Preface

Neural Networks (NNs) play a very important role in deep learning and Artificial Intelligence (AI), with applications in a wide variety of domains, from medical diagnosis to financial forecasting, and even machine diagnostics.

Hands-On Neural Networks is designed to guide you through learning about neural networks in a practical way. The book will get you started by giving you a brief introduction to perceptron networks. You will then gain insights into machine learning and also understand what the future of AI could look like. Next, you will study how embeddings can be used to process textual data, and the role of long short-term memory (LSTMs) networks in helping you to solve common natural language processing (NLP) problems. The later chapters will demonstrate how you can implement advanced concepts, including transfer learning, Generative Adversarial Networks (GANs), autoencoders (AEs), and reinforcement learning (RL). Finally, you can look forward to further content on the latest advancements in the field of neural networks.

By the end of this book, you will have the skills you need to build, train, and optimize your own neural network model that can be used to provide predictable solutions.