Lesson - Intorduction to PyTorch

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Intorduction to PyTorch

Goal

To provide participants with a foundational understanding of PyTorch, its capabilities, and how it can be used to implement neural networks and process data, especially in the context of Retrogressive Thaw Slumps.

Breakdown

1. Overview of Deep Learning Frameworks
   • Brief mention of popular frameworks: TensorFlow, Keras, etc.
   • Why PyTorch? Advantages and use cases
2. PyTorch Basics
   • Tensors: Understanding the basic data structure in PyTorch
   • Operations with tensors: Reshaping, slicing, mathematical operations
   • GPU vs. CPU: How PyTorch utilizes hardware acceleration
3. Data in PyTorch
   • Dataset and DataLoader: Efficiently loading and batching data
   • Transformations: Augmenting and preprocessing data
   • Connecting the dots: How RTS data can be loaded and preprocessed in PyTorch
4. Model Building in PyTorch (30 minutes)
   • nn.Module: Creating custom neural network architectures
   • Layers in PyTorch: Linear, Conv2D, RNN, etc.
   • Activation functions: ReLU, Sigmoid, Tanh, etc.
5. Optimizers, Loss Functions, and Schedulers
   • Loss functions: MSE, CrossEntropy, etc.
   • Optimizers: Adam, SGD, etc.
   • Learning rate schedulers: StepLR, ReduceLROnPlateau, etc.
6. Training, Validation, and Testing Pipeline
   • Forward and backward propagation in PyTorch
   • Model evaluation: Accuracy, loss, and other metrics
   • Overfitting: Early stopping, dropout, and other regularization techniques
   • A simple example: Training, validating, and testing a small neural network on sample data