03767nam a22004815i 4500 979-8-8688-1020-6 DE-He213 20251030114916.0 cr nn 008mamaa 250101s2024 xxu| s |||| 0|eng d 9798868810206 979-8-8688-1020-6 10.1007/979-8-8688-1020-6 doi Q325.5-.7 UYQM bicssc MAT029000 bisacsh UYQM thema Hua, Philip. author. aut http://id.loc.gov/vocabulary/relators/aut 90045 Neural Networks with TensorFlow and Keras [electronic resource] : Training, Generative Models, and Reinforcement Learning / by Philip Hua. 1st ed. 2024. Berkeley, CA : Apress : Imprint: Apress, 2024. XIII, 178 p. 53 illus., 43 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda 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 . Machine learning. 55059 Python (Computer program language). 64608 Machine Learning. 55059 Python. 47553 SpringerLink (Online service) Springer Nature eBook Printed edition: 9798868810190 Printed edition: 9798868810213 https://doi.org/10.1007/979-8-8688-1020-6 ZDB-2-CWD ZDB-2-SXPC 66285 66285