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assets		assets
Part 1 - Tensors in PyTorch (Exercises).ipynb		Part 1 - Tensors in PyTorch (Exercises).ipynb
Part 2 - Neural Networks in PyTorch (Exercises).ipynb		Part 2 - Neural Networks in PyTorch (Exercises).ipynb
Part 3 - Training Neural Networks (Exercises).ipynb		Part 3 - Training Neural Networks (Exercises).ipynb
Part 4 - Fashion-MNIST (Exercises).ipynb		Part 4 - Fashion-MNIST (Exercises).ipynb
Part 5 - Inference and Validation (Exercises).ipynb		Part 5 - Inference and Validation (Exercises).ipynb
Part 6 - Saving and Loading Models.ipynb		Part 6 - Saving and Loading Models.ipynb
Part 7 - Loading Image Data (Exercises).ipynb		Part 7 - Loading Image Data (Exercises).ipynb
Part 8 - Transfer Learning (Exercises).ipynb		Part 8 - Transfer Learning (Exercises).ipynb
README.md		README.md
fc_model.py		fc_model.py
helper.py		helper.py

README.md

Deep Learning with PyTorch

Part 1: Introduction to PyTorch and using tensors
Part 2: Building fully-connected neural networks with PyTorch
Part 3: How to train a fully-connected network with backpropagation on MNIST
Part 4: Exercise - train a neural network on Fashion-MNIST
Part 5: Using a trained network for making predictions and validating networks
(Optional) Part 6: How to save and load trained models
(Week 2-3)Part 7: Load image data with torchvision, also data augmentation
(Week 2-3) Part 8: Use transfer learning to train a state-of-the-art image classifier for dogs and cats

Note:

All exercises uses Image Data but None of them uses CNN. Image data is converted into numerical data and then used in a Fully Connected Neural Network.(Skip Part 7 and 8 for now though they do not require CNNs).