Example 2Lesson 29Free preview

Ex2 Model

This lesson is part of A.I. · All levels

Summary

Models for home prices use inputShape according to the number of features, for example 3, with a hidden layer activating ReLU and one output neuron for the price. Loss remains meanSquaredError and the optimizer can be an adam that adjusts the automatic learning rate. This architecture allows models to capture the non-linear relationship between features and prices.

Key points

InputShape tracks the number of data features (e.g. [3] for 3 features).
Hidden layers with ReLU activation are used to capture non-linear patterns.
The regression output layer uses a single neuron without any specific activation.
Adam is an optimizer that automatically adjusts learning rates.
The quantity of units determines the learning capacity of the layer.

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