Glossary

Residual Neural Network (ResNet)

Residual Neural Network or ResNet is a type of neural network architecture that is used in machine learning and computer vision. It is a deep learning algorithm that is designed to address the problem of vanishing gradients, which is a common issue faced by deep neural networks.

The vanishing gradient problem occurs when the gradients of the loss function become very small as they propagate through multiple layers of a deep neural network. This leads to slow convergence and poor performance of the network. To overcome this problem, ResNet introduces skip connections, which allow the gradients to flow directly from one layer to another, bypassing the intermediate layers.

The skip connections in ResNet help to preserve the information from the earlier layers, which can be lost in deep neural networks that do not use skip connections. This allows ResNet to build much deeper networks than was previously possible, resulting in better accuracy and performance.

ResNet has been widely used in image classification, object detection, and other computer vision tasks. It has achieved state-of-the-art performance in these tasks, and is now considered to be one of the most important neural network architectures in the field of deep learning.

In summary, Residual Neural Network or ResNet is a powerful deep learning algorithm that addresses the problem of vanishing gradients by introducing skip connections. It has proven to be highly effective in a wide range of computer vision tasks, and is an important tool in the field of machine learning.

A wide array of use-cases

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