Master advanced PyTorch functionalities for building complex deep learning models. This course covers PyTorch internals, custom autograd functions, cutting-edge network architectures, advanced optimization strategies, distributed training, model optimization for deployment, and custom C++/CUDA extensions. Gain the skills to tackle sophisticated AI engineering challenges.
Prerequisites Intermediate PyTorch & DL concepts
Level:
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.
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