This video tutorial provides an intuitive, in-depth breakdown of how an LLM learns language and uses that learning to generate text. Key concepts shown below are covered in a way that is both broad and deep, while still keeping the material accessible without losing technical rigor:

* Historical context for LLMs and GenAI

* Training an LLM -- 100K overview

* What does an LLM learn during training?

* Inferencing an LLM -- 100K overview

* 3 steps in the LLM journey from pre-training to serving

* Word Embeddings -- representing text in numeric format

* RMS Normalization -- the sound engineer of the Transformer

* Benefits of RMS Normalization over Layer Normalization

* Rotary Position Encoding (RoPE) -- making the Transformer aware of token position

* Masked Self-Attention -- making the Transformer understand context

* How RoPE generalizes well making long-context LLMs possible

* Understanding what Causal Masking is (intuition and benefit)

* Multi-Head Attention -- improving stability of Self Attention

* Residual Connections -- improving stability of learning

* Feed Forward Network

* SwiGLU Activation Function

* Stacking

* Projection Layer -- Next Token Prediction

* Inferencing a Large Language Model

* Step by Step next token generation to form sentences

* Perplexity Score -- how well did the model does

* Next Token Selector -- Greedy Sampling

* Next Token Selector -- Top-k Sampling

* Next Token Selector -- Top-p/Nucleus Sampling

* Temperature -- making an LLM's generation more creative

* Instruction finetuning -- aligning an LLM's response

* Learning going forward