Learning Notes
Notes on deep learning, PyTorch, LLMs, and more — documenting what I learn along the way.
4 series · 6 sections
Mathematical Foundations
Linear algebra, calculus, and probability concepts essential for deep learning.
Deep Learning
Neural networks from perceptrons to modern architectures.
PyTorch
Practical PyTorch — tensors, autograd, training loops, and best practices.
Large Language Models
Transformers, attention mechanisms, pretraining, and fine-tuning.