Introduction to Deep Learning: with complete Python and TensorFlow examples

About the book: In Computer Sciences there is currently a gold rush mood due to a new field called “Deep Learning”. But what is Deep Learning? This book is an introduction to Neural Networks and the most important Deep Learning model – the Convolutional Neural Network model including a description of tricks that can be used to train such models more quickly. We start with the biological role model: the Neuron. About 86.000.000.000 of these simple processing elements are in your brain! And they all work in parallel! We discuss how to model the operation of a biological neuron with technical neuron models and then consider the first simple single-layer network of technical neurons. We then introduce the Multi-Layer Perceptron (MLP) and the Convolutional Neural Network (CNN) model which uses the MLP at its end. At the end of the book we discuss promising new directions for the field of Deep Learning. A famous physicist once said: “What I cannot create, I do not understand”. For this, the book is full of examples of how to program all models discussed in Python and TensorFlow – Today, the most important Deep Learning library. About the author: Prof. Dr.-Ing. Juergen Brauer is a professor for Sensor Data Processing and Programming at the University of Applied Sciences Kempten in Germany where he holds a “Deep Learning” and other machine learning related lectures for Computer Science and Advanced Driver Assistance Systems students. His personal experience tells him: “What I cannot program, I do not understand”.

Table of Contents

Chapter 1. What is Deep Learning?
Chapter 2. Deep Learning: An agile field
Chapter 3. The biological role model: The Neuron
Chapter 4. The many faces of a neuron
Chapter 5. The Perceptron
Chapter 6. Self-Organizing Maps
Chapter 7. Multi Layer Perceptrons
Chapter 8. TensorFlow
Chapter 9. Convolutional Neural Networks
Chapter 10. Deep Learning Tricks
Chapter 11. Beyond Deep Learning
Chapter 12. Exercises