Topic
Deep Learning
6 episodes
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Overview: Quantization
We finally sit down with quantization and make it concrete: why shrinking a model’s numbers can speed inference up without wrecking the thing. We walk through the trade-offs, the common formats, and where quantization still matters in practice.
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Overview: Model Interpretability
We slow down and make model interpretability actually click: what it means to explain a model, what the main tools can and cannot show, and why the difference between a useful explanation and a comforting story matters.
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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: 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: 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.