Deep-Learning

Organize linear algebra resources for AI developers, focusing on ML and DL applications. Prioritize practical learning paths and avoid beginner pitfalls.

Explains diffusion models as generative AI systems that learn to create data by reversing a noising process.

Explains QAT for training neural networks to retain accuracy under low-precision quantization.

Explains how vectors relate to tensors in ML, including rank, framework terminology, and KV cache shapes.

Foundational neural network architecture covering perceptrons, layers, activation functions, and backpropagation-based training.

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.

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