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

Ivan Vasilev

Softcover

ISBN 10: 1837638500 ISBN 13: 9781837638505

Publisher: Packt Publishing, 2023

Synopsis

Become well-versed with the theory of deep neural networks, including convolutional, recurrent, graph, and the new transformer architectures. Use Python to apply these models to various computer vision and natural language processing tasks.

Key Features

• Understand the building blocks and mathematical foundations of neural networks
• Become familiar with convolutional, recurrent, graph, and transformers networks
• Learn how to apply them on various computer vision and natural language processing tasks

Book Description

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.

What you will learn

• Building blocks, structure, and theoretical foundations of neural networks
• Convolutional networks theory and different convolutional architectures
• Learn to solve image classification, object detection, and image segmentation
• Recurrent neural networks theory and implementation
• Language modelling, text classification, question answering, text generation
• The attention mechanism and transformers, including large language models
• Graph neural networks and their applications
• Implement various tasks with PyTorch and Keras
• Deploy neural network models in production environments

Who This Book Is For

This book is for people already familiar with programming: software developers/engineers, students, data scientists, data analysts, machine learning engineers, statisticians, and anyone interested in deep learning who have a Python programming experience.

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 Tasks
6. Recurrent Neural Networks and Language Models
7. The Attention Mechanism and Transformers
8. Applications of Transformers
9. Graph Neural Networks
10. Machine Learning Operations (ML Ops)

About this title

About the Author

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.

From the Back Cover

Master effective navigation of neural networks, including convolutions and transformers, to tackle computer vision and NLP tasks using PythonKey FeaturesUnderstand the theory, mathematical foundations and the structure of deep neural networks Become familiar with transformers, large language models, and convolutional networks Learn how to apply them on 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 in the past years and today covers 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 chat bots 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 or 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 learnEstablish 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 ContentsMachine Learning - an Introduction Neural Networks Deep Learning Fundamentals Computer Vision with Convolutional Networks Advanced Computer Vision Applications Natural Language Processing and Recurrent Neural Networks The Attention Mechanism and Transformers Exploring Large Language Models in Depth Advanced Applications of Large Language Models Machine Learning Operations (ML Ops)

Edition details

• Publisher: Packt Publishing
• Publication date: 2023
• Language: English
• ISBN 10: 1837638500
• ISBN 13: 9781837638505
• Binding: Paperback
• Edition number: 3
• Number of pages: 394