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Python Deep Learning - Second Edition: Exploring deep learning techniques and neural network architectures with PyTorch, Keras, and TensorFlow, 2nd Edition

Python Deep Learning - Second Edition: Exploring deep learning techniques and neural network architectures with PyTorch, Keras, and TensorFlow, 2nd Edition

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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 Description:

The 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 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.

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About the Book

Learn advanced state-of-the-art deep learning techniques and their applications using popular Python libraries

Key Features

  • Build a strong foundation in neural networks and deep learning with Python libraries
  • Explore advanced deep learning techniques and their applications across computer vision and NLP
  • Learn how a computer can navigate in complex environments with reinforcement learning

Book Description

With the surge in artificial intelligence in applications catering to both business and consumer needs, deep learning is more important than ever for meeting current and future market demands. With this book, you'll explore deep learning, and learn how to put machine learning to use in your projects.

This second edition of Python Deep Learning will get you up to speed with deep learning, deep neural networks, and how to train them with high-performance algorithms and popular Python frameworks. You'll uncover different neural network architectures, such as convolutional networks, recurrent neural networks, long short-term memory (LSTM) networks, and capsule networks. You'll also learn how to solve problems in the fields of computer vision, natural language processing (NLP), and speech recognition. You'll study generative model approaches such as variational autoencoders and Generative Adversarial Networks (GANs) to generate images. As you delve into newly evolved areas of reinforcement learning, you'll gain an understanding of state-of-the-art algorithms that are the main components behind popular games Go, Atari, and Dota.

By the end of the book, you will be well-versed with the theory of deep learning along with its real-world applications.

What you will learn

  • Grasp the mathematical theory behind neural networks and deep learning processes
  • Investigate and resolve computer vision challenges using convolutional networks and capsule networks
  • Solve generative tasks using variational autoencoders and Generative Adversarial Networks
  • Implement complex NLP tasks using recurrent networks (LSTM and GRU) and attention models
  • Explore reinforcement learning and understand how agents behave in a complex environment
  • Get up to date with applications of deep learning in autonomous vehicles
Who this book is for

This book is for data science practitioners, machine learning engineers, and those interested in deep learning who have a basic foundation in machine learning and some Python programming experience. A background in mathematics and conceptual understanding of calculus and statistics will help you gain maximum benefit from this book.


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Product Details
  • ISBN-13: 9781789348460
  • Publisher: Packt Publishing
  • Publisher Imprint: Packt Publishing
  • Height: 235 mm
  • No of Pages: 386
  • Spine Width: 20 mm
  • Weight: 661 gr
  • ISBN-10: 1789348463
  • Publisher Date: 14 Jan 2019
  • Binding: Paperback
  • Language: English
  • Returnable: N
  • Sub Title: Exploring deep learning techniques and neural network architectures with PyTorch, Keras, and TensorFlow, 2nd Edition
  • Width: 191 mm


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