Overview
The Residual Learning Framework is a significant advancement in deep learning, allowing for the effective training of very deep neural networks. By introducing skip connections, it addresses the vanishing gradient problem, which often hampers the training of traditional deep networks. This framework...
Key Terms
Example: In a residual block, the output is the sum of the input and the output of the convolutional layers.
Example: Skip connections help in preserving information across layers in deep networks.
Example: Gradient descent updates the weights of the network based on the gradient of the loss.
Example: Common activation functions include ReLU, Sigmoid, and Tanh.
Example: Backpropagation is essential for training neural networks effectively.
Example: Deep learning is used in applications like image classification and natural language processing.