Название: Deep Learning, Vol. 1: From Basics to Practice
Автор: Andrew Glassner
Издательство: Amazon Digital Services LLC
ASIN: B079XSQNRX
Год: 2018
Страниц: 909
Язык: английский
Формат: True PDF
Размер: 130.3 MB

People are using the tools of deep learning to change how we think about science, art, engineering, business, medicine, and even music. This book is for people who want to understand this field well enough to create deep learning systems, train them, and then use them with confidence to make their own contributions.

The book takes a friendly, informal approach. Our goal is to make the ideas of this field simple and accessible to everyone, as shown in the Table of Contents below.

Since most practitioners today use one of several free, open-source deep-learning libraries to build their systems, the hard part isn't in the programming. Rather, it's knowing what tools to use, and when, and how. Building a working deep learning system requires making a series of technically informed choices, and with today's tools, those choices require understanding what's going on under the hood.

This book is designed to give you that understanding. You'll be able to choose the right kind of architecture, how to build a system that can learn, how to train it, and then how to use it to accomplish your goals. You'll be able to read and understand the documentation for whatever library you'd like to use. And you'll be able to follow exciting, on-going breakthroughs as they appear, because you'll have the knowledge and vocabulary that let you read new material, and discuss it with other people doing deep learning.

The book is extensively illustrated with over 1000 original figures. They are also all available for free download, for your own use.

Table of Contents:

1 Introduction to Machine Learning
2 Statistics
3 Probability
4 Bayes' Rule
5 Curves And Surfaces
6 Information Theory
7 Classification
8 Training And Testing
9 Overfitting And Underfitting
10 Neurons
11 Learning And Reasoning
12 Data Preparation
13 Classifiers
14 Ensembles
15 Scikit-Learn
16 Feed Forward Networks
17 Activation Functions
18 Backpropagation
19 Optimizers








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