Unleashing the Magic of Deep Learning
Chronicles of Deep Learning.
DeepLearning
Introduction deep learning
why we need to know about Deep Learning.
LoRA: Fine-tuning Giant Models Without Retraining Them
Why updating billions of frozen weights is wasteful, and how a couple of small low-rank matrices can do the job instead.
Mixture-of-Experts (MoE): Huge Models, Cheap Inference
How routing each input to only a few ‘expert’ sub-networks lets models grow to trillions of parameters without a proportional compute bill.
State Space Models and Mamba: Beyond Quadratic Attention
Why attention gets expensive as sequences grow, and how Mamba borrows ideas from control theory to process long sequences in linear time.
Vision Transformers (ViT): Attention Instead of Convolutions
How the transformer architecture that took over NLP learned to see images, one patch at a time.