Explore algorithms, neural architectures, reinforcement strategies, and the mathematical foundations powering modern artificial intelligence.
A comprehensive breakdown of the Transformer architecture, self-attention mechanisms, and how they revolutionized natural language processing and beyond.
From LeNet to ResNet and Vision Transformers. Explore the evolution of CNNs, their mathematical foundations, and real-world deployment strategies.
Understanding value-based, policy-based, and actor-critic methods. Includes detailed explanations of PPO, A3C, and modern reward shaping techniques.
A deep dive into adaptive learning rate methods, momentum, weight decay, and how optimization choices impact model convergence and generalization.
From data curation to distributed training, scaling laws, and instruction tuning. A practical guide to how foundation models are built and aligned.
Why models memorize instead of learn. Explore dropout, early stopping, L1/L2 penalties, data augmentation, and modern generalization theory.