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!”
Have you ever wondered why does the cost function under gradient descent really looks like the way it does? If yes, today we will be building this up the right way. This is going to be a little longer, but totally worth it. So grab your coffee.Continue reading “Causing the COST Function”
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.