nach der Bestellung gedruckt Neuware - Printed after ordering - Take the next step in implementing various common and not-so-common neural networks with Tensorflow 1.xKey Features:Skill up and implement tricky neural networks using Google's TensorFlow 1.xAn easy-to-follow guide that lets you explore reinforcement learning, GANs, autoencoders, multilayer perceptrons and more.Hands-on recipes to work with Tensorflow on desktop, mobile, and cloud environmentBook Description:Deep neural networks (DNNs) have achieved a lot of success in the field of computer vision, speech recognition, and natural language processing. This exciting recipe-based guide will take you from the realm of DNN theory to implementing them practically to solve real-life problems in the artificial intelligence domain.In this book, you will learn how to efficiently use TensorFlow, Google's open source framework for deep learning. You will implement different deep learning networks, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Deep Q-learning Networks (DQNs), and Generative Adversarial Networks (GANs), with easy-to-follow standalone recipes. You will learn how to use TensorFlow with Keras as the backend. You will learn how different DNNs perform on some popularly used datasets, such as MNIST, CIFAR-10, and Youtube8m. You will not only learn about the different mobile and embedded platforms supported by TensorFlow, but also how to set up cloud platforms for deep learning applications. You will also get a sneak peek at TPU architecture and how it will affect the future of DNNs.By using crisp, no-nonsense recipes, you will become an expert in implementing deep learning techniques in growing real-world applications and research areas such as reinforcement learning,GANs, and autoencoders.What You Will Learn:Leverage different data sets such as MNIST, CIFAR-10, and Youtube8m with TensorFlow and learn how to access and use them in your codeUse TensorBoard to understand neural network architectures, optimize the learning process, and peek inside the neural network black boxUse different regression techniques for prediction and classifi cation problemsBuild single and multilayer perceptrons in TensorFlowImplement a CNN and a RNN in TensorFlow, and use them to solve real-world problemsLearn how Restricted Boltzmann Machines can be used to recommend moviesUnderstand the implementation of autoencoders and deep belief networks, and use them for emotion detectionMaster the different reinforcement learning methods in order to implement game playing agentsWho this book is for:This book is intended for data analysts, data scientists, machine learning practitioners and deep learning enthusiasts who want to perform deep learning tasks on a regular basis and are looking for a handy guide they can refer to. People who are slightly familiar with neural networks, and now want to gain expertise in working with different types of neural networks and datasets, will find this book quite useful.

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Detalles bibliogr�ficos

T�tulo: TensorFlow 1.x Deep Learning Cookbook : Over 90 unique recipes to solve artificial-intelligence driven problems with Python
Editor: Packt Publishing
Fecha de publicaci�n: 2017
Idioma: Ingl�s
ISBN 10: 1788293592
ISBN 13: 9781788293594
Encuadernaci�n: Taschenbuch
Estado: Neu
Dimensiones: 235�mil�metros de ancho por 191 de alto por 29 de largo
Peso: 989 gramos

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Sinopsis

Take the next step in implementing various common and not-so-common neural networks with Tensorflow 1.x

Key Features:

Skill up and implement tricky neural networks using Google's TensorFlow 1.x
An easy-to-follow guide that lets you explore reinforcement learning, GANs, autoencoders, multilayer perceptrons and more.
Hands-on recipes to work with Tensorflow on desktop, mobile, and cloud environment

Book Description:

Deep neural networks (DNNs) have achieved a lot of success in the field of computer vision, speech recognition, and natural language processing. This exciting recipe-based guide will take you from the realm of DNN theory to implementing them practically to solve real-life problems in the artificial intelligence domain.

In this book, you will learn how to efficiently use TensorFlow, Google's open source framework for deep learning. You will implement different deep learning networks, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Deep Q-learning Networks (DQNs), and Generative Adversarial Networks (GANs), with easy-to-follow standalone recipes. You will learn how to use TensorFlow with Keras as the backend. You will learn how different DNNs perform on some popularly used datasets, such as MNIST, CIFAR-10, and Youtube8m. You will not only learn about the different mobile and embedded platforms supported by TensorFlow, but also how to set up cloud platforms for deep learning applications. You will also get a sneak peek at TPU architecture and how it will affect the future of DNNs.

By using crisp, no-nonsense recipes, you will become an expert in implementing deep learning techniques in growing real-world applications and research areas such as reinforcement learning,GANs, and autoencoders.

What You Will Learn:

Leverage different data sets such as MNIST, CIFAR-10, and Youtube8m with TensorFlow and learn how to access and use them in your code
Use TensorBoard to understand neural network architectures, optimize the learning process, and peek inside the neural network black box
Use different regression techniques for prediction and classifi cation problems
Build single and multilayer perceptrons in TensorFlow
Implement a CNN and a RNN in TensorFlow, and use them to solve real-world problems
Learn how Restricted Boltzmann Machines can be used to recommend movies
Understand the implementation of autoencoders and deep belief networks, and use them for emotion detection
Master the different reinforcement learning methods in order to implement game playing agents

Who this book is for:

This book is intended for data analysts, data scientists, machine learning practitioners and deep learning enthusiasts who want to perform deep learning tasks on a regular basis and are looking for a handy guide they can refer to. People who are slightly familiar with neural networks, and now want to gain expertise in working with different types of neural networks and datasets, will find this book quite useful.

Acerca del autor

Antonio Gulli is a transformational software executive and business leader with a passion for establishing and managing global technological talent for innovation and execution. He is an expert in search engines, online services, machine learning, information retrieval, analytics, and cloud computing. So far, he has been lucky enough to gain professional experience in four different countries in Europe and manage teams in six different countries in Europe and America. Currently, he works as site lead and director of cloud in Google Warsaw, driving European efforts for Serverless, Kubernetes, and Google Cloud UX. Previously, Antonio helped to innovate academic search as the vice president for Elsevier, a worldwide leading publisher. Before that, he drove query suggestions and news search as a principal engineer for Microsoft. Earlier, he served as the CTO for Ask.com, driving multimedia and news search. Antonio has filed for 20+ patents, published multiple academic papers, and served as a senior PC member in multiple international conferences. He truly believes that to be successful, you must have a great combination of management, research skills, just-get-it-done, and selling attitude.Amita Kapoor is an associate professor in the Department of Electronics, SRCASW, University of Delhi. She has been actively teaching neural networks for the last 20 years. She did her master's in electronics in 1996 and PhD in 2011. During her PhD, she was awarded the prestigious DAAD fellowship to pursue a part of her research work in Karlsruhe Institute of Technology, Karlsruhe, Germany. She had been awarded the best presentation award at International Conference Photonics 2008 for her paper. She is a member of professional bodies such as OSA (Optical Society of America), IEEE (Institute of Electrical and Electronics Engineers), INNS (International Neural Network Society), and ISBS (Indian Society for Buddhist Studies). Amita has more than 40 publications in international journals and conferences to her credit. Her present research areas include machine learning, artificial intelligence, neural networks, robotics, Buddhism (philosophy and psychology) and ethics in AI.

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