Neural Networks with TensorFlow and Keras

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   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   Who This Book Is For Data scientists, machine learning enthusiasts, and software developers who wish to deepen their understanding of neural networks and machine learning techniques 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. Philip Hua brings over 30 years of experience in investment, risk management, and IT. He has held senior positions as a partner at a hedge fund, led risk and IT departments at both large and boutique firms, and co-founded a successful fintech company. Alongside Dr. Paul Wilmott, he developed the CrashMetrics methodology, a crucial tool for evaluating severe market risk in portfolios. Philip holds a PhD in Applied Mathematics from Imperial College London, an MBA, and a BSc in Engineering.