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What is Residual Connection?. A technique for training very deep… | by Wanshun Wong | Towards Data Science

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One of the dilemmas of training neural networks is that we usually want deeper neural networks for better accuracy and performance. However, the deeper the network, the harder it is for the training to converge. In this article we will talk about residual connection (also known as skip connection), which is a simple yet very effective technique to make training deep neural networks easier. It is widely adopted by different models, from ResNet in computer vision where it was first introduced, Transformer in natural language processing, all the way to AlphaZero in reinforcement learning and AlphaFold for protein structure predictions. In traditional feedforward neural networks, data flows through each layer sequentially: The output of a layer is the input for the next layer. Residual connection provides another path for data to reach latter parts of the neural network by skipping some layers. Consider a sequence of layers, layer i to layer i + n, and let F be the function represented by

What is Residual Connection? | Towards Data Science Skip to content What is Residual Connection? A technique for training very deep neural networks Wanshun Wong Dec 18, 2021 6 min read Share One of the dilemmas of training neural networks is that we usually want deeper neural networks for better accuracy and performance. However, the deeper the network, the harder it is for the training to converge. In this article we will talk about residual connection (also known as skip connection), which is a simple yet very effective technique to make training deep neural networks easier. It is widely ado

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