I intend to create multiple videos and code tutorials around this topic, but for now here's a simple definition...

Backpropagation, short for "backward propagation of errors," is a supervised learning algorithm commonly used for training artificial neural networks. It is a key component of many machine learning models, particularly in the field of deep learning.

The purpose of backpropagation is to minimize the error of the neural network's output by adjusting the weights of the connections between neurons. The process involves propagating the error backward from the network's output layer to its input layer, updating the weights in the process. This iterative optimization process helps the neural network learn and improve its ability to make accurate predictions.

The process of backpropagation is typically viewed as occurring in three distinct stages:

1) Forward Pass

The input data is fed forward through the network, layer by layer, producing an output. The output is compared to the actual target or ground truth to calculate the error.

2) Backward Pass

The error is propagated backward through the network. The partial derivatives of the error with respect to the weights are calculated using the chain rule of calculus.

3) Weight Update:

The weights are adjusted in the opposite direction of the gradient of the error with respect to the weights. The learning rate determines the size of the weight updates. Iteration:

Steps 1-3 are repeated for multiple iterations (epochs) until the model's performance converges to a satisfactory level.

The backpropagation algorithm is a form of gradient descent optimization. It adjusts the weights of the neural network to minimize the error function (loss function) by finding the steepest descent in the multidimensional weight space. The learning rate is a crucial hyperparameter in this process, as it determines the size of the steps taken during the weight updates. Too large a learning rate may cause the algorithm to overshoot the minimum, while too small a learning rate may result in slow convergence.

Backpropagation has been instrumental in the success of training deep neural networks, enabling the development of complex models capable of learning intricate patterns in large datasets. It is a foundational concept in the training of neural networks for a wide range of tasks, including image recognition, natural language processing, and more.