Topic
Conditional Probability
12 episodes
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Overview: Diffusion Models
We finally slow down and make Diffusion Models click: the noisy-photo-in-reverse idea, the training loop, the denoising steps, and why this became so central for image generation. We also get into prompts, guidance, latent space, speed trade-offs, and why the concept is still very much alive.
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Overview: Causal Inference
We keep circling causal inference because the difference between correlation and cause is where a lot of AI gets tricked. We finally slow it down, build the intuition from observational data to interventions, and show why that oxygen-mask problem keeps showing up everywhere.
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Overview: Bayes' Theorem
We finally slow down on Bayes' Theorem, the belief-updating rule we keep smuggling into conversations about evals, spam filters, diagnosis, ranking, and calibration. We make it click through one package-sorting picture: evidence only matters against the pile it came from.
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Overview: Confounding Variables
We slow down on confounding variables, the hidden factors that can make data look causal when it is not. We use the same hidden-knob picture all the way through, from everyday examples to machine learning and causal data mixture work.
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Overview: Decoding Strategy
We finally slow down on decoding strategy, the rule that turns a model's next-token odds into the actual words you see. We use one hallway-and-doors picture to make greedy decoding, sampling, top-k, top-p, beam search, and newer decoding work feel less like magic knobs.
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Overview: Sampling and Temperature
We’re finally slowing down and explaining sampling and temperature from the ground up, because we keep circling back to it and it’s one of those knobs that sounds simple until it isn’t. We talk through why the same prompt can produce different answers, how temperature changes the shape of the next-token choices, and where the trade-offs actually land.
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Overview: Sequence Modeling
We slow down and finally define sequence modeling, the idea underneath next-token prediction, language models, and a surprising amount of modern AI. We keep it grounded in one picture: covering the next word and training a model to guess what belongs there.
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Overview: Calibration
We finally slow down and make calibration click: what it means for a model’s confidence to match reality, how you measure that, and why it matters when you actually want to trust the thing. We keep it grounded in the systems we’ve been circling for ages, because calibration is everywhere once you start looking.
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Exploring Next Overview: Speculative Decoding
We finally slow down and unpack speculative decoding from the ground up: the draft model, the verify step, and why it can make generation faster without changing the output. We keep it concrete, because that trick sounds like cheating until the mechanism actually clicks.
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Overview: Loss Function
We’re finally slowing down and making loss function click: the scoreboard that tells a model how wrong it was, and the signal that lets training move in a useful direction. We also keep the usual Justy-Cody back-and-forth, because apparently even a loss function needs two friends arguing about it for forty minutes.
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Overview: Conditional Probability
We keep running into conditional probability anywhere we try to reason from partial evidence, so we finally sat down and made it the whole point. We’re breaking down P(A|B), why the denominator matters, and why this little idea quietly sits under a ton of AI behavior.
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Overview: Autoregressive Generation
We finally slow down and make autoregressive generation click: the whole thing is just a model writing one token, then using what it wrote to choose the next one. We keep the focus on the loop, the trade-offs, and why that one-step-at-a-time setup is still the backbone of modern language models.