From the course: Computer Vision for Data Scientists
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Backpropagation in CNNs
From the course: Computer Vision for Data Scientists
Backpropagation in CNNs
- Backpropagation is why deep learning works. It's a critical algorithm in training convolutional neural networks. It calculates and propagates gradients through the network to update its parameters. The algorithm works by applying the chain rule of calculus, computing the gradients of the loss function with respect to each parameter in the network. These gradients are then used to adjust the parameters, minimizing the loss function and improving the model's performance. In the context of CNNs, backpropagation works differently through layers from convolutional, pooling, and fully connected layers. Backpropagation, which only happens during training, involves several steps that helps the network make accurate predictions. First, the input data goes through each layer where the operations, such as convolution, pooling, and nonlinear activations are performed to generate an output. The final layer produces the network's…
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