Topic
Gradient Descent
10 episodes
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GitHub FareedKhan Dev/train LLM From scratch: A straightforward method for training your LLM, from downloading data to generating text.
A single-GPU end-to-end LLM training guide lands on GitHub—hand-written PyTorch, pretrain to chat in one repo, plus full RLHF. Vince is giddy; Ava wants to know which corner of the GPU shelf this actually runs on. Build Next shows the exact CLI to kick it off on a T4.
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Overview: Model Generalization
We finally slow down and make model generalization click from the ground up: what it means, how you measure it, and why memorizing the training set is a dead end. We keep coming back to the same simple idea, because that’s the whole game.
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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: 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.
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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.