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)

Brief history of AV research

The first serious attempt to implement self-driving cars began in the 1980s in Europe and the USA. Since the mid 2000s, progress has rapidly accelerated. The following is a timeline of some AV research historic points and milestones:

  • The first major effort in the area was the Eureka Prometheus Project (https://en.wikipedia.org/wiki/Eureka_Prometheus_Project), which lasted from 1987 to 1995. It culminated in 1995, when an autonomous Mercedes-Benz S-Class took a 1,600 km trip from Munich to Copenhagen and back using computer vision. At some points, the car achieved speeds of up to 175 km/h on the German Autobahn (fun fact: some sections of the Autobahn don't have speed restrictions). The car was able to overtake other cars on its own. The average distance between human interventions was 9 km, and at one point it drove 158 km without interventions...