Understanding Neural Networks: Forward Propagation and Activation Functions

How are Neural Networks trained: Forward Propagation

1. Introduction :

Neural Network Architecture Diagram (Input, Hidden, Output Layers)

1.Input Layer (Green Nodes): Represents the input features.

2.Hidden Layer (Red Nodes): Applies weights and biases, followed by an activation function.

3.Output Layer (Blue Node): Produces the final prediction or classification.

Key Concepts Highlighted:

1. Weights ( w ): Represent the strength of the connection between neurons.

2. Bias: Helps shift the activation function to improve learning.

3. Activation Function: Transforms the weighted sum ( w *x + Bias) to introduce non-linearity.

This example simulates a single-layer neural network

Predict 1st year college grades from high school SAT and GPA scores.