Salman is a Chartered Financial Analyst and heads up the Tech Team at Thomson Reuters in Singapore. He has led over 20 risk, trading and technology implementations from start to finish over his career. As the Head of the Tech Team at Thomson Reuters, Salman combines his rare skill set of strong knowledge in technology and finance to formulate unique solutions. He holds a degree and a number of professional qualifications in the fields of Computer Science, Machine Learning, Big Data and Technology from King's College London, Imperial College and the Massachusetts Institute of Technology.
As the Text Metadata Services (TMS) Lead in Singapore, I wanted to share my knowledge by writing a short piece on an important aspect of Natural Language Processing (NLP) called Named Entity Recognition (NER). Named Entity Recognition is not to be confused with Named Entity Resolution. A simple example to distinguish between the two is that a machine reading a document might recognize a person, say William Henry Gates and a second person in the same document, say Bill Gates. Named Entity Resolution is a way in which these two names can be resolved to a single person whom we all know as one of the founders of Microsoft, Bill Gates.
Bitcoin, like Gold is mined. There are finite amounts of each and there are costs of mining both. Gold is used as a trade currency, investment currency and a reserve currency. Bitcoin is rarely traded for real goods, barely classifies as an investment and is far from a reserve currency, despite what companies such as Magister Advisors would have you think.
Over the last year I've had the pleasure of getting an in depth understanding of the product development process at a small startup (just under 4 years old). I've had the chance to get involved in Design Sessions, Market Segmentation, User Journey, Minimum Viable Products (MVP), Unique Selling Propositions (USP) and Value Propositions.
In the 21st Century, the Data Science community commonly cited the three main ingredients for developing a good machine learning model: 1. Good quality data, preferably labelled 2. High Power Computing 3. Efficient, Precise and Accurate Algorithms