├── Notes ├── CS231n_Notes │ ├── Module 0-Python Numpy Tutorial.pdf │ ├── Module 1-1-Image Classification Data-driven Approach, k-Nearest Neighbor, trainvaltest splits.pdf │ ├── Module 1-2-Linear classification Support Vector Machine, Softmax I.pdf │ ├── Module 1-3-Optimization Stochastic Gradient Descent.pdf │ ├── Module 1-4-Backpropagation, Intuitions.pdf │ ├── Module 1-5-Neural Networks Part 1 Setting up the Architecture.pdf │ ├── Module 1-6-Neural Networks Part 2 Setting up the Data and the Loss.pdf │ ├── Module 1-7-Neural Networks Part 3 Learning and Evaluation.pdf │ ├── Module 1-8-Putting it together Minimal Neural Network Case Study.pdf │ ├── Module 2-1-Convolutional Neural Networks Architectures, Convolution Pooling Layers.pdf │ ├── Module 2-2-Understanding and Visualizing Convolutional Neural Networks.pdf │ └── Module 2-3-Transfer Learning and Fine-tuning Convolutional Neural Networks.pdf └── CS231n_中文 │ ├── CS231n_中文 │ ├── CS231n课程笔记翻译：Python Numpy教程 - 知乎专栏.pdf │ ├── CS231n课程笔记翻译：卷积神经网络笔记 - 知乎专栏.pdf │ ├── CS231n课程笔记翻译：反向传播笔记 - 知乎专栏.pdf │ ├── CS231n课程笔记翻译：图像分类笔记（上） - 知乎专栏.pdf │ ├── CS231n课程笔记翻译：图像分类笔记（下） - 知乎专栏.pdf │ ├── CS231n课程笔记翻译：最优化笔记（上） - 知乎专栏.pdf │ ├── CS231n课程笔记翻译：最优化笔记（下） - 知乎专栏.pdf │ ├── CS231n课程笔记翻译：神经网络笔记 2 - 知乎专栏.pdf │ ├── CS231n课程笔记翻译：神经网络笔记1（上） - 知乎专栏.pdf │ ├── CS231n课程笔记翻译：神经网络笔记1（下） - 知乎专栏.pdf │ ├── CS231n课程笔记翻译：神经网络笔记3（上） - 知乎专栏.pdf │ ├── CS231n课程笔记翻译：神经网络笔记3（下） - 知乎专栏.pdf │ ├── CS231n课程笔记翻译：线性分类笔记（上） - 知乎专栏.pdf │ ├── CS231n课程笔记翻译：线性分类笔记（下） - 知乎专栏.pdf │ ├── CS231n课程笔记翻译：线性分类笔记（中） - 知乎专栏.pdf │ ├── Stochastic Gradient Descent Tricks.pdf │ ├── 斯坦福CS231n课程作业# 1简介 - 知乎专栏.pdf │ ├── 斯坦福CS231n课程作业# 2简介 - 知乎专栏.pdf │ └── 斯坦福CS231n课程作业# 3简介 - 知乎专栏.pdf │ └── __MACOSX │ ├── ._CS231n_中文 │ └── CS231n_中文 │ ├── ._CS231n课程笔记翻译：Python Numpy教程 - 知乎专栏.pdf │ ├── ._CS231n课程笔记翻译：卷积神经网络笔记 - 知乎专栏.pdf │ ├── ._CS231n课程笔记翻译：反向传播笔记 - 知乎专栏.pdf │ ├── ._CS231n课程笔记翻译：图像分类笔记（上） - 知乎专栏.pdf │ ├── ._CS231n课程笔记翻译：图像分类笔记（下） - 知乎专栏.pdf │ ├── ._CS231n课程笔记翻译：最优化笔记（上） - 知乎专栏.pdf │ ├── ._CS231n课程笔记翻译：最优化笔记（下） - 知乎专栏.pdf │ ├── ._CS231n课程笔记翻译：神经网络笔记 2 - 知乎专栏.pdf │ ├── ._CS231n课程笔记翻译：神经网络笔记1（上） - 知乎专栏.pdf │ ├── ._CS231n课程笔记翻译：神经网络笔记1（下） - 知乎专栏.pdf │ ├── ._CS231n课程笔记翻译：神经网络笔记3（上） - 知乎专栏.pdf │ ├── ._CS231n课程笔记翻译：神经网络笔记3（下） - 知乎专栏.pdf │ ├── ._CS231n课程笔记翻译：线性分类笔记（上） - 知乎专栏.pdf │ ├── ._CS231n课程笔记翻译：线性分类笔记（下） - 知乎专栏.pdf │ ├── ._CS231n课程笔记翻译：线性分类笔记（中） - 知乎专栏.pdf │ ├── ._Stochastic Gradient Descent Tricks.pdf │ ├── ._斯坦福CS231n课程作业# 1简介 - 知乎专栏.pdf │ ├── ._斯坦福CS231n课程作业# 2简介 - 知乎专栏.pdf │ └── ._斯坦福CS231n课程作业# 3简介 - 知乎专栏.pdf ├── README.md ├── Slides ├── Stanford University CS231n_ Convolutional Neural Networks for Visual Recognition.pdf ├── winter1516_lecture1.pdf ├── winter1516_lecture10.pdf ├── winter1516_lecture11.pdf ├── winter1516_lecture12.pdf ├── winter1516_lecture13.pdf ├── winter1516_lecture14.pdf ├── winter1516_lecture2.pdf ├── winter1516_lecture3.pdf ├── winter1516_lecture4.pdf ├── winter1516_lecture5.pdf ├── winter1516_lecture6.pdf ├── winter1516_lecture7.pdf ├── winter1516_lecture8.pdf └── winter1516_lecture9.pdf ├── assignment1 ├── .gitignore ├── .ipynb_checkpoints │ ├── features-checkpoint.ipynb │ ├── knn-checkpoint.ipynb │ ├── softmax-checkpoint.ipynb │ ├── svm-checkpoint.ipynb │ └── two_layer_net-checkpoint.ipynb ├── README.md ├── collectSubmission.sh ├── cs231n │ ├── __init__.py │ ├── classifiers │ │ ├── __init__.py │ │ ├── k_nearest_neighbor.py │ │ ├── linear_classifier.py │ │ ├── linear_svm.py │ │ ├── neural_net.py │ │ └── softmax.py │ ├── data_utils.py │ ├── datasets │ │ ├── .gitignore │ │ └── get_datasets.sh │ ├── features.py │ ├── gradient_check.py │ └── vis_utils.py ├── features.ipynb ├── frameworkpython ├── knn.ipynb ├── requirements.txt ├── softmax.ipynb ├── start_ipython_osx.sh ├── svm.ipynb └── two_layer_net.ipynb ├── assignment2 ├── .gitignore ├── .ipynb_checkpoints │ ├── BatchNormalization-checkpoint.ipynb │ ├── ConvolutionalNetworks-checkpoint.ipynb │ ├── Dropout-checkpoint.ipynb │ └── FullyConnectedNets-checkpoint.ipynb ├── BatchNormalization.ipynb ├── ConvolutionalNetworks.ipynb ├── Dropout.ipynb ├── FullyConnectedNets.ipynb ├── README.md ├── collectSubmission.sh ├── cs231n │ ├── .gitignore │ ├── __init__.py │ ├── classifiers │ │ ├── __init__.py │ │ ├── cnn.py │ │ ├── convnet.py │ │ └── fc_net.py │ ├── data_utils.py │ ├── datasets │ │ ├── .gitignore │ │ └── get_datasets.sh │ ├── fast_layers.py │ ├── gradient_check.py │ ├── im2col.py │ ├── im2col_cython.pyx │ ├── layer_utils.py │ ├── layers.py │ ├── optim.py │ ├── setup.py │ ├── solver.py │ └── vis_utils.py ├── frameworkpython ├── kitten.jpg ├── puppy.jpg ├── requirements.txt └── start_ipython_osx.sh └── assignment3 ├── .gitignore ├── .ipynb_checkpoints ├── ImageGeneration-checkpoint.ipynb ├── ImageGradients-checkpoint.ipynb ├── LSTM_Captioning-checkpoint.ipynb └── RNN_Captioning-checkpoint.ipynb ├── ImageGeneration.ipynb ├── ImageGradients.ipynb ├── LSTM_Captioning.ipynb ├── RNN_Captioning.ipynb ├── collectSubmission.sh ├── cs231n ├── .gitignore ├── __init__.py ├── captioning_solver.py ├── classifiers │ ├── __init__.py │ ├── pretrained_cnn.py │ └── rnn.py ├── coco_utils.py ├── data_utils.py ├── datasets │ ├── get_coco_captioning.sh │ ├── get_pretrained_model.sh │ └── get_tiny_imagenet_a.sh ├── fast_layers.py ├── gradient_check.py ├── im2col.py ├── im2col_cython.pyx ├── image_utils.py ├── layer_utils.py ├── layers.py ├── lstm_test.py ├── optim.py ├── rnn_layers.py └── setup.py ├── frameworkpython ├── kitten.jpg ├── lstm_test.py ├── requirements.txt ├── sky.jpg ├── start_ipython_osx.sh └── timg.jpg /Notes/CS231n_Notes/Module 0-Python Numpy Tutorial.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JimLee4530/CS231n/HEAD/Notes/CS231n_Notes/Module 0-Python Numpy Tutorial.pdf -------------------------------------------------------------------------------- /Notes/CS231n_Notes/Module 1-1-Image Classification 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