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Neural networks with TensorFlow and ...

Hua, Philip.

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Neural networks with TensorFlow and Keras = training, generative models, and reinforcement learning /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Neural networks with TensorFlow and Keras/ by Philip Hua.
Reminder of title:	training, generative models, and reinforcement learning /
Author:	Hua, Philip.
Published:	Berkeley, CA :Apress : : 2024.,
Description:	xiii, 178 p. :ill. (chiefly color), digital ;24 cm.
[NT 15003449]:	Chapter 1: Introduction to Neural Networks -- Chapter 2: Using Tensors -- Chapter 3: How Machines Learn -- Chapter 4: Network Layers -- Chapter 5: The Training Process -- Chapter 6: Generative Models -- Chapter 7: Re-enforcement Learning -- Chapter 8: Using Pre-trained Networks.
Contained By:	Springer Nature eBook
Subject:	Neural networks (Computer science) -
Online resource:	https://doi.org/10.1007/979-8-8688-1020-6
ISBN:	9798868810206

Neural networks with TensorFlow and Keras = training, generative models, and reinforcement learning /
Hua, Philip.

Neural networks with TensorFlow and Kerastraining, generative models, and reinforcement learning /[electronic resource] :by Philip Hua. - Berkeley, CA :Apress :2024. - xiii, 178 p. :ill. (chiefly color), digital ;24 cm.

Chapter 1: Introduction to Neural Networks -- Chapter 2: Using Tensors -- Chapter 3: How Machines Learn -- Chapter 4: Network Layers -- Chapter 5: The Training Process -- Chapter 6: Generative Models -- Chapter 7: Re-enforcement Learning -- Chapter 8: Using Pre-trained Networks.

Explore the capabilities of machine learning and neural networks. This comprehensive guidebook is tailored for professional programmers seeking to deepen their understanding of neural networks, machine learning techniques, and large language models (LLMs). The book explores the core of machine learning techniques, covering essential topics such as data pre-processing, model selection, and customization. It provides a robust foundation in neural network fundamentals, supplemented by practical case studies and projects. You will explore various network topologies, including Deep Neural Networks (DNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM) networks, Variational Autoencoders (VAE), Generative Adversarial Networks (GAN), and Large Language Models (LLMs). Each concept is explained with clear, step-by-step instructions and accompanied by Python code examples using the latest versions of TensorFlow and Keras, ensuring a hands-on learning experience. By the end of this book, you will gain practical skills to apply these techniques to solving problems. Whether you are looking to advance your career or enhance your programming capabilities, this book provides the tools and knowledge needed to excel in the rapidly evolving field of machine learning and neural networks. What You Will Learn Grasp the fundamentals of various neural network topologies, including DNN, RNN, LSTM, VAE, GAN, and LLMs Implement neural networks using the latest versions of TensorFlow and Keras, with detailed Python code examples Know the techniques for data pre-processing, model selection, and customization to optimize machine learning models Apply machine learning and neural network techniques in various professional scenarios.

ISBN: 9798868810206

Standard No.: 10.1007/979-8-8688-1020-6doiSubjects--Topical Terms:

532070
Neural networks (Computer science)

LC Class. No.: QA76.87

Dewey Class. No.: 006.32

Neural networks with TensorFlow and Keras = training, generative models, and reinforcement learning /
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505 0 $a Chapter 1: Introduction to Neural Networks -- Chapter 2: Using Tensors -- Chapter 3: How Machines Learn -- Chapter 4: Network Layers -- Chapter 5: The Training Process -- Chapter 6: Generative Models -- Chapter 7: Re-enforcement Learning -- Chapter 8: Using Pre-trained Networks.
520 $a Explore the capabilities of machine learning and neural networks. This comprehensive guidebook is tailored for professional programmers seeking to deepen their understanding of neural networks, machine learning techniques, and large language models (LLMs). The book explores the core of machine learning techniques, covering essential topics such as data pre-processing, model selection, and customization. It provides a robust foundation in neural network fundamentals, supplemented by practical case studies and projects. You will explore various network topologies, including Deep Neural Networks (DNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM) networks, Variational Autoencoders (VAE), Generative Adversarial Networks (GAN), and Large Language Models (LLMs). Each concept is explained with clear, step-by-step instructions and accompanied by Python code examples using the latest versions of TensorFlow and Keras, ensuring a hands-on learning experience. By the end of this book, you will gain practical skills to apply these techniques to solving problems. Whether you are looking to advance your career or enhance your programming capabilities, this book provides the tools and knowledge needed to excel in the rapidly evolving field of machine learning and neural networks. What You Will Learn Grasp the fundamentals of various neural network topologies, including DNN, RNN, LSTM, VAE, GAN, and LLMs Implement neural networks using the latest versions of TensorFlow and Keras, with detailed Python code examples Know the techniques for data pre-processing, model selection, and customization to optimize machine learning models Apply machine learning and neural network techniques in various professional scenarios.
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