Python Deep Learning - Third Edition: Understand how deep neural networks work and apply them to real-world tasks

Master effective navigation of neural networks, including convolutions and transformers, to tackle computer vision and NLP tasks using Python

Key Features

• Understand the theory, mathematical foundations and structure of deep neural networks
• Become familiar with transformers, large language models, and convolutional networks
• Learn how to apply them to various computer vision and natural language processing problems
• Purchase of the print or Kindle book includes a free PDF eBook

Book Description

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.

What you will learn

• Establish theoretical foundations of deep neural networks
• Understand convolutional networks and apply them in computer vision applications
• Become well versed with natural language processing and recurrent networks
• Explore the attention mechanism and transformers
• Apply transformers and large language models for natural language and computer vision
• Implement coding examples with PyTorch, Keras, and Hugging Face Transformers
• Use MLOps to develop and deploy neural network models

Who this book is for

This book is for software developers/engineers, students, data scientists, data analysts, machine learning engineers, statisticians, and anyone interested in deep learning. Prior experience with Python programming is a prerequisite.

Table of Contents

1. Machine Learning – an Introduction
2. Neural Networks
3. Deep Learning Fundamentals
4. Computer Vision with Convolutional Networks
5. Advanced Computer Vision Applications
6. Natural Language Processing and Recurrent Neural Networks
7. The Attention Mechanism and Transformers
8. Exploring Large Language Models in Depth
9. Advanced Applications of Large Language Models
10. Machine Learning Operations (ML Ops)

Ivan Vasilev started working on the first open source Java deep learning library with GPU support in 2013. The library was acquired by a German company, with whom he continued its development. He has also worked as a machine learning engineer and researcher in medical image classification and segmentation with deep neural networks. Since 2017, he has focused on financial machine learning. He co-founded an algorithmic trading company, where he’s the lead engineer.

He holds an MSc in artificial intelligence from Sofia University St. Kliment Ohridski and has written two previous books on the same topic.