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.

Table of Contents

Machine 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)

Також купити книгу Python Deep Learning - Third Edition: Understand how deep neural networks work and apply them to real-world tasks 3rd ed. Edition, Ivan Vasilev Ви можете по посиланню