Lessons
The overview is the whole idea in one sitting. These pages take one concrete case each and walk it through end to end — inputs, steps, and result — so you can see how the vocabulary from Terms shows up in practice.
Deep Learning
Machine learning with stacked neurons and ReLU layers: how a deep network builds a price guess from house features in TypeScript.
- 01
Deep House Architecture
Build a 3→4→4→1 ReLU network and run one forward pass on a house — no training yet.
- 02
Forward Pass Through Depth
Trace [1500, 3, 10] through two ReLU layers to a price; see which neurons switch off.
- 03
Backprop Through Depth
Send price error backward through two ReLU layers and take one gradient-descent step.
- 04
Train the Deep House Model
Epochs over all five HOUSES: forward, backprop, update — watch loss fall.
- 05
Inference and Hold-Out
Freeze the net, price a new listing, and check a held-out house so train loss is not the whole story.
Related concepts
| Topic | Description |
|---|---|
| Artificial Intelligence (AI) | What AI is at the first layer: human decisions turned into computer rules — before machine learning enters the picture. |
| Machine Learning | How a model improves from examples: labels, features, training, and the difference between fitting data and predicting on new data. |