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Notes, Codes, and Tutorials for the Deep Learning Course <which I taught at ChinaHadoop>
| Date | Stars |
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| 2026-07-24 | 262 |
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| 2026-08-06 | 262 |
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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](http://cs229.stanford.edu/) > 1. [Deep Learning Book读书笔记](https://github.com/exacity/simplified-deeplearning.git) > 1. [Hands-on Machine Learning with Scikit-Learn and TensorFlow](https://github.com/ageron/handson-ml) [[book](https://www.amazon.com/_/dp/1491962291?tag=oreilly20-20)] > 学习资料： > 1. [Effective TensorFlow](https://github.com/vahidk/EffectiveTensorflow) - TensorFlow tutorials and best practices. > 1. [Finch](https://github.com/zhedongzheng/finch) - Many Machine Intelligence models implemented (mainly tensorflow, sometimes pytorch / mxnet) > 1. [Pytorch Tutorials](https://github.com/yunjey/pytorch-tutorial) - PyTorch Tutorial for Deep Learning Researchers. > 1. [MXNet the straight dope](https://github.com/zackchase/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](https://github.com/jastarex/DeepLearningCourseCodes/tree/master/01_TF_basics_and_linear_regression)(TensorFlow, PyTorch) > - [MNIST数据集演示](https://github.com/jastarex/DeepLearningCourseCodes/blob/master/01_TF_basics_and_linear_regression/mnist_data_introduction_tf.ipynb) > - [TensorFlow基础](https://github.com/jastarex/DeepLearningCourseCodes/blob/master/01_TF_basics_and_linear_regression/tensorflow_basic.ipynb) > - [线性回归模型-TensorFlow](https://github.com/jastarex/DeepLearningCourseCodes/blob/master/01_TF_basics_and_linear_regression/linear_regression_tf.ipynb) > - [线性回归模型-PyTorch](https://github.com/jastarex/DeepLearningCourseCodes/blob/master/01_TF_basics_and_linear_regression/linear_regression_pt.ipynb) > - [线性回归模型-MXNet](https://github.com/jastarex/DeepLearningCourseCodes/blob/master/01_TF_basics_and_linear_regression/linear_regression_mx.ipynb) (contributed by [LinkHS](https://github.com/LinkHS)) ### 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](https://github.com/jastarex/DeepLearningCourseCodes/tree/master/02_Logistic_regression_and_multilayer_perceptron)(TensorFlow, PyTorch) > - [K近邻算法在图像分类上的应用-TensorFlow](https://github.com/jastarex/DeepLearningCourseCodes/blob/master/02_Logistic_regression_and_multilayer_perceptron/nearest_neighbor_tf.ipynb) > - [K近邻算法在图像分类上的应用-PyTorch](https://github.com/jastarex/DeepLearningCourseCodes/blob/master/02_Logistic_regression_and_multilayer_perceptron/nearest_neighbor_pt.ipynb) (contributed by Johnny Chen) > - [多层神经网络示例-TensorFlow](https://github.com/jastarex/DeepLearningCourseCodes/blob/master/02_Logistic_regression_and_multilayer_perceptron/neural_network_tf.ipynb) > - [多层神经网络示例-PyTorch](https://github.com/jastarex/DeepLearningCourseCodes/blob/master/02_Logistic_regression_and_multilayer_perceptron/neural_network_pt.ipynb) ### 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](https://github.com/jastarex/DeepLearningCourseCodes/tree/master/03_CNN_basics)(TensorFlow) > - [卷积神经网络基础示例-原生实现](https://github.com/jastarex/DeepLearningCourseCodes/blob/master/03_CNN_basics/cnn_tf_raw.ipynb) > - [卷积神经网络基础示例-主流实现](https://github.com/jastarex/DeepLearningCourseCodes/blob/master/03_CNN_basics/cnn_tf.ipynb) ### 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](https://github.com/jastarex/DeepLearningCourseCodes/tree/master/04_CNN_advances)(TensorFlow) > - [卷积神经网络进阶示例与可视化](https://github.com/jastarex/DeepLearningCourseCodes/blob/master/04_CNN_advances/cnn_mnist_simple.ipynb) > - [卷积神经网络进阶示例-TF-Slim实现](https://github.com/jastarex/DeepLearningCourseCodes/blob/master/04_CNN_advances/cnn_mnist_modern.ipynb) > - [准备自定义数据集训练卷积神经网络](https://github.com/jastarex/DeepLearningCourseCodes/blob/master/04_CNN_advances/basic_gendataset.ipynb) > - [使用自定义数据集训练卷积神经网络](https://github.com/jastarex/DeepLearningCourseCodes/blob/master/04_CNN_advances/cnn_custom_simple
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