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)

AV introduction

When we talk about AVs, we usually imagine fully driverless vehicles. But in reality, we have cars, which require a driver, but still provide some automated features.

The Society of Automotive Engineers (SAE) has developed a scale of six levels of automation:

  • Level 0: The driver handles the steering, acceleration, and braking of the vehicle. The features at this level can only provide warnings and immediate assistance to the driver's actions. Examples of features of this level include the following:
    • A lane departure warning simply warns the driver when the vehicle has crossed one of the lane markings.
    • A blind spot warning warns the driver when another vehicle is located in the blind spot area of the car (the area immediately left or right of the rear end of the vehicle).
  • Level 1: Features that provide either steering or acceleration/braking assistance...