Forward Propagation for Predictions in Neural Networks
Forward Propagation is the process of computing outputs by passing input data through a neural network.
During this process, input data is transformed as it passes through each layer, ultimately producing a prediction result.
Forward propagation is used not only during training but also when making actual predictions.
For example, when an image classification model receives a photo of a cat, the process of generating an output such as "The probability that this image is a cat is 85%" as it passes through several layers is forward propagation.
Input: Handwritten digit image (28x28 pixels)
Hidden Layer 1: Detect basic lines and shapes
Hidden Layer 2: Learn the digit's form
Output Layer: Highest probability for the digit '5' → Final output: '5'
Forward propagation is a key process for making predictions in a neural network.
As the data is passed through the layers, weights and biases are applied, and activation functions generate the final result.
However, without prior training, the accuracy may be low, necessitating the use of backpropagation to adjust the weights for improvement.
In the next lesson, we will explore Backpropagation and methods for adjusting weights.
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