TensorFlow, a popular library for machine learning, embraces the innovation and community-engagement of open source, but has the support, guidance, and stability of a large corporation. Because of its multitude of strengths, TensorFlow is appropriate for individuals and businesses ranging from startups to companies as large as, well, Google. TensorFlow is currently being used for natural language processing, artificial intelligence, computer vision, and predictive analytics. TensorFlow, open sourced to the public by Google in November 2015, was made to be flexible, efficient, extensible, and portable. Computers of any shape and size can run it, from smartphones all the way up to huge computing clusters. This book is for anyone who knows a little machine learning (or not) and who has heard about TensorFlow, but found the documentation too daunting to approach. It introduces the TensorFlow framework and the underlying machine learning concepts that are important to harness machine intelligence. After reading this book, you should have a deep understanding of the core TensorFlow API.
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本书带领读者在本地机器建立可执行的tensorflow环境,手把手在jupyter notebook上教读者hack代码从而比较全面的理解tensorflow的基本概念,对于初学tensorflow的同行来说是一本上手比较容易的教材。 第5章开始讲Deep Learning(从经典的CNN开始)时, 书本的内容, 包括文字和代码的执行结果都在jupyter上,此时从jupyter上学习显然是更方便的。
评分本书带领读者在本地机器建立可执行的tensorflow环境,手把手在jupyter notebook上教读者hack代码从而比较全面的理解tensorflow的基本概念,对于初学tensorflow的同行来说是一本上手比较容易的教材。 第5章开始讲Deep Learning(从经典的CNN开始)时, 书本的内容, 包括文字和代码的执行结果都在jupyter上,此时从jupyter上学习显然是更方便的。
评分本书带领读者在本地机器建立可执行的tensorflow环境,手把手在jupyter notebook上教读者hack代码从而比较全面的理解tensorflow的基本概念,对于初学tensorflow的同行来说是一本上手比较容易的教材。 第5章开始讲Deep Learning(从经典的CNN开始)时, 书本的内容, 包括文字和代码的执行结果都在jupyter上,此时从jupyter上学习显然是更方便的。
评分注意RNN和Deploy的内容,Code Snippets,Next steps and additional resources.
评分本书带领读者在本地机器建立可执行的tensorflow环境,手把手在jupyter notebook上教读者hack代码从而比较全面的理解tensorflow的基本概念,对于初学tensorflow的同行来说是一本上手比较容易的教材。 第5章开始讲Deep Learning(从经典的CNN开始)时, 书本的内容, 包括文字和代码的执行结果都在jupyter上,此时从jupyter上学习显然是更方便的。
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