Causal Attention: From Q, K, and V to the Next Token | CMU 10-423 Lecture 2
An RNN compresses the prefix into a state.
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1239 postsTraining and Decoding: Why the Same Logits Produce Different Sequences | CMU 10-423 Lecture 3
Learning a next-token distribution and choosing a token from that distribution are consecutive but distinct operations.
Diffusion Training and Sampling: Random Timesteps and Reverse Chains | CMU 10-423 Lecture 8
Lecture 7 constructed noisy inputs and noise targets.
VAEs: Reconstruction, KL, and Reparameterization | CMU 10-423 Lecture 9
A conventional autoencoder compresses an input into a vector and reconstructs it.
ICL, SFT, and RLHF: From Changing Inputs to Changing Weights | CMU 10-423 Lecture 11
Separate what ICL, SFT, reward models, and RLHF each change, with worked examples and answered variations.
DPO and Latent Diffusion: Updating on Preferences, Conditioning the Denoiser | CMU 10-423 Lecture 12
Derive the DPO loss with a recomputable example, then trace shapes and conditioning paths in latent diffusion.
Vision-Language Models: Turning Images into Language-Model Inputs | CMU 10-423 Lecture 13
A complete worked example and answered variations for Vision-Language Models: Turning Images into Language-Model Inputs.
Cross-Attention and DiT: How Conditions Enter a Denoiser | CMU 10-423 Lecture 14
A complete worked example and answered variations for Cross-Attention and DiT: How Conditions Enter a Denoiser.
Q-Former and Scaling Laws: Bridging Frozen Models, Allocating a Fixed Budget | CMU 10-423 Lecture 15
Trace Q-Former queries and two-stage training, then compute a model/data split under a fixed compute budget.
Mixture of Experts: Separating Capacity from Active Computation | CMU 10-423 Lecture 16
A complete worked example and answered variations for Mixture of Experts: Separating Capacity from Active Computation.
Distributed Training: Splitting Data, Models, and Optimizer State | CMU 10-423 Lecture 17
A complete worked example and answered variations for Distributed Training: Splitting Data, Models, and Optimizer State.
Efficient Inference: FlashAttention, KV Caches, and Speculative Decoding | CMU 10-423 Lecture 18
A complete worked example and answered variations for Efficient Inference: FlashAttention, KV Caches, and Speculative Decoding.
Long Context: Fitting Input Is Not Reliable Retrieval | CMU 10-423 Lecture 19
Long Context: Fitting Input Is Not Reliable Retrieval | CMU 10-423 Lecture 19
State Space Models: From Recurrence to Convolution | CMU 10-423 Lecture 21
State Space Models: From Recurrence to Convolution | CMU 10-423 Lecture 21
Generative AI Risks: Turning Failures into Tests | CMU 10-423 Lecture 22
Generative AI Risks: Turning Failures into Tests | CMU 10-423 Lecture 22
Audio Understanding and Generation: Samples to Text, Tokens Back to Sound | CMU 10-423 Lecture 24
Compute frames, mel bins, and WER, then account for codec tokens and contrast diffusion sampling.
Video Generation: Modeling the Time Axis | CMU 10-423 Lecture 25
Video Generation: Modeling the Time Axis | CMU 10-423 Lecture 25
World Models and Alignment Theory: Action Interventions and Corrigibility Assumptions | CMU 10-423 Lecture 26
Separate three world representations and evidence of action control, then examine consensus costs and corrigibility theorem conditions.
LoRA: What Changes When Most Weights Stay Frozen | CMU 10-423 Lecture 10
A complete worked example and answered variations for LoRA: What Changes When Most Weights Stay Frozen.
Practice Exam: Diagnose Conceptual Gaps | CMU 10-423 Topic Guide
Use the public practice exam to diagnose assumptions, data flow, and evaluation errors through a worked probability example.