April 13, 2018
I’ve answered this question thousands of times.
I feel as though, because I repeat “prerequisites” so often, students stop believing me.
They wonder, “I took some other Udemy course on ML by some marketer guy and I learned so much! Why can’t you do the same?”
Unfortunately, they haven’t realized the truth: they didn’t learn machine learning, they learned some marketer’s interpretation of machine learning.
It didn’t lead to employable skills, and it didn’t lead to a proper understanding of ML.
What happens after?
Then you try to take a more advanced ML course (i.e. something at the college-level, or perhaps my courses).
Then you realize, “Hey, this is nothing like the course I took before. Why is there so much math?”
You expected all of ML to be as easy as the marketer’s course, because you didn’t understand that it wasn’t a real ML course.
I want to share a forum thread that I think should be useful, because instead of hearing it from me, now you can hear it from other people saying the exact same things.
The top comment in this discussion states the same topics I list in the prerequisites for nearly all of my ML courses:
- Calculus
- Linear Algebra
- Probability
Enjoy: https://news.ycombinator.com/item?id=15116379
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April 11, 2018
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April 2018
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April 10, 2018
A lot of students come up to me and ask about when they’re going to learn the latest and greatest new deep learning algorithm.
Sometimes, it’s easy to forget how applicable even the most basic of tools are.
As you know, I consider Linear Regression to be the best starting point for deep learning and machine learning in general.
And wouldn’t you know, here it is being used in the most advanced, state-of-the-art video codec we have today:
next generation video: Introducing AV1
Check it out!
This new state-of-the-art video codec is based on research done by multiple big companies, such as Google, Cisco, and Mozilla.
As you can see, the final equation is just a line (\( y = mx + b \)).
$$ CfL(\alpha) = \alpha L^{AC} + DC $$
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