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)

What this book covers

Chapter 1, Machine Learning – an Introduction, will introduce you to the basic ML concepts and terms that we'll be using throughout the book. It will give an overview of the most popular ML algorithms and applications today. It will also introduce the DL library that we'll use throughout the book.

Chapter 2, Neural Networks, will introduce you to the mathematics of neural networks. We'll learn about their structure, how they make predictions (that's the feedforward part), and how to train them using gradient descent and backpropagation (explained through derivatives). The chapter will also discuss how to represent operations with neural networks as vector operations.

Chapter 3, Deep Learning Fundamentals, will explain the rationale behind using deep neural networks (as opposed to shallow ones). It will take an overview of the most popular DL libraries and real-world applications of DL.

Chapter 4, Computer Vision with Convolutional Networks, teaches you about convolutional neural networks (the most popular type of neural network for computer vision tasks). We'll learn about their architecture and building blocks (the convolutional, pooling, and capsule layers) and how to use a convolutional network for an image classification task.

Chapter 5, Advanced Computer Vision, will build on the previous chapter and cover more advanced computer vision topics. You will learn not only how to classify images, but also how to detect an object's location and segment every pixel of an image. We'll learn about advanced convolutional network architectures and the useful practical technique of transfer learning.

Chapter 6, Generating Images with GANs and VAEs, will introduce generative models (as opposed to discriminative models, which is what we'll have covered up until this point). You will learn about two of the most popular unsupervised generative model approaches, VAEs and GANs, as well some of their exciting applications.

Chapter 7, Recurrent Neural Networks and Language Models, will introduce you to the most popular recurrent network architectures: LSTM and gated recurrent unit (GRU). We'll learn about the paradigms of NLP with recurrent neural networks and the latest algorithms and architectures to solve NLP problems. We'll also learn the basics of speech-to-text recognition.

Chapter 8, Reinforcement Learning Theory, will introduce you to the main paradigms and terms of RL, a separate ML field. You will learn about the most important RL algorithms. We'll also learn about the link between DL and RL. Throughout the chapter, we will use toy examples to better demonstrate the concepts of RL.

Chapter 9, Deep Reinforcement Learning for Games, you will understand some real-world applications of RL algorithms, such as playing board games and computer games. We'll learn how to combine the knowledge from the previous parts of the book to create better-than-human computer players on some popular games.

Chapter 10, Deep Learning in Autonomous vehicles, we'll discuss what sensors autonomous vehicles use, so they can create the 3D model of the environment. These include cameras, radar sensors, ultrasound sensors, Lidar, as well as accurate GPS positioning. We'll talk about how to apply deep learning algorithms for processing the input of these sensors. For example, we can use instance segmentation and object detection to detect pedestrians and vehicles using the vehicle cameras. We'll also make an overview of some of the approaches vehicle manufacturers use to solve this problem (for example Audi, Tesla, and so on).