Backpropagation is a supervised learning algorithm used to train artificial neural networks. The backpropagation algorithm uses the gradient descent optimization method to find the best weights for the neural network’s connection strengths, or synapses, to minimize the prediction error between the actual output and the expected output. The algorithm is called “backpropagation” because it “propagates” the error backwards through the network, from the output layer to the input layer, adjusting the weights accordingly.

Here is a high-level overview of the backpropagation algorithm:

  1. Initialize the network weights randomly.

  2. For each training example, feed the input through the network and calculate the predicted output.

  3. Calculate the error between the predicted output and the actual output.

  4. Propagate the error backwards through the network, updating the weights based on the gradient of the error with respect to the weights.

  5. Repeat steps 2-4 for all training examples.

  6. Repeat steps 2-5 for multiple epochs until the error converges.

  7. Use the trained network to make predictions on new, unseen data.

Where to Learn More#

I’ve covered artificial neural networks in-depth in the following course:

Data Science: Deep Learning and Neural Networks in Python