This is a structured deep learning curriculum for programmers, delivered as a collection of Jupyter notebooks. It teaches the fundamentals of training neural networks for computer vision, natural language processing, tabular data analysis, and collaborative filtering using PyTorch and the fastai library. The course is designed to be hands-on, guiding learners from building a training loop from scratch to fine-tuning pretrained models for a variety of practical tasks.
The curriculum distinguishes itself by covering the full lifecycle of a deep learning project, from data preparation and augmentation to model deployment and interpretation. It includes dedicated material on medical imaging with DICOM files, generative adversarial networks, and distributed training across multiple GPUs. The course also provides practical guidance on using cloud environments for execution and on sharing models through the Hugging Face Hub.
Beyond training, the course material covers model evaluation with custom metrics, uncertainty estimation through Monte Carlo dropout, and model interpretation through feature attribution and embedding visualization. It also addresses reproducibility with random seed management and offers a structured path for migrating existing PyTorch workflows into the fastai training loop.
