Congratulations!

You’ve completed Build a Tiny Neural Network by Hand. You did the unglamorous work that actually teaches—multiply, sigmoid, loss, update, repeat.

What You Accomplished

✅ Built a single-neuron classifier from study hours (and sleep hours) to pass/fail
✅ Ran forward passes with weights and bias you can explain
✅ Used sigmoid and a threshold without pretending the model “thinks”
✅ Measured mistakes with squared error loss
✅ Trained with a plain Python loop—no TensorFlow or PyTorch required
✅ Scaled to two inputs and saw how weight updates follow the same rule
✅ Connected the idea to backpropagation as error flowing backward

Key Skills You Gained

1. Forward pass — raw = Σ(input × weight) + bias, then sigmoid
2. Decision rule — probability compared to 0.5
3. Loss — (actual − prediction)² as a wrongness meter
4. Training loop — epochs, rows, learning rate
5. Multi-feature weights — one weight per input, same update pattern

What’s Next?

Continue Learning

Math & classics:

• Logistic regression writeups (you already implemented the core)
• 3Blue1Brown neural network videos for visual intuition
• Andrew Ng’s ML course — week 1–2 overlaps heavily with what you did

Frameworks (when ready):

• PyTorch “first neural network” official tutorial — map loss.backward() to what you wrote by hand
• Fast.ai practical intro — still emphasizes what happens inside

Practice Projects

1. New dataset — predict pass/fail from quiz scores + attendance (make up 8–10 rows)
2. Learning rate lab — plot total loss per epoch for 0.01, 0.1, 0.5
3. Threshold experiment — use 0.6 instead of 0.5; who flips Pass/Fail?
4. Export weights — save weight, bias to JSON and load them in a tiny CLI predictor

Resources

Documentation:

• Python math module — exp for sigmoid
• NumPy beginner guide — optional matrix view

Tools:

• githubRepo/2026/05/20/tiny-neural-network-by-hand/ — runnable samples from this tutorial

Communities:

• r/learnmachinelearning — beginner-friendly questions
• PyTorch Discord — when you move to frameworks

Feedback

What clicked for you? What felt rushed? What topic should the next tiny tutorial cover (e.g. ReLU, one hidden layer)? Your notes help us improve these pages.

Thank You

You did the work most people skip—building the smallest possible network and watching it learn. That mental model will survive the next framework release.

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