Welcome back to our course "Neural Network Fundamentals: Neurons and Layers"! You're making excellent progress. In the previous units, we built a single artificial neuron, enhanced it with the sigmoid activation function, and then combined multiple neurons into a DenseLayer class using JavaScript and mathjs.
In this final lesson, we'll bring our dense layer to life by implementing forward propagation — the process by which information travels through a neural network from input to output. This is where our layer actually processes data and produces meaningful activations.
By the end of this lesson, you'll have a functional dense layer that can take inputs, process them through multiple neurons simultaneously, and produce outputs that could be fed to another layer or used directly for predictions.
Before we dive into code, let's clarify what forward propagation means in the context of neural networks.
Forward propagation (or forward pass) is the process of taking input data and passing it through the network to generate an output. It's called "forward" because information flows forward through the network, from the input layer, through any hidden layers, to the output layer.
For our dense layer, forward propagation involves three key steps:
- Weighted sum calculation: Multiply each input feature by its corresponding weight.
- Bias addition: Add the bias term to each weighted sum.
- Activation: Apply the activation function to introduce non-linearity.
