Deep Residual Networks (ResNet) are a type of neural network architecture for machine learning. It is designed to be more efficient than other types of networks such as Convolutional Neural Networks and Convolutional Long Short-Term Memory networks.
ResNet is a type of deep neural network that can be used for tasks such as image classification, object detection, and semantic segmentation. The architecture of the network is designed to be more efficient than other types of networks such as Convolutional Neural Networks and Convolutional Long Short-Term Memory networks.
Residual networks are a type of deep neural network that is designed to be computationally efficient. They are designed for tasks such as image recognition, object detection, and classification. ResNet is one of the most successful implementations of this type of neural network.
A residual network is a type of deep neural network that is designed to be computationally efficient. They are designed for tasks such as image recognition, object detection, and classification. ResNet is one of the most successful implementations of this type of neural network.
The Residual Network (ResNet) is a type of deep neural network that was developed in 2016. It has the same structure as the original Convolutional Neural Network, but it has skip connections between layers.
It is used for tasks such as image classification, object detection, and speech recognition.
Key points:
- ResNet stands for the residual neural network which was introduced by Kaiming at the ILSVRC 2015.
- In the beginning, it was trained a neural network with 152 layers while still having lower complexity than VGG net
- it achieved an error rate of 3.57% only and managed to be in the top 5 which beats human-level performance on the status set.
- There are a total of four types of ResNet: ResNet 34, ResNet50, ResNet 101, and ResNet 152.
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