The Little Learner in Python
How does a neural network learn? This series follows the ideas in The Little Learner, rebuilding them in Python so that we can see how each piece works.
We start with a model and a loss, then build gradient descent, tensor operations, optimizers, and automatic differentiation. Finally, we put those pieces together into a neural network that classifies Iris flowers.
- Part 1: The Core Optimization Mechanism
- Part 2: Gradient Descent
- Part 3: Tensors
- Part 4: Optimizers
- Part 5: Automatic Differentiation
- Part 5.5: Automatic Differentiation 2.0
- Part 6: Neural Networks
The articles develop the ideas step by step. For the complete, runnable implementation and examples, see the tiny-learner repository.