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网易云课堂终于官方发布了吴恩达经过授权的汉化课程-“”深度学习专项课程“”,这是自己做的一些笔记以及代码。下为网易云学习链接

deeplearning.ai-note

吴恩达在Coursera上推出的“深度学习专项课程“引起了一波AI学习热潮,而自发布以来,国内学习者对于课程汉化的呼声也从未停止。前些天,网易云课堂终于官方发布了经过授权的汉化课程

视频

  • (荐)哔哩哔哩up上传中英字幕课程:https://space.bilibili.com/288630933
  • 网易云视频教程地址:https://mooc.study.163.com/university/deeplearning_ai#/c
  • Coursera视频(收费,不过可以申请免费):https://www.coursera.org/specializations/deep-learning

笔记在线阅读http://www.ai-start.com/dl2017

深度学习专项课程(Deep Learning Specialization on Coursera)

Course 1. 神经网络和深度学习 Neural Networks and Deep Learning

  1. Week1 - [第一周:深度学习引言(Introduction to Deep Learning)]
  2. Week2 - [第二周:神经网络的编程基础(Basics of Neural Network programming)]
  3. Week3 - [第三周:浅层神经网络(Shallow neural networks)]
  4. Week4 - [第四周:深层神经网络(Deep Neural Networks)]

Course 2. 改善深层神经网络 Improving Deep Neural Networks Hyperparameter tuning, Regularization and Optimization

  1. Week1 - [第一周:深度学习的实用层面(Practical aspects of Deep Learning)] - Setting up your Machine Learning Application - Regularizing your neural network - Setting up your optimization problem
  2. Week2 - [第二周:优化算法 (Optimization algorithms)]
  3. Week3 - [第三周超参数调试,batch正则化和程序框架(Hyperparameter tuning, Batch Normalization and Programming Frameworks)]

Course 3. 结构化机器学习项目 Structuring Machine Learning Projects

  1. Week1 - [第一周:机器学习策略(1)(ML Strategy (1))] - Setting up your goal - Comparing to human-level performance
  2. Week2 - [第二周:机器学习策略(2)(ML Strategy (2))] - Error Analysis - Mismatched training and dev/test set - Learning from multiple tasks - End-to-end deep learning

Course 4. 卷积神经网络 Convolutional Neural Networks

  1. Week1 - [第一周 卷积神经网络(Foundations of Convolutional Neural Networks)]
  2. Week2 - [第二周 深度卷积网络:实例探究(Deep convolutional models: case studies)](
  3. Week3 - [第三周 目标检测(Object detection)]
  4. Week4 - [第四周 特殊应用:人脸识别和神经风格转换(Special applications: Face recognition &Neural style transfer)]

Course 5. 序列模型 Sequence Models

  1. Week1 - [Recurrent Neural Networks]
  2. Week2 - [Natural Language Processing & Word Embeddings]
  3. Week3 - [Sequence models & Attention mechanism]

目录结构

├─01神经网络和深度学习
│  ├─Code编程作业
│  └─Quiz测验题
├─02改善深度神经网络
│  ├─Code编程作业
│  └─Quiz测验题
├─03结构化机器学习项目
│  ├─Code编程作业
│  └─Quiz测验题
├─04卷积神经网络
│  ├─Code编程作业
│  └─Quiz测验题
├─05序列模型
│  ├─Code编程作业
│  └─Quiz测验题

可学习参考资料

  • 笔记在线阅读
  • http://blog.csdn.net/hdhuangzhihao
  • http://blog.csdn.net/justry24/article/category/7159918
  • http://blog.csdn.net/koala_tree/article/category/7186915
  • http://blog.csdn.net/weixin_37993251
  • http://blog.csdn.net/hongbin_xu/article/category/7323091
  • http://blog.csdn.net/junjun_zhao【有自己的注解】
  • https://blog.csdn.net/u013733326/article/details/79827273 别人汇总的作业博客

TO DO

持续更新中... ...

更多可参考

开源许可证 License MIT

  • 开源是一种精神,MachineLearning_Ng的开源更是人的一种进步
  • 开源是自由的,而不是免费的。Free(自由) is not free(免费) 请认真阅读并遵守以下开源协议 License MIT

此外,代码仅作学习深度学习专项课程(Deep Learning Specialization on Coursera)所用,代码和笔记禁止私用,违者必究


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