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Two Sides of the Same Coin: Jeremy Howard’s fast.ai vs Andrew Ng’s deeplearning.ai

How Not to ‘Overfit’ Your AI Learning by Taking Both fast.ai and deeplearning.ai courses

Michael Li
Towards Data Science
6 min readSep 24, 2019

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Data science and artificial intelligence might be the hottest topic in tech right now, and rightfully so. There are tremendous breakthroughs both on application level and research fields. This is a blessing, and a curse, at least for students and enthusiasts that want to break into this area. There are too many algorithms to learn, too many coding/engineering skills to hone, and way too many new papers to keep up with even if you felt you’ve mastered the art.

The journey is long, the learning curve is steep, the strife is real, yet the potential is so great people still flock into it. The good thing is we also have great educators and instructors working on mitigating the pain and make the process a little less harsh and a bit more fun. We’ll explore two of the greatest among them and share a potentially effective approach to help you swim through the sea of Data Science a bit happier.

AI Learning ‘Burn-out’

a cup of coffee is needed if you’re burned out
Photo by Toa Heftiba on Unsplash

If you list what one needs to learn to become an ‘OK’ data scientist or machine learning engineer, it could be scarily long:

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Towards Data Science
Towards Data Science

Published in Towards Data Science

Your home for data science and AI. The world’s leading publication for data science, data analytics, data engineering, machine learning, and artificial intelligence professionals.

Michael Li
Michael Li

Written by Michael Li

Data Scientist | Blogger | Product Manager | Developer | Pentester | https://www.linkedin.com/in/michael-li-dfw

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