Dive into Deep Learning (D2L.ai)

The first part covers basics and preliminaries.

1. Chapter1: offers an introduction to deep learning.
2. Chapter2: we quickly bring you up to speed on the prerequisites required for hands-on deep learning, such as how to store and manipulate data, and how to apply various numerical operations based on basic concepts from linear algebra, calculus, and probability.
3. Chapter3 and Chapter4 cover the most basic concepts and techniques of deep learning, such as linear regression, multilayer perceptrons and regularization.

The next five chapters focus on modern deep learning techniques.

1. Chapter5 describes the various key components of deep learning calculations and lays the groundwork for us to subsequently implement more complex models.
2. Chapter6 and Chapter7, we introduce convolutional neural networks (CNNs), powerful tools that form the backbone of most modern computer vision systems.
3. Chapter8 and Chapter9, we introduce recurrent neural networks (RNNs), models that exploit temporal or sequential structure in data, and are commonly used for natural language processing and time series prediction.
4. Chapter10, we introduce a new class of models that employ a technique called attention mechanisms and they have recently begun to displace RNNs in natural language processing.

Part three discusses scalability, efficiency, and applications.

1. Chapter11, we discuss several common optimization algorithms used to train deep learning models.
2. Chapter12 examines several key factors that influence the computational performance of your deep learning code.
3. Chapter13, we illustrate major applications of deep learning in computer vision.
4. Chapter14 and Chapter15, we show how to pretrain language representation models and apply them to natural language processing tasks.

To successfully apply deep learning, you must understand how to cast a problem, the mathematics of modeling, the algorithms for fitting your models to data, and the engineering techniques to implement it all.

Resource:

FREE PDF download:
https://d2l.ai/d2l-en.pdf
Manual
https://d2l.ai/chapter_installation/index.html
All Notebooks (zip file):
https://d2l.ai/d2l-en.zip
Website:
https://d2l.ai
Github:
https://github.com/d2l-ai/d2l-en
Artículo:
https://www.amazon.science/latest-news/amazon-scientists-author-popular-deep-learning-book