Topic
Neural Network Parameters
10 episodes
-
Overview: Fine tuning on Execution Traces
We keep throwing around fine-tuning on execution traces, so we finally define it from the ground up. We’re talking about training a model on the steps, not just the answer, and why that changes what it learns.
-
Overview: Supervised Fine Tuning
We finally slow down and make supervised fine-tuning click, because we keep leaning on S F T like everyone already has the whole shape of it. We build it from the apprentice-and-worked-examples picture into the actual training loop, the examples, and the trade-offs.
-
Overview: Active vs Total Parameters
We finally slow down on active vs total parameters, because we keep throwing the phrase around like it explains itself. This is us making the difference click: what a model stores versus what it actually uses when it answers.
-
Overview: Fine Tuning
We finally do the fine-tuning episode we kept circling, and we make the core idea click: you start with a pretrained model, then adjust its weights on your own examples so it behaves the way your task actually needs. We also dig into when that helps, when it doesn’t, and why the quality of the data is the whole game.
-
Overview: Embeddings
We’re finally doing a full pass on embeddings, because they keep showing up under half the things we talk about. We get into what an embedding actually is, why it turns meaning into usable coordinates, and why that little geometric trick sits under so much of modern AI.
-
Overview: Neural Network Parameters
We finally slow down and make neural network parameters click from the ground up: what they are, how training changes them, and why the final frozen numbers matter so much. We keep coming back to the same mental picture so it actually sticks, instead of just sounding like another ML buzzword.
-
Overview: Classifier
We finally slow down and make classifier click from the ground up: what it is, how it learns, and why the boring details like labels, loss, and held-out tests matter. We keep it in first-person and keep it practical, because that’s the whole point of calling this a Classifier episode.
-
Overview: Backpropagation
We finally define backpropagation the way we probably should’ve ages ago: as the backward bookkeeping step that tells a neural network which knobs caused the miss. We also connect it to loss, gradients, and gradient descent so the whole training loop actually clicks.
-
Overview: Gradient Descent
We’re finally giving gradient descent the full couch-table treatment, because it’s hiding under half the AI stories we keep talking about. We get into the hill-climbing mental model, how loss, parameters, and backpropagation fit together, and why the whole thing is the boring engine that actually makes learning happen.
-
Overview: Neural Network
We finally slow down on neural networks: what they are, how the little adjustable pieces learn from examples, and why this basic idea sits underneath so much of modern A I.