Hi there 👋
- 🔭 I’m currently working on patent analysis with NLP frameworks
- 🧑🏻💻 I have done many projects of applying machine learning and NLP algorithms in different data-driven projects
- 👯 I’m open to new opportunities in the field of NLP, Machine Learning and Data Science
- 😍 I am a big fan of FAIR data principle and Knowledge Graph with Ontology Engineering Mindset
⚡ Fun fact:
In the 80s and 90s, engineers built complex systems by combining simple and well-studied “parts”. A good engineer is better to know everything from small "parts" to the final "making". I used to be a big fan of pursuing the synthezied knowledge and skills. However, after many years of learning and practice, I learned a very hard lesson that this synthezied method of learning no longers prepare engineers for what “engineering” today is.
Now engineers usually write code for complex hardware, which they do not fully understand (and often this happens because of trade secrets, and not because of laziness or lack of time - take the same Apple and its technology). The same statement is true for software, since software environments consist of giant libraries with the broadest functionality. According to Sassman, today his students spend most of their time reading manuals for these libraries to figure out how to link them together with a simple goal - so that everything works and does what they need.
According to Sassman, “Programming today is more like science: you take part of the library and“ poke ”it - look at what it does. Then you ask yourself, “Can I customize it so that it does what I need?”. The “analysis through synthesis” approach used by SICP, when you build a large system from simple, small parts, has become irrelevant. Today we program by “poke method”.
In the 2000s, Python was chosen as an alternative to Lisp in MIT. In favor of the language of teachers, the fact that a significant number of libraries are available for Python that allow you to use it for solving exercises in a wide variety of types of projects (for example, for writing robot control software).
Today, firms and academic communities are the organizers of synthezied knowledge. In computer science, no single man should still try to know both hardware and software well.
Alas, THIS IS THE WAY.
吾生也有涯，而知也无涯。以有涯隨无涯，殆已；已而為知者，殆而已矣。