Prerequisites: Strong understanding of Python, foundational deep learning concepts (neural networks, backpropagation), and intermediate PyTorch experience (building and training standard models). Familiarity with calculus and linear algebra is beneficial.

Level: Advanced

What You'll Learn

• PyTorch Internals

  Understand the underlying mechanics of PyTorch tensors and the autograd engine.

• Custom Operations

  Implement custom autograd functions and C++/CUDA extensions for specialized operations.

• Advanced Architectures

  Build complex models like Transformers, GNNs, and Normalizing Flows.

• Optimization Techniques

  Apply advanced optimizers, learning rate schedules, mixed-precision training, and regularization.

• Distributed Training

  Implement various parallel training strategies including DDP, Model Parallelism, and FSDP.

• Model Deployment Optimization

  Optimize models for inference using TorchScript, quantization, pruning, and profiling.