ResNet Using Keras | Residual Network | Convolutional Neural Network

In this video we go through how to code the ResNet model and in particular ResNet50 from scratch using jupyter notebook.
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ResNet50:
ResNet, short for Residual Networks is a classic neural network used as a backbone for many computer vision tasks. This model was the winner of ImageNet challenge in 2015
ResNet50 is a variant of ResNet model which has 48 Convolution layers along with 1 MaxPool and 1 Average Pool layer.
In 2012 at the LSVRC2012 classification contest AlexNet won the the first price, After that ResNet was the most interesting thing that happened to the computer vision and the deep learning world.
Because of the framework that ResNets presented it was made possible to train ultra deep neural networks and by that i mean that i network can contain hundreds or thousands of layers and still achieve great performance.However, increasing network depth does not work by simply stacking layers together. Deep networks are hard to train because of the notorious vanishing gradient problem as the gradient is back-propagated to earlier layers, repeated multiplication may make the gradient extremely small. As a result, as the network goes deeper, its performance gets saturated or even starts degrading rapidly.
Skip Connection - The Strength of ResNet
ResNet first introduced the concept of skip connection. The main innovation of ResNet is the skip connection. As you know, without adjustments, deep networks often suffer from vanishing gradients, ie: as the model backpropagates, the gradient gets smaller and smaller. Tiny gradients can make learning intractable. It allows the network to learn the identity function, which allows it pass the the input through the block without passing through the other weight layers!
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