Building RestNets in Keras step by step

Welcome to another tutorial! Now we will learn how to build very deep convolutional networks, using Residual Networks (ResNets). This model was the winner of the ImageNet challenge in 2015. The fundamental breakthrough with ResNet was it allowed us to train extremely deep neural networks with 150+layers successfully. Prior to ResNet training very deep neural networks were difficult due to the problem of vanishing gradients.

However, increasing network depth does not work by simply stacking layers together. Deep networks are hard to train because of the 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

In this tutorial, we will:
Implement the basic building blocks of ResNets.
Put together these building blocks to implement and train a state-of-the-art neural network for image classification.

Text version tutorial: https://pylessons.com/Tensorflow-Kera...
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