Feedforward Neural Networks

A feedforward neural network is the simplest type of neural network, where data moves only in one direction, from input to output. Data flows one way through layers of artificial neurons until the output is achieved.

There is no memory of past inputs involved, each input is processed completely fresh, layer by layer. This straightforward design makes it a good starting point for understanding how neural networks function.

How Data Flows Through a Network

Step 1: Input Data
The input layer receives the raw data, such as an image, text, or numbers.

Step 2: Apply Weights and Bias
Each input is multiplied by a weight, and a bias is added to calculate a weighted sum.

Step 3: Apply the Activation Function
The weighted sum passes through an activation function, which determines what information moves to the next layer.

Step 4: Process Through Hidden Layers
The hidden layers repeat the same process, learning patterns and extracting useful features from the data.

Step 5: Generate the Output
The processed data reaches the output layer, which produces the final prediction or result.

Step 6: Calculate the Error (Training Only)
The prediction is compared with the correct answer to measure how accurate it is.

Step 7: Update Weights and Biases (Training Only)
The network adjusts its weights and biases to reduce the error and improve future predictions.

Step 8: Repeat the Process
The network repeats these steps many times with different training examples until it learns to make accurate predictions.

Forward Propagation Explained

Forward propagation is the process where data moves through a neural network from the input layer to the output layer to make a prediction.

• Input Layer: The network receives the input data, such as image pixels, text, or numerical values.
• Weighted Sum: Each neuron multiplies the input values by their weights and adds them together.
• Add Bias: A bias is added to the weighted sum to help the network learn more effectively.
• Activation Function: The result passes through an activation function, which decides what information should be sent to the next layer.

This process repeats through each layer until the network produces the final output or prediction.