Read on Twitter
I just want junior and aspiring machine learning engineers to know that 80% of the advice I see in the "machine learning" topic on Twitter is bad, and a good 30% is just outright false.
So if you're looking and you see like TWELVE TOPICS YOU MUST UNDERSTAND or TEN ESSENTIAL MATH FUNDAMENTALS of RIGHT COURSES EVERY ML ENGINEER MUST HAVE
those are universally wrong and confusing, hyperfit to the hype and current use case of the tweeter
those are universally wrong and confusing, hyperfit to the hype and current use case of the tweeter
And also *deep breath" AI AND DEEP LEARNING ARE NOT INTERCHANGABLE
AI existed decades before deep learning and sure, AI can use deep learning
but deep learning is NOT the only or even best way to build AI. That is use case dependent.
AI existed decades before deep learning and sure, AI can use deep learning
but deep learning is NOT the only or even best way to build AI. That is use case dependent.
Like just
Hello
It is me! A professional machine learning engineer. I know nothing about computer vision from the past 10 years and learning it ahead of a defined use case would be a waste of my time.
Look at me, out here violating every requirements list. You can too!
Hello
It is me! A professional machine learning engineer. I know nothing about computer vision from the past 10 years and learning it ahead of a defined use case would be a waste of my time. Look at me, out here violating every requirements list. You can too!
Most ML engineers are specialists in a domain of problems.
And if I encountered a vision use case at work, I would learn then. Like when we switched from doing text NLP to text AND voice, so I learned a lot about speech.
You don't show up day 1 on the job knowing everything.
And if I encountered a vision use case at work, I would learn then. Like when we switched from doing text NLP to text AND voice, so I learned a lot about speech.
You don't show up day 1 on the job knowing everything.
