Recently, I have seen many folks wanting to start with data science, study concepts/tests, but often cannot intuitively infer where to use a particular test, validation method when working with data.Continue reading “The Mystic Stats – Pilot”
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).Continue reading “LSTM Cell – with a magnifier!”
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.
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.Continue reading “Machine Learning : How NOT to get started with Machine Learning.”
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.Continue reading “Re-Structuring Machine Learning Execution”
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.