Deep learning architectures are composed of multilevel nonlinear operations that represent high-level abstractions. This allows you to learn useful feature representations from data. Hands-On Deep Learning Architectures with Python gives you a rundown explaining the essential learning algorithms used for deep and shallow architectures. Packed with practical implementations and ideas to build efficient artificial intelligence systems, this book will help you learn how neural networks play a major role in building deep architectures.
You will gain an understanding of various deep learning architectures, such as AlexNet, VGG Net, GoogleNet, and many more, with easy-to-follow code and diagrams. In addition to this, the book will also guide you in building and training various deep architectures, such as the Boltzmann mechanism, autoencoders, convolutional neural networks (CNNs), recurrent neural networks (RNN), natural language processing (NLP), generative adversarial networks (GANs), and others, with practical implementations. This book explains the essential learning algorithms used for deep and shallow architectures.
By the end of this book, you will be able to construct deep models using popular frameworks and datasets with the required design patterns for each architecture. You will be ready to explore the possibilities of deep architectures in today's world.
