ResNet : Convolution Neural Network

ResNet, also known as residual neural network, refers to the idea of ​​adding residual learning to the traditional convolutional neural network,

ResNet, also known as residual neural network, refers to the idea of ​​adding residual learning to the traditional convolutional neural network, which solves the problem of gradient dispersion and accuracy degradation (training set) in deep networks, so that the network can get more and more The deeper, both the accuracy and the speed are controlled.

*The problem caused by increasing depth *:

• The first problem brought by increasing depth is the problem of gradient explosion / dissipation . This is because as the number of layers increases, the gradient of backpropagation in the network will become unstable with continuous multiplication, and become particularly large or special. small. Among them , the problem of gradient dissipation often occurs. i.e effect of the weight decreases.

ai deep-learning convolution-neural-net resnet50 cnn

In this video I present a simple example of a CNN (Convolutional Neural Network) applied to image classification of digits. CNN is one of the well known Deep Learning algorithms.

Project walk-through on Convolution neural networks using transfer learning. From 2 years of my master’s degree, I found that the best way to learn concepts is by doing the projects.

PyTorch For Deep Learning — Convolutional Neural Networks ( Fashion-MNIST ). This blog post is all about how to create a model to predict fashion mnist images and shows how to implement convolutional layers in the network.

In this blog, I’ll show how to build CNN model for image classification.

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