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.
publication date
2023-11-24
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
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
approach
Each chapter is organized with a comprehensive theoretical introduction to the topic as its main body. This is followed by coding examples that serve to validate the presented theory, providing readers with a practical hands-on experience. The examples are executed using PyTorch, Keras, or Hugging Face Transformers. The book is designed for individuals with minimal prior deep learning knowledge, employing clear and straightforward language throughout.
audience
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.
short description
The book will start from the theoretical foundations of deep neural networks, and it will delve into the most popular network architectures – transformers and convolutional networks. It will combine them with PyTorch, Keras, and Hugging Face Transformers examples in the fields of computer vision and natural language processing.
subtitle
Understand how deep neural networks work and apply them to real-world tasks
keywords
Machine learning book, Ai and machine learning for coders, Hands-on machine learning, Machine learning python, Deep learning book, Deep learning with python, Pytorch book, Deep learning with pytorch, Pytorch deep learning, Neural networks and deep learning, Lstm neural network
Product ISBN
9781837638505
