Google发布的第二代深度学习系统TensorFlow

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在Jupyter Notebook环境中使用Python和TensorFlow 2.0创建、执行、修改和共享机器学习应用程序。这本书打破了编程机器学习应用程序的任何障碍,通过使用Jupyter Notebook而不是文本编辑器或常规IDE。

您将从学习如何使用Jupyter笔记本来改进使用Python编程的方式开始。在获得一个良好的基础与Python工作在木星的笔记本,你将深入什么是TensorFlow,它如何帮助机器学习爱好者,以及如何解决它提出的挑战。在此过程中,使用Jupyter笔记本创建的示例程序允许您应用本书前面的概念。

那些刚接触机器学习的人可以通过这些简单的程序来学习基本技能。本书末尾的术语表提供了常见的机器学习和Python关键字和定义,使学习更加容易。

你将学到什么

程序在Python和TensorFlow 解决机器学习的基本障碍 在Jupyter Notebook环境中发展

这本书是给谁的

理想的机器学习和深度学习爱好者谁对Python编程感兴趣使用Tensorflow 2.0在Jupyter 笔记本应用程序。了解一些机器学习概念和Python编程(使用Python version 3)的基本知识会很有帮助。

http://file.allitebooks.com/20200923/Machine%20Learning%20Concepts%20with%20Python%20and%20the%20Jupyter%20Notebook%20Environment.pdf

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The world is going through one of the most dangerous pandemics of all time with the rapid spread of the novel coronavirus (COVID-19). According to the World Health Organisation, the most effective way to thwart the transmission of coronavirus is to wear medical face masks. Monitoring the use of face masks in public places has been a challenge because manual monitoring could be unsafe. This paper proposes an architecture for detecting medical face masks for deployment on resource-constrained endpoints having extremely low memory footprints. A small development board with an ARM Cortex-M7 microcontroller clocked at 480 Mhz and having just 496 KB of framebuffer RAM, has been used for the deployment of the model. Using the TensorFlow Lite framework, the model is quantized to further reduce its size. The proposed model is 138 KB post quantization and runs at the inference speed of 30 FPS.

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