Why Initialize a Neural Network with Random Weights || Quick Explained

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What if you initialize weight in Neural Network with zeros.
What will be the consequences or why we initialize weights randomly?
In this video, I've explained why it's important to initialize weights randomly (from normal distribution) so that a Network can't form symmetry.

This video includes explanations as well as experimental proof about what will happen if initial weight will be zero.

Stay tuned:

Thanks.

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Please leave feedback if you can. It means a lot to me

DevelopersHutt

This channel is clearly underrated.. Kudos bro..

MsPOOJA

You are doing an amazing job. This way of first explaining the concept and then showing its implementation via proper code is superb!! Keep growing.

karangoyal

Great! you're single video was enough to break the symmetry

vipingautam

Simple and Wonderful Explanation. Great Thanks!

eleanortay

Thanks alot brother.. love you lot.. keep going.. ❤️❤️❤️

mandarchincholkar

Make Playlist for building neural network from scratch. Without using any library

neerajverma

Low curvature initialization is a good idea. Random initialization is very high curvature and it is very difficult to smooth that out.

hoaxuan

Thanks for your clear explanation.
Here I have a question about activation functions:
In case we are doing a non-linear regression and dealing with a "PDE of 2-3 degrees of derivatives", can we use a non-differentiable activation function such as ReLU, or we should necessarily use an infinitely differentiable one like Tanh?

AmirhosseinKhademi-ings

Off topic: There is a thing called Fast Transform fixed filter bank neural nets. I can't go into details because that leads to comment removal.

hoaxuan

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