TensorFlow 1.x Deep Learning Cookbook: Over 90 unique recipes to solve artificial-intelligence driven problems with Python
By 作者: Antonio Gulli – Amita Kapoor
ISBN-10 书号: 1788293592
ISBN-13 书号: 9781788293594
Release Finelybook 出版日期: 2017-12-12
pages 页数: (536 )

$44.99

Book Description to Finelybook sorting

Deep neural networks (DNNs) have achieved a lot of success in the field of computer vision, speech recognition, and natural language processing. The entire world is filled with excitement about how deep networks are revolutionizing artificial intelligence. This exciting recipe-based guide will take you from the realm of DNN theory to implementing them practically to solve the real-life problems in 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 independent recipes. You will learn how to make Keras as backend with TensorFlow.

With a problem-solution approach, you will understand how to implement different deep neural architectures to carry out complex tasks at work. You will learn the performance of different DNNs on some popularly used data sets such as MNIST, CIFAR-10, Youtube8m, and more. 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. Get a sneak peek of TPU architecture and how they will affect DNN future.

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, autoencoders and more.

Contents
1: TENSORFLOW – AN INTRODUCTION
2: REGRESSION
3: NEURAL NETWORKS – PERCEPTRON
4: CONVOLUTIONAL NEURAL NETWORKS
5: ADVANCED CONVOLUTIONAL NEURAL NETWORKS
6: RECURRENT NEURAL NETWORKS
7: UNSUPERVISED LEARNING
8: AUTOENCODERS
9: REINFORCEMENT LEARNING
10: MOBILE COMPUTATION
11: GENERATIVE MODELS AND CAPSNET
12: DISTRIBUTED TENSORFLOW AND CLOUD DEEP LEARNING
13: LEARNING TO LEARN WITH AUTOML (META-LEARNING)
14: TENSORFLOW PROCESSING UNITS
What You Will Learn
Install TensorFlow and use it for CPU and GPU operations
Implement DNNs and apply them to solve different AI-driven problems.
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 classification problems
Build single and multilayer perceptrons in TensorFlow
Implement CNN and RNN in TensorFlow, and use it to solve real-world use cases.
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 to implement game playing agents.
GANs and their implementation using TensorFlow.
Authors
Antonio Gulli
Antonio Gulli is a software executive and business leader with a passion for establishing and managing global technological talent, 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 has managed people in six different countries in Europe and America. Antonio served as CEO, GM, CTO, VP, director, and site lead in multiple fields ranging from publishing (Elsevier) to consumer internet (Ask.com and Tiscali) and high-tech R&D (Microsoft and Google).

Amita Kapoor
Amita Kapoor, is Associate Professor in the Department of Electronics, SRCASW, University of Delhi. She has been actively teaching neural networks for the last twenty years. She did her Masters in Electronics in 1996, and her PhD in 2011. During the course of her PhD she was awarded prestigious DAAD fellowship to pursue a part of her research work in Karlsruhe Institute of Technology, Karlsruhe, Germany. She had been awarded best Presentation Award at the Photonics 2008 international conference 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). She has more than 40 publications in international journals and conferences. Her present research areas include Machine Learning, Artificial Intelligence, Neural Networks, Photonics, and Robotics.

She is the Co-author of Tensorflow Deep Learning 1.x Cookbook.

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TensorFlow 1.x Deep Learning Cookbook
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