thinking-in-tensors-writing-in-pytorch
Thinking in tensors, writing in PyTorch (a hands-on deep learning intro)
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최종 버전 다운로드 (.zip)- FUNDING.yml
- Convolutions.ipynb
- Data augmentation.ipynb
- Image classification.ipynb
- Transfer learning.ipynb
- Using an ImageNet-pretrained model.ipynb
- Animals.csv
- Heart_rate_and_weight.csv
- imagenet_classes.csv
- imagenet_sota_paperswithcode.png
- squeezenet_fox_kerasjs.png
- CIFAR.ipynb
- CPU vs GPU.ipynb
- Matrix exercise.ipynb
- Operation playground.ipynb
- dog.jpeg
- linreg_setosa.png
- matrix_factorization_city_temperature.png
- 1 RNN architecture overview.ipynb
- 2 Bracket grammar.ipynb
- 3 Embedding vs one-hot encoding.ipynb
- 4 LSTM GRU anatomy.ipynb
- Fake logs.ipynb
- Names gender 1.ipynb
- Names gender 2.ipynb
- OpenAI bot.ipynb
- Text generation.ipynb
- Transformer example.ipynb
- Word vectors.ipynb
- .gitignore
- 0 Before you start.ipynb
- 1 Vectors, matrices and tensors.ipynb
- 1_tech PyTorch aritmetics.ipynb
- 2 Gradient Descent.ipynb
- 3 Linear regression.ipynb
- 4 Multiple Linear Regression.ipynb
- 5 Nonlinear regression.ipynb
- 6 Classification.ipynb
- 7 Log loss.ipynb
- environment.yml
- LICENSE
- materials.md
- README.md
// repository documentation
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