LSTM Cell – with a magnifier!


Today, we’ll be talking about LSTM Networks QnA Style. The motivation for this is again as I have seen often when people read about LSTM’s, they have more questions than they have answers for. So, here I will try to give a gist of LSTM networks in comparison to FFN(Feed Forward Network  or a regular NN).

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Demystifying Regularisation!

The motivation for this specific topic?
Often when I connect around, the answer to ‘When to use Regularization’ is ‘To prevent overfitting of model’. While that is true, it is important to understand how it works and get a sense to have an overall understanding of your model.

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Machine Learning : How NOT to get started with Machine Learning.

Over past year, I have seen quite a number of folks start with Data Science.  And there are plenty of articles indicating the surface area of the entire domain. Many start, but few continue.  Here, I try to list some traps that could stall an aspiring Data Scientist’s progress. As always, feel free to share any feedback you have.

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Re-Structuring Machine Learning Execution

Recently, I gained some insight on Structuring Machine Learning projects. How I wish I had this insight when we did some experiments in ML domain in not so distant past. Anyways, I wouldn’t want anybody else to get hit by the same stones, so below is a crux of what I think I have understood.

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Before starting Andrew Ng’s ML Course…

If you are thinking of starting ML, without a doubt Andrew Ng’s Course on Coursera. is the best place to start.

However, a couple of things below that should ease your journey.

  • Make sure you complete at least 4 Weeks. The first 2 assignments are the mountain that you must scale before witnessing the beautiful horizons of ML.
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