Book Image

Python Deep Learning - Second Edition

By : Ivan Vasilev, Daniel Slater, Gianmario Spacagna, Peter Roelants, Valentino Zocca
Book Image

Python Deep Learning - Second Edition

By: Ivan Vasilev, Daniel Slater, Gianmario Spacagna, Peter Roelants, Valentino Zocca

Overview of this book

With the surge in artificial intelligence in applications catering to both business and consumer needs, deep learning is more important than ever for meeting current and future market demands. With this book, you’ll explore deep learning, and learn how to put machine learning to use in your projects. This second edition of Python Deep Learning will get you up to speed with deep learning, deep neural networks, and how to train them with high-performance algorithms and popular Python frameworks. You’ll uncover different neural network architectures, such as convolutional networks, recurrent neural networks, long short-term memory (LSTM) networks, and capsule networks. You’ll also learn how to solve problems in the fields of computer vision, natural language processing (NLP), and speech recognition. You'll study generative model approaches such as variational autoencoders and Generative Adversarial Networks (GANs) to generate images. As you delve into newly evolved areas of reinforcement learning, you’ll gain an understanding of state-of-the-art algorithms that are the main components behind popular games Go, Atari, and Dota. By the end of the book, you will be well-versed with the theory of deep learning along with its real-world applications.
Table of Contents (12 chapters)

Machine Learning - an Introduction

"Machine Learning (CS229) is the most popular course at Stanford. Why? Because, increasingly, machine learning is eating the world." - Laura Hamilton, Forbes

Machine learning(ML) techniques are being applied in a variety of fields, and data scientists are being sought after in many different industries. With machine learning, we identify the processes through which we gain knowledge that is not readily apparent from data in order to make decisions. Applications of machine learning techniques may vary greatly, and are found in disciplines as diverse as medicine, finance, and advertising.

In this chapter, we'll present different machine learning approaches, techniques, some of their applications to real-world problems, and we'll also introduce one of the major open source packages available in Python for machine learning, PyTorch. This will lay the foundation for the later chapters in which we'll focus on a particular type of machine learning approach using neural networks, which will aim to emulate brain functionality. In particular, we will focus on deep learning. Deep learning makes use of more advanced neural networks than those used during the 1980s. This is not only a result of recent developments in the theory, but also advancements in computer hardware. This chapter will summarize what machine learning is and what it can do, preparing the reader to better understand how deep learning differentiates itself from popular traditional machine learning techniques.

This chapter will cover the following topics:

  • Introduction to machine learning
  • Different machine learning approaches
  • Neural networks
  • Introduction to PyTorch