Artificial Neural Networks (ANNs) learn by adjusting their weights and biases based on input data to improve performance over time.
- The training process involves modifying connections between neurons to recognize patterns, classify data, or make predictions.
Different training methods exist, including:
- Hebbian Learning
- Perceptron Learning
- Backpropagation Learning.
1.) Hebbian Learning:
Hebbian Learning is a biologically inspired learning rule based on the principle:
"Neurons that fire together, wire together."
This means that connections between frequently activated neurons are strengthened over time, while inactive connections weaken.
How it Works:
- If two neurons activate simultaneously, the connection (synapse) between them strengthens.
- If one neuron fires but the other does not, the connection weakens.
The weight update rule follows:
where:
- w = connection weight
- η = learning rate
- x,y = neuron activation values
Applications:
- Memory association in AI systems.
- Pattern recognition tasks.
2.) Perceptron Learning:
Perceptron Learning is a supervised learning algorithm used in binary classification tasks. It updates the weights of a single-layer perceptron based on classification errors.
How it Works:
- The perceptron takes inputs, applies weights, and produces an output using an activation function (e.g., step function).
- If the output is incorrect, the algorithm adjusts weights using the formula
where:
- d = desired output
- y = actual output
- x = input feature
- η = learning rate
Limitations:
- Can only solve linearly separable problems.
- Cannot solve complex problems like XOR classification.
Applications:
- Image classification.
- Spam detection in emails.
3.) Backpropagation Learning:
Backpropagation (Backward Propagation of Errors) is a supervised learning algorithm used in multi-layer neural networks. It updates weights to minimize errors using gradient descent.
How it Works:
- Forward Pass: The input data passes through the network, generating an output.
- Error Calculation: The difference between the predicted output and actual output (loss) is computed.
- Backward Pass:
- The error is propagated backward through the network using the chain rule of differentiation.
- Weights are updated using the formula
where:
- E = error
- η = learning rate
- w = weight
Advantages:
- Works for non-linearly separable problems.
- Can train deep neural networks effectively.
Applications:
- Face and speech recognition.
- Autonomous vehicles.
