TensorFlow 1.x Deep Learning Cookbook

Antonio Gulli, Amita Kapoor

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商品描述

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. 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

商品描述(中文翻譯)

在Tensorflow 1.x中實現各種常見和不太常見的神經網絡的下一步。

主要特點:
- 使用Google的TensorFlow 1.x提升技能並實現棘手的神經網絡。
- 一個易於跟隨的指南,讓您探索強化學習、生成對抗網絡(GANs)、自編碼器、多層感知器等。
- 實踐Tensorflow在桌面、移動和雲環境中的應用的實用配方。

書籍描述:
深度神經網絡(DNNs)在計算機視覺、語音識別和自然語言處理等領域取得了很大的成功。整個世界對於深度網絡如何革命人工智能充滿了興奮。這本令人興奮的基於配方的指南將帶您從DNN理論的領域實際實現它們,以解決人工智能領域中的實際問題。

在本書中,您將學習如何有效使用TensorFlow,這是Google的開源深度學習框架。您將使用簡單易懂的獨立配方實現不同的深度學習網絡,如卷積神經網絡(CNNs)、循環神經網絡(RNNs)、深度Q學習網絡(DQNs)和生成對抗網絡(GANs)。您還將學習如何將Keras作為TensorFlow的後端。

通過問題解決的方法,您將了解如何在工作中實現不同的深度神經架構來執行複雜的任務。您將了解不同DNNs在一些常用數據集(如MNIST、CIFAR-10、Youtube8m等)上的性能。您不僅將了解TensorFlow支持的不同移動和嵌入式平台,還將了解如何為深度學習應用程序設置雲平台。先睹為快,了解TPU的一瞥。