Vanishing Gradient explained using Code!

YouTube Excerpt: I'll explain Vanishing Gradient Problem in Neural Networks using Keras & Python. It arises when more layers using sigmoid or tanh activation functions are added to neural networks, the gradients of the loss function approaches zero, making the network hard to train. The Problem could be solved by using a ReLU activation function instead of a Sigmoid or TanH Function. Notebook Link : https://github.com/bhattbhavesh91/vanishing-gradient-problem/ If you do have any questions with what we covered in this video then feel free to ask in the comment section below & I'll do my best to answer those. If you enjoy these tutorials & would like to support them then the easiest way is to simply like the video & give it a thumbs up & also it's a huge help to share these videos with anyone who you think would find them useful. Please consider clicking the SUBSCRIBE button to be notified for future videos & thank you all for watching. You can find me on: Blog - http://bhattbhavesh91.github.io Twitter - https://twitter.com/_bhaveshbhatt GitHub - https://github.com/bhattbhavesh91 Medium - https://medium.com/@bhattbhavesh91 #deeplearning #vanishinggradient

I'll explain Vanishing Gradient Problem in Neural Networks using Keras & Python. It arises when more layers using sigmoid or tanh activation...

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