What is a Layer in a Neural Network?
In a neural network, a layer is a structure composed of neurons that processes input and passes it to the next layer.
The more layers there are, the more complex patterns the neural network can learn. Neural networks consisting of hundreds of layers are known as Deep Neural Networks.
Machine learning models utilizing these deep neural networks are called Deep Learning.
The primary layers in a neural network are as follows:
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Input Layer: The layer where data enters the neural network.
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Hidden Layer: The layer where input data is processed, and features are extracted. This is where the core computations of the neural network occur.
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Output Layer: The layer that outputs the final results.
Working Principle of Neural Networks
Neural networks learn through the processes of Forward Propagation and Backpropagation.
1. Forward Propagation
This is the process where data received at the Input Layer is passed through the Hidden Layers to the Output Layer.
During this process, each neuron applies weights and an activation function to transform the data.
Here are some common activation functions:
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Sigmoid Function: Transforms the output to a range between 0 and 1, making it interpretable as probability.
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ReLU (Rectified Linear Unit): Converts negative values to zero while keeping positive values unchanged, enabling fast computation.
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Softmax Function: Used in multi-class classification to calculate the probabilities for each class.
2. Backpropagation
This involves calculating the difference between the results obtained at the Output Layer and the actual values using a loss function, then adjusting the weights accordingly.
The loss function quantitatively measures how incorrect the model's predictions are.
Subsequently, optimization techniques like Gradient Descent are used to update the weights to reduce error.
Repeating this process multiple times allows the neural network to learn to produce increasingly accurate results.
In our next lesson, we will delve into the roles of each layer and the working principles of neural networks in more detail.
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