Key Concepts:
Forward Pass:
- During the forward pass, input data is fed into the neural network, and activations are computed layer by layer, propagating from the input layer through the hidden layers to the output layer.
The weighted sum of inputs is calculated at each neuron, and then passed through an activation function to produce the neuron's output.
- Once the forward pass is complete and the output of the network is generated, the error or loss is computed using a predefined loss function (e.g., mean squared error for regression tasks, cross-entropy loss for classification tasks).
The error quantifies the discrepancy between the predicted output and the actual target output
Backward Pass:
- In the backward pass, the error is propagated backward through the network to update the weights of the connections.
The gradient of the error with respect to each weight in the network is computed using the chain rule of calculus.
- After computing the gradients of the error with respect to the network's weights, the weights are updated using an optimization algorithm such as gradient descent.
Gradient descent adjusts the weights in the direction that minimizes the error, gradually reducing the error over multiple iterations (epochs) of training.
- The learning rate is a hyperparameter that determines the size of the steps taken during gradient descent.
It controls the speed at which the network learns and influences the convergence and stability of the training process.
Forward Pass:
- Input data is fed forward through the network, and activations are computed at each layer until the output is generated.
The output of the network is compared to the actual target output to compute the error.
- The error is propagated backward through the network using the chain rule of calculus.
Gradients of the error with respect to each weight in the network are computed layer by layer, starting from the output layer and moving backward towards the input layer.
- The gradients are used to update the weights of the connections between neurons.
The weights are adjusted in the direction that minimizes the error, determined by the gradient descent algorithm.
Steps 1-3 are repeated for multiple iterations (epochs) until the network converges to a satisfactory solution or the training process reaches a predefined stopping criterion.
Backpropagation in Neural Network Architectures:
- Feedforward Neural Networks (FNNs): Backpropagation is used to train FNNs with multiple layers of neurons, where information flows in one direction from the input to the output layer.
Recurrent Neural Networks (RNNs): Backpropagation through time (BPTT) is an extension of backpropagation used to train RNNs, which have connections that form cycles and can process sequential data.
Convolutional Neural Networks (CNNs): Backpropagation is applied to train CNNs, specialized for processing grid-like data such as images, by adjusting the weights of convolutional and pooling layers.
Backpropagation is a powerful algorithm for training neural networks by iteratively adjusting the weights of connections between neurons to minimize the error between predicted and target outputs. It enables neural networks to learn from labeled training data and make accurate predictions on unseen data, making it a fundamental technique in the field of machine learning and artificial intelligence.