Neural Networks Tensorflow Keras by Hua Philip (17 results)

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Paperback. Condition: 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. Condition: 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. Condition: 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.

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Paperback. Condition: 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. Condition: 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 our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Paperback. Condition: 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 our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Taschenbuch. Condition: Neu. Neuware -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).APress in Springer Science + Business Media, Heidelberger Platz 3, 14197 Berlin 192 pp. Englisch.

Paperback. Condition: 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.

Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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 scenariosWho This Book Is ForData scientists, machine learning enthusiasts, and software developers who wish to deepen their understanding of neural networks and machine learning techniques 270 pp. Englisch.

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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 scenariosWho This Book Is ForData scientists, machine learning enthusiasts, and software developers who wish to deepen their understanding of neural networks and machine learning techniques.