III. The Learning Process: Forward and Backward Propagation

Now that we understand the roles of weights and biases, let’s explore how they come into play during the learning process of a neural network.

A. Forward Propagation

Forward propagation is the initial phase of processing input data through the neural network to produce an output or prediction. Here’s how it works:

1. Input Layer: The input data is fed into the neural network’s input layer.
2. Weighted Sum: Each neuron in the subsequent layers calculates a weighted sum of the inputs it receives, where the weights are the adjustable parameters.
3. Adding Biases: To this weighted sum, the bias associated with each neuron is added. This introduces an offset or threshold for activation.
4. Activation Function: The result of the weighted sum plus bias is passed through an activation function. This function determines whether the neuron should activate or remain dormant based on the calculated value.
5. Propagation: The output of one layer becomes the input for the next layer, and the process repeats until the final layer produces the network’s prediction.

B. Backward Propagation

Once the network has made a prediction, it’s essential to evaluate how accurate that prediction is and make adjustments to improve future predictions. This is where backward propagation comes into play:

1. Error Calculation: The prediction made by the network is compared to the actual target or ground truth. The resulting error, often quantified as a loss or cost, measures the disparity between prediction and reality.
2. Gradient Descent: Backward propagation involves minimizing this error. To do so, the network calculates the gradient of the error with respect to the weights and biases. This gradient points in the direction of the steepest decrease in error.
3. Weight and Bias Updates: The network uses this gradient information to update the weights and biases throughout the network. The goal is to find the values that minimize the error.
4. Iterative Process: This process of forward and backward propagation is repeated iteratively on batches of training data. With each iteration, the network’s weights and biases get closer to values that minimize the error.

In essence, backward propagation fine-tunes the network’s parameters, adjusting weights and biases to make the network’s predictions more accurate. This iterative learning process continues until the network achieves a satisfactory level of performance on the training data.

Machine learning, with its ever-expanding applications in various domains, has revolutionized the way we approach complex problems and make data-driven decisions. At the heart of this transformative technology lies neural networks, computational models inspired by the human brain’s architecture. Neural networks have the remarkable ability to learn from data and uncover intricate patterns, making them invaluable tools in fields as diverse as image recognition, natural language processing, and autonomous vehicles. To grasp the inner workings of neural networks, we must delve into two essential components: weights and biases.