▍1. 深度学习花书
说明： 深度学习领域文献，简称花书，对初学者有很大的参考价值。(Documents in the field of in-depth learning, referred to as flower books, have great reference value for beginners.)
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▍2. tf2img
读取文件夹下的图片，缩放分辨率，存为tensorflow数据集tf格式，以制备数据集，方便之后直接读取，同时提供了tf数据集转普通img和numpy格式(Read the picture under the folder, zoom the resolution, save it as tensorflow data set TF format, to prepare data set, easy to read directly after.)
▍3. tf2img
说明： 读取文件夹下的图片，缩放分辨率，存为tensorflow数据集tf格式，以制备数据集，方便之后直接读取，同时提供了tf数据集转普通img和numpy格式(Read the picture under the folder, zoom the resolution, save it as tensorflow data set TF format, to prepare data set, easy to read directly after.)
▍4. TensorFlow-Examples-master
机器学习算法基于TensorFlow2.0的实现(Implementation of machine learning Based on TensorFlow 2.0)
▍5. TensorFlow-Examples-master
说明： 机器学习算法基于TensorFlow2.0的实现(Implementation of machine learning Based on TensorFlow 2.0)
▍6. AI
说明： 一个简单的人工智能神经网络，训练一段时间后可以自动接球，但是需要的硬件性能比较强10系显卡比较好，需要安装python3.6.5 ,tensorflow,等，不会安装找我，我有教程。(A simple artificial intelligence neural network, after training for a period of time, can automatically catch the ball, but the required hardware performance is stronger than 10 series graphics card is better, need to install Python 3.6.5, tensorflow, etc., will not install to find me, I have a tutorial.)
▍7. 《Python+Cookbook》第三版中文v2.0.0
用于学习python与cookbook的一本书，适用于python小伙伴(A book for learning Python and cookbook, for Python buddies)
▍8. 《Python+Cookbook》第三版中文v2.0.0
说明： 用于学习python与cookbook的一本书，适用于python小伙伴(A book for learning Python and cookbook, for Python buddies)
▍9. 《Python标准库》中文版
python标准库，用于学习python的小伙伴(Python standard library)
▍10. 《Python标准库》中文版
说明： python标准库，用于学习python的小伙伴(Python standard library)
▍11. 《深度学习入门：基于Python的理论与实现》
本书主要介绍深度学习的核心算法，以及在计算机视觉，语音识别、自然语言处理中的相关应用，适合深度学习从业人士或者相关研究生作为参考资料，也可以作为入门教程来大致了解深度学习的相关前沿技术(This book mainly introduces the core algorithms of deep learning, as well as relevant applications in computer vision, speech recognition and natural language processing. It is suitable for practitioners of deep learning or relevant graduate students as reference materials, and can also be used as an introductory course to roughly understand the cutting-edge technologies of deep learning)
▍12. 《深度学习入门：基于Python的理论与实现》
说明： 本书主要介绍深度学习的核心算法，以及在计算机视觉，语音识别、自然语言处理中的相关应用，适合深度学习从业人士或者相关研究生作为参考资料，也可以作为入门教程来大致了解深度学习的相关前沿技术(This book mainly introduces the core algorithms of deep learning, as well as relevant applications in computer vision, speech recognition and natural language processing. It is suitable for practitioners of deep learning or relevant graduate students as reference materials, and can also be used as an introductory course to roughly understand the cutting-edge technologies of deep learning)
▍13. Tradition_dic
一个简单的传统自然语言处理的小示例，包括Jieba分词和朴素贝叶斯分类算法。(A simple example of natural language processing includes Jieba word segmentation and Naive Bayesian classification algorithm.)
▍14. Tradition_dic
说明： 一个简单的传统自然语言处理的小示例，包括Jieba分词和朴素贝叶斯分类算法。(A simple example of natural language processing includes Jieba word segmentation and Naive Bayesian classification algorithm.)
▍15. MasteringMachineLearningWithscikit-learn
在这本书里，我们将看到一些机器学习的模型和算法。我们会介绍一些常用的机器学习任务和模型的效果评估方法。而这些模型和算法都是通过十分流行的Python机器学习库scikit-learn来完成，里面有许多机器学习的模型和算法，每个API都简单易用(In this book, we will look at some machine learning models and algorithms.We will introduce some commonly used methods for evaluating the effects of machine learning tasks and models.These models and algorithms are built using the popular Python machine learning library scikit-learn, which has many machine learning models and algorithms, and each API is easy to use)
▍16. MasteringMachineLearningWithscikit-learn
说明： 在这本书里，我们将看到一些机器学习的模型和算法。我们会介绍一些常用的机器学习任务和模型的效果评估方法。而这些模型和算法都是通过十分流行的Python机器学习库scikit-learn来完成，里面有许多机器学习的模型和算法，每个API都简单易用(In this book, we will look at some machine learning models and algorithms.We will introduce some commonly used methods for evaluating the effects of machine learning tasks and models.These models and algorithms are built using the popular Python machine learning library scikit-learn, which has many machine learning models and algorithms, and each API is easy to use)
▍17. dlbook_cn_v0.5-beta
(中文版)深度学习(Deep Learning)是由Yoshua Bengio推出的机器学习领域的教科书，以开源的形式免费在网络上提供，主要面向学习机器学习的学生和研究者，以及没有机器学习背景但希望能快速地掌握这方面知识并在他们的产品中使用的软件工程师。(Deep Learning by Yoshua Bengio launched a textbook in the field of machine Learning, in the form of open source provided for free on the Internet, mainly for Learning machine Learning of students and researchers, and the lack of machine Learning background but hope to be able to quickly grasp the knowledge and use of software engineer in their products.)
▍18. dlbook_cn_v0.5-beta
说明： (中文版)深度学习(Deep Learning)是由Yoshua Bengio推出的机器学习领域的教科书，以开源的形式免费在网络上提供，主要面向学习机器学习的学生和研究者，以及没有机器学习背景但希望能快速地掌握这方面知识并在他们的产品中使用的软件工程师。(Deep Learning by Yoshua Bengio launched a textbook in the field of machine Learning, in the form of open source provided for free on the Internet, mainly for Learning machine Learning of students and researchers, and the lack of machine Learning background but hope to be able to quickly grasp the knowledge and use of software engineer in their products.)
▍19. 李宏毅—1天搞懂深度学习
本文是2016 台湾资料科学年会前导课程“一天搞懂深度学习”的全部讲义PPT（共268页），由台湾大学电机工程学助理教授李宏毅主讲。作者在文中分四个部分对神经网络的原理、目前存在形态以及未来的发展进行了介绍。深度学习的每一个核心概念在文中都有相关案例进行呈现，通俗易懂。一天的时间搞懂深度学习？其实并不是没有可能。(This is the entire handout PPT (268 pages in total) of "a day to understand deep learning", the leading course of the 2016 Taiwan annual conference of data science. It was given by li hongyi, assistant professor of electrical engineering at Taiwan university.In this paper, the author introduces the principle, existing form and future development of neural network in four parts.Each core concept of deep learning is presented in relevant cases, which are easy to understand.)
▍20. 李宏毅—1天搞懂深度学习
说明： 本文是2016 台湾资料科学年会前导课程“一天搞懂深度学习”的全部讲义PPT（共268页），由台湾大学电机工程学助理教授李宏毅主讲。作者在文中分四个部分对神经网络的原理、目前存在形态以及未来的发展进行了介绍。深度学习的每一个核心概念在文中都有相关案例进行呈现，通俗易懂。一天的时间搞懂深度学习？其实并不是没有可能。(This is the entire handout PPT (268 pages in total) of "a day to understand deep learning", the leading course of the 2016 Taiwan annual conference of data science. It was given by li hongyi, assistant professor of electrical engineering at Taiwan university.In this paper, the author introduces the principle, existing form and future development of neural network in four parts.Each core concept of deep learning is presented in relevant cases, which are easy to understand.)
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