What is backpropagation?

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Backpropagation (short for backward propagation of errors) is the learning algorithm used to train neural networks. It adjusts the weights of the network so that predictions become more accurate over time.

How it works (Step by Step):

  1. Forward Pass:

    • Input data flows through the network (from input layer → hidden layers → output layer).

    • The network makes a prediction.

  2. Error Calculation:

    • The difference between the predicted output and the actual (true) output is calculated using a loss function (e.g., Mean Squared Error, Cross-Entropy).

  3. Backward Pass (Backpropagation):

    • The algorithm computes how much each weight in the network contributed to the error.

    • It uses calculus (chain rule of derivatives) to calculate the gradient of the loss function with respect to each weight.

  4. Weight Update:

    • Weights are updated using an optimization algorithm (commonly Gradient Descent) to minimize the error.

    • New Weight = Old Weight – (Learning Rate × Gradient).

  5. Repeat:

    • This process repeats for many iterations (epochs) until the network’s predictions are accurate enough.

Why is it important?

  • Without backpropagation, neural networks couldn’t learn from data effectively.

  • It makes deep learning possible by enabling networks with many layers to adjust their parameters.

  • It’s efficient and scalable, even for very large models.

✅ In short: Backpropagation is the engine behind how neural networks learn, by comparing predictions with reality, finding errors, and adjusting weights step by step to improve accuracy.

Read More:

What is a neural network?

What are stop words in NLP?

Explain the difference between CNN and RNN.

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