Backpropagation Videos

• Backpropagatin by hand

What is backpropagation?

Backpropagation is the main learning method that helps artificial neural networks improve at their tasks. At its heart, it's a way for the network to figure out which parts of itself need adjusting to get better results.

Backpropagation is the method we use to optimize parameters in a Neural Network. The ideas behind backpropagation are quite simple, but there are tons of details. Jeremy gives an introduction here! Short tweet here.

• Inputs: These are the data points fed into the system (like images, text, or numbers).
• Parameters: These are the adjustable values that the model learns during training.
• Architecture: This is the structure or design of the model that processes the inputs using the parameters.
• Predictions: These are the outputs that the model generates based on the inputs.
• Labels: These are the correct answers or ground truth that the model is trying to match.
• Loss: This measures how far off the model's predictions are from the correct labels.
• Update: This is the key feedback loop where the model's parameters are adjusted based on the loss to improve future predictions.

Backpropagation computes the gradients of a loss function with respect to the weights in Deep Neural Networks

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The beauty of backpropagation is that it automates the learning process. The network figures out for itself which adjustments will help it improve, rather than requiring humans to program every detail.

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How it works (in simple steps):