Python Deep Learning
Understand how deep neural networks work and apply them to real-world tasks
3rd Edition
Published on 24. November 2023
Book
Paperback/Softback
362 pages
978-1-83763-850-5 (ISBN)
Description
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 DescriptionThe 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 forThis 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.
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 DescriptionThe 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 forThis 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.
More details
Edition
3rd Revised edition
Language
English
Place of publication
Birmingham
United Kingdom
Target group
Professional and scholarly
Edition type
Revised edition
Dimensions
Height: 235 mm
Width: 191 mm
Thickness: 20 mm
Weight
676 gr
ISBN-13
978-1-83763-850-5 (9781837638505)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Ivan Vasilev | Valentino Zocca
Python Deep Learning
Understand how deep neural networks work and apply them to real-world tasks
E-Book
05/2024
3rd Edition
Packt Publishing Limited
from
€35.99
Available for download
Person
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.
He holds an MSc in artificial intelligence from Sofia University St. Kliment Ohridski and has written two previous books on the same topic.
Content
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)
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)