Python Deep Learning: Understand how deep neural networks work and apply them to real-world tasks 3rd ed. Edition. Ivan Vasilev

Артикул: LB-0018476
Наличие:Есть в наличии

950.00 грн.

Количество

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 the structure of deep neural networks
Become familiar with transformers, large language models, and convolutional networks

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 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.

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.