Python Deep Learning - Third Edition | Understand how deep neural networks work and apply them to real-world tasks

Ivan Vasilev

ISBN 10: 1837638500 / ISBN 13: 9781837638505

Verlag: Packt Publishing, 2023

Beschreibung

Python Deep Learning - Third Edition | Understand how deep neural networks work and apply them to real-world tasks | Ivan Vasilev | Taschenbuch | Englisch | 2023 | Packt Publishing | EAN 9781837638505 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.

Bestandsnummer des Verkäufers 128050761

Bibliografische Details

• Titel: Python Deep Learning - Third Edition | Understand how deep neural networks work and apply them to real-world tasks
• Verlag: Packt Publishing
• Erscheinungsjahr: 2023
• Sprache: Englisch
• ISBN-10: 1837638500
• ISBN-13: 9781837638505
• Einband: Taschenbuch
• Auflage: 3. Auflage
• Zustand: Neu
• Abmessungen: 235B x 191 H x 20 T Millimeter Breite: 235 mm, Höhe: 191 mm, Tiefe: 20 mm
• Gewicht: 676 Gramm
• Verkäuferkataloge:
  • Bücher

Über diesen Titel

Inhaltsangabe

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
• 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 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

1. Machine Learning - an Introduction
2. Neural Networks
3. Deep Learning Fundamentals
4. Computer Vision with Convolutional Networks
5. Advanced Computer Vision Applications
6. Natural Language Processing and Recurrent Neural Networks
7. The Attention Mechanism and Transformers
8. Exploring Large Language Models in Depth
9. Advanced Applications of Large Language Models
10. Machine Learning Operations (ML Ops)

Über die Autorin bzw. den Autor

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, where he continued its development. He has also worked as a machine learning engineer and researcher in the area of medical image classification and segmentation with deep neural networks. Since 2017 he has focused on financial machine learning. He is working on a Python open source algorithmic trading library, which provides the infrastructure to experiment with different ML algorithms. The author holds an MSc degree in Artificial Intelligence from The University of Sofia, St. Kliment Ohridski.