掌握TensorFlow机器学习实战:2017版高级教程

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《Packt.TensorFlow.Machine.Learning.Cookbook.2017》是一本专为机器学习从业者编写的实用指南,作者是尼克·麦克卢尔(Nick McClure),他是一位经验丰富的数据科学家。该书以TensorFlow,一个开源的机器智能软件库,为核心,提供了深度的教程和实战项目,旨在帮助读者在日常机器学习任务中有效地应用TensorFlow。 本书共分为11章,从基础入门开始,逐步深入到高级技术。第一章介绍如何入门TensorFlow,包括声明张量、使用占位符和变量,以及操作矩阵的基本概念。第二章则探讨了TensorFlow的工作方式,帮助读者理解其背后的逻辑和设计理念。 第三章至第五章涉及线性回归、支持向量机(SVM)和最近邻方法等基础机器学习模型,通过实际操作让读者掌握这些技术。第六章至第九章涵盖了神经网络、自然语言处理(NLP)、卷积神经网络(CNN)和循环神经网络(RNN)等深度学习技术,这些都是提升模型准确性和速度的关键所在。 最后一章(第十章)重点讨论将TensorFlow应用于生产环境,指导读者如何将学到的知识转化为实际项目的部署策略。作者尼克·麦克卢尔结合自身在数据分析和人工智能领域的经验,分享了实用技巧和见解,并鼓励读者在博客(<http://fromdata.org/>)和Twitter(@nfmcclure)上持续学习和交流。 《TensorFlow Machine Learning Cookbook》适合希望提高TensorFlow技能,无论是初学者还是有一定经验的开发者,都能从中获益匪浅。全书共401页,以英文编写,由Packt Publishing于2017年3月6日出版,适合那些希望通过实践和实例学习最新一代机器学习技术的读者阅读。通过这本书,读者不仅能掌握基础的TensorFlow知识,还能深入了解并运用到诸如深度学习、自然语言处理等高级领域。
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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