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How Backpropagation works?

Backpropagation is a supervised learning algorithm pivotal in training artificial neural networks. It efficiently computes the gradient of the loss function with respect to each weight in the network, facilitating the update of weights in a manner that minimizes the loss. Below is a detailed step-by-step guide on how backpropagation operates: 1. Forward Propagation:
  • Data Input: Input data is fed through the network from the input layer through hidden layers to produce output predictions.
  • Transformation and Activation: At each layer, inputs are multiplied by the layer’s weights, summed up with a bias term, and passed through a non-linear activation function like ReLU or sigmoid, determining the layer’s output which serves as input for the next layer.
2. Compute Loss:
  • Comparison and Loss Calculation: The output predictions are compared to the true labels/targets using a loss function such as Mean Squared Error (MSE) for regression or Cross-Entropy for classification.
  • Loss Quantification: The loss function quantifies the disparity between the predictions and the actual targets, providing a scalar value that measures the network's performance.
3. Backward Propagation:
  • Gradient Computation: Starts by calculating the gradient of the loss function with respect to the network's final outputs. This gradient serves as the trigger for the backward pass.
  • Propagation of Gradients: Using the chain rule of calculus, these initial gradients are back-propagated through the network. At each layer, gradients of the loss with respect to that layer’s weights and biases are computed based on the gradients from the subsequent layer.
4. Update Weights:
  • Gradient Utilization: The gradients obtained during the backward propagation are used to update the weights and biases of the network.
  • Optimization Algorithms: Techniques such as Stochastic Gradient Descent (SGD), Adam, or RMSprop are employed to adjust the weights. These optimizers modify the weights in the direction that minimally decreases the loss, governed by parameters like the learning rate.
5. Repeat:
  • Iterative Process: The cycle of forward propagation, loss computation, backpropagation, and weight updating is repeated across multiple epochs. Each iteration refines the network’s weights and biases.
  • Convergence Check: This iterative update continues until the loss value converges to a minimum or another stopping criterion, such as a set number of epochs, is met.
Backpropagation is crucial for training deep learning models, allowing them to learn high-level abstractions and complex mappings from data. It adjusts the internal parameters (weights and biases) of the network based on the error feedback, which is instrumental in applications ranging from image and speech recognition to natural language processing and beyond. The method's effectiveness in numerous fields underscores its foundational role in contemporary AI and machine learning advancements.