The provided text appears to be a transcript of a video where the author is attempting to code a PyTorch deep learning model in 15 minutes. They start by explaining the rules of the challenge, import necessary dependencies, create a neural network class, set up the optimizer and loss function, and define a training loop. The code saves the trained model state to a file named "model_state.pt" at the end. The author is aiming to complete this task within a 15-minute timeframe.
Key Facts:
1. The goal is to code a PyTorch deep learning model in 15 minutes.
2. PyTorch is an open-source deep learning framework developed by Facebook.
3. The model is designed to classify images using the MNIST dataset.
4. The neural network architecture includes convolutional layers and linear layers.
5. Training involves using an Adam optimizer with a learning rate of 1e-3.
6. The training loop runs for 10 epochs, and for each epoch, it iterates through the dataset in batches.
7. The loss is calculated using the cross-entropy loss function.
8. Gradients are zeroed before backpropagation, and the model parameters are updated using gradient descent.
9. The training progress, including the epoch and loss, is printed for each batch.
10. The trained model is saved to a file named "model_state.pt" in binary format.