Backpropagation step by step: the forward and backward pass (fremaconsulting.ch)

A recent article discusses the step-by-step process of backpropagation in a multilayer perceptron (MLP), illustrating how it helps in training neural networks by minimizing loss. It explores how the forward pass computes outputs through layers using linear transformations and non-linear activations like ReLU, followed by softmax to generate class probabilities. The backpropagation process utilizes the chain rule to compute gradients efficiently, enabling the adjustment of weights based on the loss derivative relative to the weights. This method is crucial for training deep networks as it overcomes challenges like the vanishing and exploding gradient problems. Significantly, the article highlights the cost of computations during training. Each backward sweep must retain values from the forward pass, which raises memory demands, particularly for large networks. The analysis shows that while the gradient can be computed relatively quickly per layer, memory usage can become a limiting factor, especially with larger batch sizes. This insight underscores the ongoing challenges in optimizing neural network training and contributes to our understanding of the computational demands of modern AI systems, informing future designs and strategies for efficiency in AI/ML applications.