Hands-On Deep Learning Architectures with Python: Create deep neural networks to solve computational problems using TensorFlow and Keras

Concepts, tools, and techniques to explore deep learning architectures and methodsKey FeaturesExplore advanced deep learning architectures using different datasets and frameworksImplement deep architectures for neural network models such as CNN, RNN, GAN, and many moreDiscover design patterns for various deep learning architecturesBook DescriptionDeep learning architectures are composed of multilevel nonlinear operations that represent high-level abstractions. This allows you to learn useful feature representations from the data.Hands-On Deep Learning Architectures with Python will give 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 understand 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 (CNN), Recurrent Neural Networks (RNN), Natural Language Processing (NLP), Generative Adversarial Networks (GAN), and more with practical implementations.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 with deep architectures in today's world.What You Will LearnPractice CNN, RNN, and other commonly used architectures with PythonExplore architectures such as VGGNet, AlexNet, and GoogLeNetBuild deep learning architectures for AI applications such as face and image recognition, abnormal behavior, and fraud detectionUnderstand the architectures and applications of Boltzmann machines and autoencoder with concrete examplesLearn and apply artificial intelligence and neural network concepts to your architectureUnderstand deep learning architectures for mobile and embedded systems Who This Book Is ForIf you’re a data scientist, machine learning developer/engineer, deep learning practitioner, or are curious about the field of AI and want to upgrade your knowledge of various deep learning architectures, this book will appeal to you. You are expected to have some knowledge of statistics and machine learning algorithms to get the best out of this book.

Author: Antonio Amadeu

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