This book is for data science practitioners, machine learning engineers, and those interested in deep learning who have a basic foundation in machine learning and some Python programming experience. A background in mathematics and conceptual understanding of calculus and statistics will help you gain maximum benefit from this book.

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).

To get the most out of this book, you should be familiar with Python. You'd benefit from some basic knowledge of calculus and statistics. The code examples are best run on a Linux machine with an NVIDIA GPU capable of running PyTorch, TensorFlow, and Keras.

import torch

torch.manual_seed(1234)

hidden_units = 5

net = torch.nn.Sequential(
 torch.nn.Linear(4, hidden_units),
 torch.nn.ReLU(),
 torch.nn.Linear(hidden_units, 3)
)

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