Scientia est Potentia

Pandas is a data manipulation and analysis library for python. It is heavily used by the Machine Learning community to clean, transform, and analyze structured data. Why should you read this post? I’ve been using pandas for working with structured data like Electronic Health Records (EHR), insurance... [Read More]

The first article in this series focused on the general mechanism of RNN, architectures, variants and applications. The objective was to abstract away the details and illustrate the high-level concepts in RNN. Naturally, the next step is to dive into the details. In this article, we will follow a... [Read More]

We can go a long way in Machine Learning without having to deal with the scary probabilistic expressions. But, in order to be able to read the bleeding edge research as soon as it is published, and not from a blog, months later, we need to able to speak the... [Read More]

Update : Part 2 is now available at Unfolding RNNs II : Vanilla, GRU, LSTM RNNs from scratch in Tensorflow. RNN is one of those toys that eluded me for a long time. I just couldn’t figure out how to make it work. Ever since I read Andrej Karpathy’s... [Read More]

In my last article, I talked a bit about the theoretical aspect of the famous Sequence to Sequence Model. I have shared the code for my implementation of seq2seq - easy_seq2seq. I have adopted most of the code from en-fr translation example provided by Google. Hence, most... [Read More]