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024 7 _a10.1007/979-8-8688-1020-6
_2doi
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100 1 _aHua, Philip.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_990045
245 1 0 _aNeural Networks with TensorFlow and Keras
_h[electronic resource] :
_bTraining, Generative Models, and Reinforcement Learning /
_cby Philip Hua.
250 _a1st ed. 2024.
264 1 _aBerkeley, CA :
_bApress :
_bImprint: Apress,
_c2024.
300 _aXIII, 178 p. 53 illus., 43 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _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.
520 _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 .
650 0 _aMachine learning.
_955059
650 0 _aPython (Computer program language).
_964608
650 1 4 _aMachine Learning.
_955059
650 2 4 _aPython.
_947553
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9798868810190
776 0 8 _iPrinted edition:
_z9798868810213
856 4 0 _uhttps://doi.org/10.1007/979-8-8688-1020-6
912 _aZDB-2-CWD
912 _aZDB-2-SXPC
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