What is a Diffusion Model?
Explains diffusion models as generative AI systems that learn to create data by reversing a noising process.
Deep-Learning
Quantization-Aware Training (QAT) in AI
Explains QAT for training neural networks to retain accuracy under low-precision quantization.
Vectors vs Tensors
Explains how vectors relate to tensors in ML, including rank, framework terminology, and KV cache shapes.
Multi-Layer Perceptron (MLP)
Foundational neural network architecture covering perceptrons, layers, activation functions, and backpropagation-based training.
Attention in Machine Learning
Explanation of the attention mechanism in ML, covering Query/Key/Value, self-attention, multi-head, causal, cross-attention, and efficiency variants like FlashAttention and GQA.
Diffusion Models in AI
Overview of diffusion models, how they reverse a gradual noising process to generate data, key variants like DDPM, DDIM, and Latent Diffusion Models, and how text-to-image conditioning works