<ul data-eligibleForWebStory="true"><li>Backpropagation is the learning algorithm used by most neural networks to update model weights after each prediction.</li><li>It helps neural networks learn from mistakes by adjusting internal weights.</li><li>The process is similar to correcting a student's mistake and allowing them to improve.</li><li>Backpropagation allows the network to adjust internal weights in the right direction.</li><li>Steps in backpropagation involve input data passing through network layers, making predictions, and comparing them to actual labels.</li><li>A loss function like MSE or Cross-Entropy quantifies how wrong the model's prediction was.</li><li>Weights are adjusted based on minimizing the loss, and this cycle repeats for the next batch of data.</li><li>Analogies like a thermostat adjusting room temperature help understand the concept of backpropagation.</li><li>Backpropagation is essential for deep learning models to learn and improve over time.</li><li>It guides the network in tweaking internal weights to get closer to correct values.</li><li>Backpropagation is the foundation of how deep learning models improve their decisions over time.</li></ul>

Backpropagation in Deep Learning

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