GitHub - joshhu/cht_pytorch_201910: 中華電信2019年10月份pytorch課程

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Name		Name	Last commit message	Last commit date
Latest commit History 25 Commits
.gitignore		.gitignore
00 - Pytorch環境介紹.pptx		00 - Pytorch環境介紹.pptx
01 - 1. PyTorch tensors.ipynb		01 - 1. PyTorch tensors.ipynb
01 - 2. Autograd.ipynb		01 - 2. Autograd.ipynb
01 - 3. 手刻Gradient descent.ipynb		01 - 3. 手刻Gradient descent.ipynb
01 - 4. Linear Regression with pytorch.ipynb		01 - 4. Linear Regression with pytorch.ipynb
01 - 5. Logistic Regression with Moons dataset.ipynb		01 - 5. Logistic Regression with Moons dataset.ipynb
01 - 6. Logistic Regression with Moons dataset(nn).ipynb		01 - 6. Logistic Regression with Moons dataset(nn).ipynb
01 - 7. Logistic Regression with Moons dataset(nn CUDA).ipynb		01 - 7. Logistic Regression with Moons dataset(nn CUDA).ipynb
01 - Pytorch元件介紹.pptx		01 - Pytorch元件介紹.pptx
02 - 1. MNIST Feedforward Neural Network(more layers).ipynb		02 - 1. MNIST Feedforward Neural Network(more layers).ipynb
02 - 1. MNIST Feedforward Neural Network.ipynb		02 - 1. MNIST Feedforward Neural Network.ipynb
02 - 2. MNIST Feedforward Neural Network with CUDA.ipynb		02 - 2. MNIST Feedforward Neural Network with CUDA.ipynb
02 - Neural Network.pptx		02 - Neural Network.pptx
03 - 1. CNN with Mnist.ipynb		03 - 1. CNN with Mnist.ipynb
03 - CNN.pptx		03 - CNN.pptx
04 - Pytorch神經網路技巧.pptx		04 - Pytorch神經網路技巧.pptx
05 - 1. 原始資料夾照比例分成augment, val, test.ipynb		05 - 1. 原始資料夾照比例分成augment, val, test.ipynb
05 - 2. Data Augmentation.ipynb		05 - 2. Data Augmentation.ipynb
05 - 3. Std and mean.ipynb		05 - 3. Std and mean.ipynb
05 - 4. Training.ipynb		05 - 4. Training.ipynb
05 - GAN.pptx		05 - GAN.pptx
07 - Convolve Lena.ipynb		07 - Convolve Lena.ipynb
D.pkl		D.pkl
DCGAN.ipynb		DCGAN.ipynb
Fashion_MNISTGAN.ipynb		Fashion_MNISTGAN.ipynb
G.pkl		G.pkl
MNISTGAN.ipynb		MNISTGAN.ipynb
MNIST_on_TPU(tf+keras).ipynb		MNIST_on_TPU(tf+keras).ipynb
d201.pth		d201.pth
fnn_model.pkl		fnn_model.pkl
lena.jpeg		lena.jpeg
pcm.py		pcm.py
tf20_Mnist.ipynb		tf20_Mnist.ipynb