03535nam a22004335i 4500001001800000003000900018005001700027007001500044008004100059020003700100024003500137050001400172072001700186072002300203072001600226100008100242245014600323250001800469264005400487300006600541336002600607337002600633338003600659347002400695505028400719520175001003650002902753650004702782650002902829650001902858710003402877773002602911776003602937776003602973856004603009912001403055912001503069999001703084979-8-8688-1020-6DE-He21320251030114916.0cr nn 008mamaa250101s2024    xxu|    s    |||| 0|eng d  a97988688102069979-8-8688-1020-67 a10.1007/979-8-8688-1020-62doi 4aQ325.5-.7 7aUYQM2bicssc 7aMAT0290002bisacsh 7aUYQM2thema1 aHua, Philip.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut99004510aNeural Networks with TensorFlow and Kerash[electronic resource] :bTraining, Generative Models, and Reinforcement Learning /cby Philip Hua.  a1st ed. 2024. 1aBerkeley, CA :bApress :bImprint: Apress,c2024.  aXIII, 178 p. 53 illus., 43 illus. in color.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda0 aChapter 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.  aExplore 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 . 0aMachine learning.955059 0aPython (Computer program language).96460814aMachine Learning.95505924aPython.9475532 aSpringerLink (Online service)0 tSpringer Nature eBook08iPrinted edition:z979886881019008iPrinted edition:z979886881021340uhttps://doi.org/10.1007/979-8-8688-1020-6  aZDB-2-CWD  aZDB-2-SXPC  c66285d66285