backpropagation the backpropagation algorithm is popular technique to train anns and it led to resurgence in the popularity of neural networks in the the algorithm typically has two main stages--propagation and weight updates they are described briefly as follows propagation the input data sample vectors are propagated forward through the neural network to generate the output values from the output layer compare the generated output vector with the actual/desired output vector for that input data vector compute difference in error at the output units backpropagate error values to generate deltas at each node/neuron weight update compute weight gradients by multiplying the output delta (errorand input activation use learning rate to determine percentage of the gradient to be subtracted from original weight and update the weight of the nodes these two stages are repeated multiple times with multiple iterations/epochs until we get satisfactory results typically backpropagation is used along with optimization algorithms or functions like stochastic gradient descent multilayer perceptrons multilayer perceptronalso known as mlpis fully connectedfeed-forward artificial neural network with at least three layers (inputoutputand at least one hidden layerwhere each layer is fully connected to the adjacent layer each neuron usually is non-linear functional processing unit backpropagation is typically used to train mlps and even deep neural nets are mlps when they have multiple hidden layers typically used for supervised machine learning tasks like classification convolutional neural networks convolutional neural networkalso known as convnet or cnnis variant of the artificial neural networkwhich specializes in emulating functionality and behavior of our visual cortex cnns typically consist of the following three components multiple convolutional layerswhich consist of multiple filters that are convolved across the height and width of the input data ( image raw pixelsby basically computing dot product to give two-dimensional activation map on stacking all the maps across all the filterswe end up getting the final output from convolutional layer