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Machine Learning Basics
A hands-on series that builds the intuition behind how machine learning models actually learn — starting from the idea of prediction and working up through linear regression, loss functions, gradient descent, overfitting, and activation functions, all the way to what "predicting the next token" really means. Every concept comes with an interactive demo you can run in your browser.
Based on the AI Power Hour: Weights, Biases and Gradient Descent session.
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Part 1
Prediction and Regression
Why prediction is at the heart of intelligence, how Sir Francis Galton accidentally invented machine learning, and what "regression" actually means.
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Part 2
Weights, Bias, and the Linear Model
What weight and bias actually do to a line, and how changing them lets you fit any straight-line relationship in your data.
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Part 3
Solve It or Search for It
The two ways to find the best weight and bias — a one-shot formula versus a step-by-step search — and why the search wins once models get big or bendy.
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Part 4
Loss Functions
How we measure how wrong a model is, Mean Squared Error vs Mean Absolute Error, why their loss curves have different shapes, and what the shape tells gradient descent.
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Part 5
Gradient Descent
How gradient descent uses the slope of the loss curve to iteratively nudge weight and bias towards their ideal values, the training loop explained.
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Part 6
The Learning Rate
Why the step size matters, too small and training crawls, too large and it overshoots. Interactive demos of slow convergence and oscillation.
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Part 7
Overfitting and Generalisation
Why a model can score perfectly on its training data and still fail in the real world, what memorising versus learning looks like, and how held-back data keeps a model honest.
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Part 8
Local vs Global Minima
Why the loss surface is a bumpy bowl, not a perfect cup, and why ending up at a local minimum is often fine in practice.
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Part 9
Activation Functions
How ReLU, leaky ReLU, sigmoid, and tanh transform a linear neuron's output to model non-linear real-world behaviour, with interactive demos for each.
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Part 10
Neurons, Chaining, and Specialisation
What happens when you connect activated neurons together, how chaining produces new behaviour, how parallel neurons specialise on sub-problems, and how gradient descent trains a tiny neural network to approximate a sine wave.
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Part 11
From Straight Lines to ChatGPT
Closing the loop — how the weights, biases, loss functions, and gradient descent you now understand scale up from fitting a line to predicting the next token in a sentence.