LoRA (Low-Rank Adaptation) in AI
LoRA efficiently fine-tunes large models by training small low-rank adapter matrices instead of updating all weights.
Machine-Learning
Mixture of Experts in AI
Explains sparse Mixture-of-Experts (MoE) architecture with conditional computation, router/gate mechanisms, load balancing, and trade-offs vs. dense models.
Autoregressive Image Generation
Explains autoregressive image generation as sequential visual-token prediction using Transformer-style next-token modeling.
What is a Diffusion Model?
Explains diffusion models as generative AI systems that learn to create data by reversing a noising process.
The Modern LLM Training Pipeline
Explains the four-stage modern LLM training pipeline from pre-training through verifiable-reward RL.
The Modern LLM Training Pipeline
Explains the four-stage modern LLM training pipeline from pre-training through verifiable-reward RL.
Quantization-Aware Training (QAT) in AI
Explains QAT for training neural networks to retain accuracy under low-precision quantization.
Multi-Layer Perceptron (MLP)
Foundational neural network architecture covering perceptrons, layers, activation functions, and backpropagation-based training.