pytorch-tutorials
Contents
Basics
-
QuickStart [File Notebook] - QuickStart gives general overview of Basics section. -
Tensors [File Notebook] - Operations on tensors, numpy arrays and casting them to tensor or vice versa. -
Datasets and DataLoaders [File Notebook] - Creating datasets and dataloaders. -
Transforms [File Notebook] - Torchvision’s augmentation methods and using them together. -
Build Neural Network [File Notebook] - Building simple neural network from scratch. -
Optimizers, Model Save and Load [File Notebook] - Creating optimizer, saving trained model and loading for inference.
Beginner
- Autograd and Freeze weights - Autograd: Automatic Differentiation.
- Neural Networks - To look deep into Neural Networks.
Intermediate
- Convolutional Neural Network - Classifying images of CIFAR10 using CNNs.
- Residual Neural Network - Using Residual Blocks to build a CNN for image classification.
- Recurrent Neural Network - Image classification using RNN networks.
- Long Short Term Memory - MNIST digit classifier using LSTM.
- Variational Auto Encoder - Reconstructing MNIST data samples using VAE.
Advanced
- Adversarial Generative Network - Deep Convolutional Generative Adversarial Network.
- Fast Neural Style Transfer - Transferring image with different styles.
- Super Resolution. Running it Using ONNX Runtime - Inference using ONNX Runtime.
Updating…
References