这些更改未包含在先前的修订版中。

更新 (2022年1月23日): 修订版 (v1.1) 今天发布-更好的图形，改进的格式，更大的页面大小 (从而将页数从1187页减少到1045页-没有内容被删除!)。如果你已经买了这本书，你可以随时下载新版本!

如果你正在寻找一本书，在那里你可以学习深度学习和PyTorch，而不必花几个小时来解密神秘的文本和代码，这很容易阅读，这就是它🙂

这本书涵盖了从梯度下降的基础知识一直到使用HuggingFace微调大型NLP模型 (BERT和GPT-2)。它分为四个部分

第一部分: 基本原理 (梯度下降、训练线性和逻辑回归)
第二部分: 计算机视觉 (更深层次的模型和激活函数，卷积，转移学习，初始化方案)
第三部分: 序列 (RNN，GRU，LSTM，seq2seq模型，注意，自我注意，变形金刚)
第四部分: 自然语言处理 (标记化，嵌入，语境词嵌入，ELMo，BERT，GPT-2)
这不是一本典型的书: 大多数教程从一些漂亮的图像分类问题开始，以说明如何使用PyTorch。这看起来很酷，但我相信它会分散你对主要目标的注意力: PyTorch是如何工作的？在本书中，我提出了一种结构化的，增量的，从第一原理的方法来学习PyTorch (并在适当的时候解决漂亮的图像分类问题)。

此外，无论如何，这都不是一本正式的书: 我在写这本书，就好像我在和你，读者交谈一样。我会问你问题 (并在不久后给你答案)，我也会讲 (愚蠢的) 笑话。

我在这里的工作是让你理解这个话题，所以我会尽可能避免花哨的数学符号，用简单的英语拼写出来。

在本书中，我将指导您完成PyTorch中许多模型的开发，向您展示为什么PyTorch使在Python中构建模型变得更加容易和直观: autograd，动态计算图，模型类等等。

我们将逐步建立，不仅是模型本身，还有你的理解，因为我向你展示了代码背后的原因，以及如何避免一些常见的陷阱和错误。

我为一般的初学者写了这本书-不仅是PyTorch初学者。我不时会花一些时间解释一些基本概念，我认为这些概念对于正确理解代码中发生的事情至关重要。

也许您已经很了解其中一些概念: 如果是这种情况，您可以简单地跳过它们，因为我已经使这些解释尽可能独立于其余内容。