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

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.

掌握神經網絡的有效導航，包括卷積和轉換器，使用Python解決計算機視覺和自然語言處理任務

主要特點：

- 理解深度神經網絡的理論、數學基礎和結構
- 熟悉轉換器、大型語言模型和卷積網絡
- 學習如何應用它們解決各種計算機視覺和自然語言處理問題。購買印刷版或Kindle電子書，可獲贈免費PDF電子書

書籍描述：

深度學習領域在過去幾年中發展迅速，今天已經涵蓋了廣泛的應用。這使得在沒有堅實基礎的情況下，導航和理解變得具有挑戰性。本書將引導您從神經網絡的基礎知識到當今使用的最先進的大型語言模型。

本書的第一部分介紹了主要的機器學習概念和範式。它涵蓋了神經網絡的數學基礎、結構和訓練算法，並深入探討了深度學習的本質。

本書的第二部分介紹了計算機視覺中的卷積網絡。我們將學習如何解決圖像分類、目標檢測、實例分割和圖像生成任務。

第三部分專注於注意機制和轉換器-大型語言模型的核心網絡架構。我們將討論它們可以解決的新型高級任務，例如聊天機器人和文本到圖像生成。

通過閱讀本書，您將對深度神經網絡的內部運作有透徹的理解。您將能夠開發新模型或適應現有模型來解決您的任務。您還將具備足夠的理解能力，以繼續進行研究並跟上該領域的最新進展。

您將學到什麼：

- 建立深度神經網絡的理論基礎
- 理解卷積網絡並在計算機視覺應用中應用它們
- 熟悉自然語言處理和循環網絡
- 探索注意機制和轉換器
- 將轉換器和大型語言模型應用於自然語言和計算機視覺
- 使用PyTorch、Keras和Hugging Face Transformers實現編碼示例
- 使用MLOps開發和部署神經網絡模型

本書適合對象：

本書適合軟件開發人員/工程師、學生、數據科學家、數據分析師、機器學習工程師、統計學家以及對深度學習感興趣的任何人。需要具備Python編程的先備知識。