I keep switching my experiments between Computer Vision, Natural Language Processing & Machine Learning, Last week I found myself fiddling with Natural Language Processing and I thought of creating a simple topic extractor to extract the topics from an input body of text.
The idea was to build something that even 10-year-old could understand and easily reproduce it on their laptop swiftly. In my opinion, getting something to work on your machine is more satisfying that just skimming through an article theoretically.
PS — I also have a bonus addition to this article to turn the extracted topics into an…
In this article, we will be discussing various possible sequence to sequence architectures for implementing Machine Translation. Even though the primary problem that we will try to solve at hand would be Machine Translation, but the same Architectures, with slight modifications, also apply to other Machine Learning use-cases such as, but not limited to:
· Text Summarisation — a model to produce a summary of the input text
· Question Answering — a model to produce an answer to an input question
· Dialog — a model to generate the next dialogue/utterance in the sequence
· Document Classification — a…
The job of Portfolio Managers & Credit Risk officers is quite daunting. They, at all times, need to be upbeat about current Market conditions. These investment banking professionals are deemed to be experts in Technical & Fundamental Analysis of stocks/instruments/counterparties/issuers or at least, are adept at handing various COTS (Commercial off-the-shelf) tools to churn out numbers/metrics and recognize market patterns to help guide their investment decisions.
Armed with this data, Portfolio Managers make decisions of adding/removing instruments from their Investment Portfolio where as, Credit Officers set/adjust trading limits or extend facilities to the Counterparties that their investment bank is interested…