├── 01_PyTorch_Basic ├── 01_TensorDef.ipynb ├── 02_NonlinearFunc.ipynb ├── 03_NeuralNetworksBasic.ipynb ├── A-convolutional-neural-networks-CNN.png ├── LeNet.png └── MNIST │ ├── MNIST.py │ └── data │ ├── processed │ ├── test.pt │ └── training.pt │ └── raw │ ├── t10k-images-idx3-ubyte │ ├── t10k-labels-idx1-ubyte │ ├── train-images-idx3-ubyte │ └── train-labels-idx1-ubyte ├── 02_AlexNet ├── AlexNet.py ├── alexnet.pdf ├── readme.md └── train │ └── catdog.py ├── 03_Learning_PyTorch_with_Examples ├── README.md ├── c1_two_layer_net_numpy.ipynb ├── c2_autograd_two_layer_net_tensor.ipynb ├── c3_autograd_two_layer_net_autograd.ipynb ├── c4_autograd_two_layer_net_custom_function.ipynb ├── c5_autograd_two_layer_net.ipynb ├── c6_nn_two_layer_net_nn.ipynb ├── c7_nn_two_layer_net_optim.ipynb ├── c8_nn_two_layer_net_module.ipynb └── c9_nn_dynamic_net.ipynb ├── 04_RNN ├── .ipynb_checkpoints │ └── rnn_tutorial-checkpoint.ipynb ├── README.md ├── imgs │ ├── rnn.png │ └── rnn_weights.png └── rnn_tutorial.ipynb ├── 05_Deep_Residual_Network ├── main-gpu.py └── main.py ├── 06_Natural_Language_Processing ├── n-gram.py └── word-embedding.py ├── 07_Automated_Reasoning ├── 20180426talk.pdf └── code │ ├── fol_example │ └── sudoku.py ├── 08_DQN ├── DQN_cartPole.ipynb ├── Hello.ipynb └── reinforcement_q_learning.ipynb ├── 09_Attention_seq2seq ├── Attention.ipynb ├── Basic_concept.ipynb ├── README.md ├── basic_seq2seq.ipynb ├── config │ ├── __pycache__ │ │ ├── __init__.cpython-36.pyc │ │ └── config.cpython-36.pyc │ └── config.py ├── dataset │ ├── DataHelper.py │ ├── Google-10000-English.txt │ └── __pycache__ │ │ ├── DataHelper.cpython-36.pyc │ │ └── __init__.cpython-36.pyc ├── image │ ├── atten1.png │ ├── atten_flow.png │ ├── attention.png │ ├── image_caption.png │ ├── no_onehot.png │ ├── padd.png │ ├── s2vt.png │ └── seq2seq.png └── simple_S2VT.ipynb ├── 10_VAE ├── README.md ├── VAE_MNIST_PYTORCH.py └── data_read_in.py └── README.md /01_PyTorch_Basic/01_TensorDef.ipynb: 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