Topic
Backpropagation
9 episodes
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Overview: Credit Assignment
We throw 'credit assignment' around constantly on this show and realized we've never actually stopped to explain it — so this episode, we fix that. It's the foundational question underneath all of machine learning: when a model gets something right, which of the thousand tiny decisions inside it actually deserved the credit?
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Overview: Activation Function
We finally slow down and make activation functions click: what they are, why neural networks need them, and why a tiny mathematical kink keeps deep learning from collapsing into one big straight-line machine.
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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.
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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.
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Overview: Deep Learning
We finally slow down and make deep learning click: what the deep part means, how layers learn features, and why training needs data, compute, loss, backpropagation, and gradient descent all working together.
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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: 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.
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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.
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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.