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🤖 | A collection of comprehensive PyTorch tutorials covering fundamental and advanced deep learning concepts
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yakhyo/pytorch-tutorials
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- QuickStart [File |Notebook] -QuickStart gives general overview ofBasics section.
- Tensors [File |Notebook] -Operations on tensors, numpy arrays and casting them to tensor or vice versa.
- Datasets and DataLoaders [File |Notebook] - Creatingdatasets anddataloaders.
- Transforms [File |Notebook] - Torchvision'saugmentation methods and using them together.
- Build Neural Network [File |Notebook] - Building simple neural network from scratch.
- Optimizers, Model Save and Load [File |Notebook] - Creatingoptimizer,saving trained model andloading for inference.
- Autograd and Freeze weights - Autograd:Automatic Differentiation.
- Neural Networks - To look deep intoNeural Networks.
- Convolutional Neural Network - Classifying images of CIFAR10 usingCNNs.
- Residual Neural Network - UsingResidual Blocks to build a CNN for image classification.
- Recurrent Neural Network - Image classification usingRNN networks.
- Long Short Term Memory - MNIST digit classifier usingLSTM.
- Variational Auto Encoder - Reconstructing MNIST data samples usingVAE.
- Adversarial Generative Network - Deep ConvolutionalGenerative Adversarial Network.
- Fast Neural Style Transfer - Transferring image with different styles.
- Super Resolution. Running it Using ONNX Runtime -Inference usingONNX Runtime.
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