Deep Learning & PyTorch Engineering
From your first nn.Module to deploying a quantized, ONNX-exported model — a hands-on, code-heavy track built around runnable PyTorch examples.
TopNotchNote
Notes that matter
coming soon
This track is next up: neural networks from scratch, the PyTorch training loop, CNNs and transfer learning, ResNet/DenseNet, similarity learning, transformers built from scratch, and model deployment (ONNX, pruning, quantization, MLflow). Beginner through advanced, all code-tagged with runnable samples.
related topics
ML Foundations — the theory this track puts into code.
PyTorch Notes — the quick-reference cheat sheet this track builds on top of.
PyTorch on GPU — the performance-engineering side once training needs to be fast.
LLMs & Generative AI — where these architectures scale up to language models.
PyTorch Notes — the quick-reference cheat sheet this track builds on top of.
PyTorch on GPU — the performance-engineering side once training needs to be fast.
LLMs & Generative AI — where these architectures scale up to language models.