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)

Convolutional layers

The convolutional layer is the most important building block of a CNN. It consists of a set of filters (also known as kernels or feature detectors), where each filter is applied across all areas of the input data. A filter is defined by a set of learnable weights. As a nod to the topic at hand, the following image illustrates this very well:

Displayed is a two-dimensional input layer of a neural network. For the sake of simplicity, we'll assume that this is the input layer, but it can be any layer of the network. As we've seen in the previous chapters, each input neuron represents the color intensity of a pixel (we'll assume it's a grayscale image for simplicity). First, we'll apply a 3x3 filter in the top-right corner of the image. Each input neuron is associated with a single weight of the filter. It has nine weights, because of...