TensorFlow for Deep Learning 免积分下载

本书通过实际案例,教授TensorFlow的基础知识,涵盖从构建简单学习系统到使用深度网络处理图像、文本和视频的全过程,适合有线性代数和微积分背景的开发者。
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通过TensorFlow学习如何解决具有挑战性的机器学习问题,TensorFlow是Google革命性的深度学习新软件库。如果你有基本线性代数和微积分的背景知识,这本实用的书介绍了如何设计能够检测图像中的对象,理解文本,分析视频和预测潜在药物特性的系统的机器学习基础知识。

TensorFlow for Deep Learning通过实际示例教授概念,并帮助您从头开始构建深度学习基础知识。它非常适合具有设计软件系统经验的开发人员,对熟悉脚本的科学家和其他专业人员非常有用,但不一定与设计学习算法有关。

  • 学习TensorFlow基础知识,包括如何执行基本计算
  • 构建简单的学习系统,以了解他们的数学基础
  • 深入了解数千个应用程序中使用的完全连接的深度网络
  • 将原型转换为具有超参数优化的高质量模型
  • 使用卷积神经网络处理图像
  • 使用递归神经网络处理自然语言数据集
  • 使用强化学习来解决游戏,如井字游戏
  • 使用包括GPU和张量处理单元的硬件训练深度网络

下载地址:TensorFlow for Deep Learning

 

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TensorFlow 是由Google Brain 团队开发的开源机器学习框架,广泛应用于深度学习研究和生产环境。 它提供了一个灵活的平台,用于构建和训练各种机器学习模型

TensorFlow Machine Learning Cookbook by Nick McClure English | 14 Feb. 2017 | ISBN: 1786462168 | 370 Pages Key Features Your quick guide to implementing TensorFlow in your day-to-day machine learning activities Learn advanced techniques that bring more accuracy and speed to machine learning Upgrade your knowledge to the second generation of machine learning with this guide on TensorFlow Book Description TensorFlow is an open source software library for Machine Intelligence. The independent recipes in this book will teach yo u how to use TensorFlow for complex data computations and will let you dig deeper and gain more insights into your data than ever before. You'll work through recipes on training models, model evaluation, sentiment analysis, regression analysis, clustering analysis, artificial neural networks, and deep learning – each using Google's machine learning library TensorFlow. This guide starts with the fundamentals of the TensorFlow library which includes variables, matrices, and various data sources. Moving ahead, you will get hands-on experience with Linear Regression techniques with TensorFlow. The next chapters cover important high-level concepts such as neural networks, CNN, RNN, and NLP. Once you are familiar and comfortable with the TensorFlow ecosystem, the last chapter will show you how to take it to production. What you will learn Become familiar with the basics of the TensorFlow machine learning library Get to know Linear Regression techniques with TensorFlow Learn SVMs with hands-on recipes Implement neural networks and improve predictions Apply NLP and sentiment analysis to your data Master CNN and RNN through practical recipes Take TensorFlow into production About the Author Nick McClure is currently a senior data scientist at PayScale, Inc. in Seattle, WA. Prior to this, he has worked at Zillow and Caesar's Entertainment. He got his degrees in Applied Mathematics from The University of Montana and the College of Saint Benedict and Saint John's University. He has a passion for learning and advocating for analytics, machine learning, and artificial intelligence. Nick occasionally puts his thoughts and musings on his blog, http://fromdata.org/, or through his Twitter account, @nfmcclure. Table of Contents Getting Started with TensorFlow The TensorFlow Way Linear Regression Support Vector Machines Nearest Neighbor Methods Neural Networks Natural Language Processing Convolutional Neural Networks Recurrent Neural Networks Taking TensorFlow to Production More with TensorFlow
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