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All Projects → ionvision → Deeplearningcoursecodes

ionvision / Deeplearningcoursecodes

Licence: apache-2.0
Notes, Codes, and Tutorials for the Deep Learning Course <which I taught at ChinaHadoop>

Labels

jupyter-notebookdeep-learningpytorchtensorflowtutorial

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Deep Learning Course Codes

Notes, Codes, and Tutorials for the Deep Learning Course at ChinaHadoop

注意每一份代码分别有Jupyter Notebook, Python, 以及HTML三种形式，大家可以按照自己的需求阅读，学习或运行。 运行时需要注意anaconda的版本问题，anaconda2-5.0.0与anaconda3-5.0.0分别对应python2.7与python3.6环境。

重要参考资料：

  1. Stanford CS229 Machine Learning, Fall 2017
  2. Deep Learning Book读书笔记
  3. Hands-on Machine Learning with Scikit-Learn and TensorFlow [book]

学习资料：

  1. Effective TensorFlow - TensorFlow tutorials and best practices.
  2. Finch - Many Machine Intelligence models implemented (mainly tensorflow, sometimes pytorch / mxnet)
  3. Pytorch Tutorials - PyTorch Tutorial for Deep Learning Researchers.
  4. MXNet the straight dope - An interactive book on deep learning. Much easy, so MXNet. Wow.

第一讲：深度学习课程总览与神经网络入门

代码示例：TensorFlow基础与线性回归模型(TensorFlow, PyTorch)

第二讲：传统神经网络

代码示例：K近邻算法，线性分类，以及多层神经网络(TensorFlow, PyTorch)

第三讲：卷积神经网络基础

代码示例：卷积神经网络的基础实现(TensorFlow)

第四讲：卷积神经网络进阶

代码示例：卷积神经网络的进阶实现(TensorFlow)

第五讲：深度神经网络：目标分类与识别

代码示例：深度神经网络-图像识别与分类(TensorFlow, PyTorch)

pip install git+https://github.com/zsdonghao/tensorlayer.git

conda install -c menpo opencv3 
或
pip install opencv-python
  • 所需数据集下载：data.zip: [微云][百度云] (覆盖./05_Image_recognition_and_classification/data文件夹)  
  • 所需模型下载： vgg19.npz  [微云][百度云] (放置于./05_Image_recognition_and_classification文件夹下)  
  • 所需模型下载：inception_v3.ckpt [微云][百度云] (放置于./05_Image_recognition_and_classification文件夹下)

第六讲：深度神经网络：目标检测与定位

代码示例：目标检测模型示例 (TensorFlow, PyTorch)

第七讲：深度神经网络：目标追踪与目标分割

代码示例：目标追踪与目标分割

第八讲：循环神经网络与序列模型

代码示例：循环神经网络

第九讲：无监督式学习与生成对抗网络

代码示例：生成对抗网络

第十讲：强化学习

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