philip hua (148 Ergebnisse)

hardcover. Zustand: Very Good.

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Zustand: New. Brand New! Not Overstocks or Low Quality Book Club Editions! Direct From the Publisher! We're not a giant, faceless warehouse organization! We're a small town bookstore that loves books and loves it's customers! Buy from Lakeside Books!

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Paperback. Zustand: New. 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 LearnGrasp the fundamentals of various neural network topologies, including DNN, RNN, LSTM, VAE, GAN, and LLMsImplement neural networks using the latest versions of TensorFlow and Keras, with detailed Python code examplesKnow the techniques for data pre-processing, model selection, and customization to optimize machine learning modelsApply machine learning and neural network techniques in various professional scenarios Who This Book Is ForData scientists, machine learning enthusiasts, and software developers who wish to deepen their understanding of neural networks and machine learning techniques.

Paperback. Zustand: new. Paperback. 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 LearnGrasp the fundamentals of various neural network topologies, including DNN, RNN, LSTM, VAE, GAN, and LLMsImplement neural networks using the latest versions of TensorFlow and Keras, with detailed Python code examplesKnow the techniques for data pre-processing, model selection, and customization to optimize machine learning modelsApply machine learning and neural network techniques in various professional scenarios Who This Book Is ForData scientists, machine learning enthusiasts, and software developers who wish to deepen their understanding of neural networks and machine learning techniques Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Paperback. Zustand: Very Good. The book has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged.

Paperback. Zustand: New. 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 LearnGrasp the fundamentals of various neural network topologies, including DNN, RNN, LSTM, VAE, GAN, and LLMsImplement neural networks using the latest versions of TensorFlow and Keras, with detailed Python code examplesKnow the techniques for data pre-processing, model selection, and customization to optimize machine learning modelsApply machine learning and neural network techniques in various professional scenarios Who This Book Is ForData scientists, machine learning enthusiasts, and software developers who wish to deepen their understanding of neural networks and machine learning techniques.

paperback. Zustand: Very Good.

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Paperback. Zustand: Brand New. 264 pages. 9.00x6.00x0.66 inches. In Stock.

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Magazine. First edition. Near Fine magazine with light signs of handling/shelving. SHIPS THE NEXT BUSINESS DAY, WRAPPED IN PADDING AND CARDBOARD. The January 25, 2009, issue of the Sunday New York Times Magazine with: Daniel Bergner on findings by a new generation of postfeminist sexologists with a portfolio of photographs by Ryan McGinley; a pictorial photographed by Nikolas Koenig on the Philip Johnson-designed house across the street from his Glass House, with text by Pilar Viladas; making jewelry out of Barbie dolls by Rob Walker; the role of social-networking in the Egyptian call for social change by Samantha M. Shapiro with photos by Shawn Baldwin; a profile of Yu Hua, the Chinese novelist, by Pankaj Mishra, with a portrait by Han Lei and photos by Gueorgui Pinkhassov; Prime Baby, a graphic novel by Gene Luen Yang, chapter 8; the conclusion of The Girl in the Green Raincoat, fiction by Laura Lippman; an interview with Arthur Levitt; and much more. Staple-bound magazine; 66 pages; color and b&w reproductions throughout; 9.5 x 11.5 inches.

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Zustand: New. 2023rd edition NO-PA16APR2015-KAP.

Zustand: New. 1st ed. 2022 edition NO-PA16APR2015-KAP.

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Paperback. Zustand: New. As a social justice endeavor, one of the goals of inclusive education is to bolster the education of all students by promoting equal opportunities for all, and investing sufficient support, curriculum and pedagogy that cultivates high self-concepts, emphasizes students' strengths rather than weaknesses, and assists students to reach their optimal potential to make a contribution to society. Dedicated to the identification of international strategies to achieve this goal, Inclusive Education for Students with Intellectual Disabilities presents examples of theory, research, policy, and practice that will advance our understanding of how best to educate and more generally structure educational environments to promote social justice and equity. Importantly, this discussion transcends research methodology, context, and geographical locations and may lead to far-reaching applications. As such, the focus is placed on research-derived educational and psycho-educative practices that seed success for students with intellectual disabilities in inclusive educational settings and the volume showcases new directions in theory, research, and practice that may inform the education and psychosocial development of students with intellectual disabilities globally.The chapter contributors in this volume consist of 31 scholars from ten different countries, and they come from a great variety of research areas (i.e., teacher education, educational psychology, special education and disability policy, special needs and inclusive education, health sciences). This volume, with a series of subsections, offers insights and useful strategies to promote meaningful advances for students with intellectual disabilities globally.

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Zustand: As New. Unread book in perfect condition.

Zustand: As New. Unread book in perfect condition.

Zustand: As New. Unread book in perfect condition.