Book Image

Python Deep Learning - Third Edition

By : Ivan Vasilev
4 (1)
Book Image

Python Deep Learning - Third Edition

4 (1)
By: Ivan Vasilev

Overview of this book

The field of deep learning has developed rapidly recently and today covers a broad range of applications. This makes it challenging to navigate and hard to understand without solid foundations. This book will guide you from the basics of neural networks to the state-of-the-art large language models in use today. The first part of the book introduces the main machine learning concepts and paradigms. It covers the mathematical foundations, the structure, and the training algorithms of neural networks and dives into the essence of deep learning. The second part of the book introduces convolutional networks for computer vision. We’ll learn how to solve image classification, object detection, instance segmentation, and image generation tasks. The third part focuses on the attention mechanism and transformers – the core network architecture of large language models. We’ll discuss new types of advanced tasks they can solve, such as chatbots and text-to-image generation. By the end of this book, you’ll have a thorough understanding of the inner workings of deep neural networks. You'll have the ability to develop new models and adapt existing ones to solve your tasks. You’ll also have sufficient understanding to continue your research and stay up to date with the latest advancements in the field.
Table of Contents (17 chapters)
1
Part 1:Introduction to Neural Networks
5
Part 2: Deep Neural Networks for Computer Vision
8
Part 3: Natural Language Processing and Transformers
13
Part 4: Developing and Deploying Deep Neural Networks

Intuition and justification for CNNs

The information we extract from sensory inputs is often determined by their context. With images, we can assume that nearby pixels are closely related, and their collective information is more relevant when taken as a unit. Conversely, we can assume that individual pixels don’t convey information related to each other. For example, to recognize letters or digits, we need to analyze the dependency of pixels close by because they determine the shape of the element. In this way, we could figure out the difference between, say, a 0 or a 1. The pixels in an image are organized in a two-dimensional grid, and if the image isn’t grayscale, we’ll have a third dimension for the color channels.

Alternatively, a magnetic resonance image (MRI) also uses three-dimensional space. You might recall that, until now, if we wanted to feed an image to an NN, we had to reshape it from a two-dimensional array into a one-dimensional array. CNNs...