Should I study NLP or Computer Vision?

Both Computer Vision and NLP (natural language processing) have been good at tackling certain circumscribed tasks. Still, they are both progressing at a rather slow speed and the NLP field is even lesser than computer vision. So, Computer Vision matures faster because of: Solid accuracy in problem-solving.

Is there demand for Computer Vision?

There hundreds of uses for computer vision. In terms of career prospects, it’s huge! Nowadays computer vision experts or researchers are in huge demand and that demand will keep on growing! Computer vision is a slightly broad term for one simple objective, “Making computers see as well or better than humans”.

Do I need a PhD for NLP?

Do I Need a PhD to Work on NLP? “Having a PhD is not 100\% necessary. Data science in general is such a new idea to a lot of people in the world, and the science part isn’t 100\% there yet. Therefore, [PhDs working in NLP] tend to approach problems from a mathematical standpoint.

Which is harder Computer Vision or NLP?

, AI researcher. It depends because both computer vision (CV) and natural language processing (NLP) are extremely hard to solve. In reality, problems like 2D bounding box object detection in computer vision are just simplified versions of the much more advanced aspect of vision.

What is the difference between NLP and computer vision?

NLP tasks are more diverse as compared to Computer Vision and range from syntax, including morphology and compositionality, semantics as a study of meaning, including relations between words, phrases, sentences and discourses, to pragmatics, a study of shades of meaning, at the level of natural communication.

Do you need publications for PhD?

The short answer is no. Publications are not required to apply for a PhD. The longer answer is that the admissions committee wants to see that you have the potential to become an excellent researcher. While publications are one indication of this, they are not the only way to show that you have strong research skills.