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What is Residual Connection?

Towards Data Science

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…

Weight Decay is Useless Without Residual Connections

Towards Data Science

How do residual connections secretly fight overfitting? Photo by ThisisEngineering RAEng on Unsplash Introduction The idea in broad strokes is fairly simple: we can render weight decay practically us...

UNDERSTANDING RESIDUAL NETWORKS

Towards Data Science

Image Recognition has advanced in recent years due to availability of large datasets and powerful GPUs that has enabled training of very deep architectures. Simonyan et al. authors of VGG…

Residual Networks (ResNets)

Towards Data Science

In earlier posts, we saw the implementation of LeNet-5, AlexNet, and VGG16 which are deep convolutional neural networks. Similarly, we can build our own deep neural network with more than 100 layers…

Did You Know There Are At Least 5 Kinds Of Skip Connections?

Towards AI

If you’ve ever worked with deep neural networks, you’ve probably wrestled with vanishing gradients, exploding gradients, or just plain sluggish training. Training neural networks is a bit of an art, b...

What is Residual Network or ResNet? — Idiot Developer

Analytics Vidhya

Deep neural networks have become popular due to their high performance in real-world applications, such as image classification, speech recognition, machine translation and many more. Over time deep…

Residual Networks in Computer Vision

Towards Data Science

Deep Convolutional Neural Networks changed the research landscape significantly for image classification [1]. As more levels were added, the expressiveness of the model increased; it was able to…

Residual Network: Implementing ResNet

Towards Data Science

Today we are going to implement the famous ResNet from Kaiming He et al. (Microsoft Research) in Pytorch. It won the 1st place on the ILSVRC 2015 classification task. Code is here, an interactive…

Understanding Residual Networks (ResNets) Intuitively

Towards Data Science

ResNets or Residual networks are the reason we could finally go very, very deep in neural networks. Everybody needs to know why they work, so, they can take better decisions and make sense of why…

Paper Walkthrough: Residual Network (ResNet)

Python in Plain English

Implementing Residual Network from scratch using PyTorch. Photo by Patrick Federi on Unsplash In today’s paper walkthrough, I want to talk about a popular deep learning model: Residual Network. Here ...

Understanding and implementation of Residual Networks(ResNets)

Analytics Vidhya

Residual learning framework to ease the training of networks that are substantially deeper than those used previously. This article is primarily based on research paper “Deep Residual Learning for…

Intuition behind Residual Neural Networks

Towards Data Science

Deep Neural Networks — “deep” because of large number of layers, have come a long way in lot of Machine Learning tasks. But how deep? Let’s see the popular case of Image Classification: AlexNet…