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This looks really cool, and kudos to them making it free. However, this seems very much applied, and I'd really like a course that dives more into the math and

by NickM 8y ago

This looks really cool, and kudos to them making it free. However, this seems very much applied, and I'd really like a course that dives more into the math and theory of AI, neural networks, deep learning, etc. etc. Anyone have any recommendations for any video lecture series, MOOCs, or anything like that?

deleted 8y ago
[deleted]
Retric 8y ago
The importance of math in understanding AI is kind of overblown. Binary Neural Networks for example are in theory worse, but the savings in computational time makes up for quite a bit. That said, a wide foundation including a deep understanding of linear algebra is more useful than just covering the specifics relevant to AI.
- globuous 8y ago
  That makes sense. But for me for instance, who's trying to learn about AI and have a math background, I like courses that explain the math behind everything because it makes it easier to understand the concepts. It's also a good way to check that I understand what's going on. And finally, I find it easier to skim over a text has equations, they easier to parse for me since I'm a slow prose reader. So I can go through a text by skimming through the math, and when I stop understanding, I try to backtrack what's going on and only move on once I understand what was blocking me. EDIT: And this applies to everything. Ex: I've been playing the guitar for over a decade but have been struggling with music theory. I've recently tried to apply the little algebra I know to it to try and find structure. I've found some really cool articles, for instance [0], and it's helped quite a bit believe it or not !! For French speakers that like music, math, and algorithms, I highly encourage a presentation by Moreno Andreatta [1] where he closes the presentation by performing a "rotation around the Do of the Beatles' Hey Jude". [0] http://repmus.ircam.fr/_media/moreno/BigoAndreatta_Computational_Musicology.pdf http://repmus.ircam.fr/_media/moreno/BigoAndreatta_Computati... [1] https://www.youtube.com/watch?time_continue=3528&v=cFiT9StEy00 https://www.youtube.com/watch?time_continue=3528&v=cFiT9StEy...
  - dalbasal 8y ago
    Recent report from my mother, who's a physicist with a strong math background, no statistics: Take the coursera intro course. She said that the first hour or two were a little frustrating for the kinds of reasons you mention. After that, she could skip the "long" explanations & examples by going to the math directly. You'll progress 5X faster than normal after that. She "got it" by creating ML "proofs" for math problems without doing math. She didn't like all the image recognition examples. Said it's a confusing place to start. You are probably stuck with learning from a source that assumes you don't know math, because most people (including me) don't. This course is probably not for you if you want theory, as it's focused on "using" ML algorithms, not writing them. That said, this might be useful to you if you already have stuff that you want to do, and need a way of doing the "sticks and duct tape."
    - NickM 8y ago
      Sorry, which Coursera intro course?
      - dalbasal 8y ago
        "Machine Learning" by Andrew Ng, Standford https://www.coursera.org/learn/machine-learning https://www.coursera.org/learn/machine-learning
    - globuous 8y ago
      Thanks so much for your answer, that's exactly what I'm looking for !!
dahart 8y ago
The Stanford course is pretty good. https://youtu.be/vT1JzLTH4G4 https://youtu.be/vT1JzLTH4G4 For something quicker & less applied, maybe the 3b1b series on neural networks: https://youtu.be/aircAruvnKk https://youtu.be/aircAruvnKk
Jagat 8y ago
You should consider taking this course series "Mathematics for Machine Learning" from coursera. https://www.coursera.org/specializations/mathematics-machine-learning https://www.coursera.org/specializations/mathematics-machine... However, if you know the very basics of matrices (multiplication, transpose) and calculus (derivatives of basic functions, and partial derivates, and chain rule) I'd highly recommend first trying basic applied ML before diving deep into the math. It'll help you see where the math you're learning is actually used, as you learn them. Try deeplearning.ai first, then try this "math for ML" course.
ForHackernews 8y ago
"Learning from Data" is a real Caltech course on Machine Learning - http://www.work.caltech.edu/telecourse.html http://www.work.caltech.edu/telecourse.html It's sometimes offered concurrently with on campus sessions, and it is not a watered-down MOOC or a tutorial for some company's toolkit.

by NickM 8y ago

deleted 8y ago

[deleted]

Retric 8y ago

The importance of math in understanding AI is kind of overblown. Binary Neural Networks for example are in theory worse, but the savings in computational time makes up for quite a bit. That said, a wide foundation including a deep understanding of linear algebra is more useful than just covering the specifics relevant to AI.

globuous 8y ago
That makes sense. But for me for instance, who's trying to learn about AI and have a math background, I like courses that explain the math behind everything because it makes it easier to understand the concepts. It's also a good way to check that I understand what's going on. And finally, I find it easier to skim over a text has equations, they easier to parse for me since I'm a slow prose reader. So I can go through a text by skimming through the math, and when I stop understanding, I try to backtrack what's going on and only move on once I understand what was blocking me. EDIT: And this applies to everything. Ex: I've been playing the guitar for over a decade but have been struggling with music theory. I've recently tried to apply the little algebra I know to it to try and find structure. I've found some really cool articles, for instance [0], and it's helped quite a bit believe it or not !! For French speakers that like music, math, and algorithms, I highly encourage a presentation by Moreno Andreatta [1] where he closes the presentation by performing a "rotation around the Do of the Beatles' Hey Jude". [0] http://repmus.ircam.fr/_media/moreno/BigoAndreatta_Computational_Musicology.pdf http://repmus.ircam.fr/_media/moreno/BigoAndreatta_Computati... [1] https://www.youtube.com/watch?time_continue=3528&v=cFiT9StEy00 https://www.youtube.com/watch?time_continue=3528&v=cFiT9StEy...
- dalbasal 8y ago
  Recent report from my mother, who's a physicist with a strong math background, no statistics: Take the coursera intro course. She said that the first hour or two were a little frustrating for the kinds of reasons you mention. After that, she could skip the "long" explanations & examples by going to the math directly. You'll progress 5X faster than normal after that. She "got it" by creating ML "proofs" for math problems without doing math. She didn't like all the image recognition examples. Said it's a confusing place to start. You are probably stuck with learning from a source that assumes you don't know math, because most people (including me) don't. This course is probably not for you if you want theory, as it's focused on "using" ML algorithms, not writing them. That said, this might be useful to you if you already have stuff that you want to do, and need a way of doing the "sticks and duct tape."
  - NickM 8y ago
    Sorry, which Coursera intro course?
    - dalbasal 8y ago
      "Machine Learning" by Andrew Ng, Standford https://www.coursera.org/learn/machine-learning https://www.coursera.org/learn/machine-learning
  - globuous 8y ago
    Thanks so much for your answer, that's exactly what I'm looking for !!

dahart 8y ago

The Stanford course is pretty good. https://youtu.be/vT1JzLTH4G4 https://youtu.be/vT1JzLTH4G4 For something quicker & less applied, maybe the 3b1b series on neural networks: https://youtu.be/aircAruvnKk https://youtu.be/aircAruvnKk

Jagat 8y ago

You should consider taking this course series "Mathematics for Machine Learning" from coursera. https://www.coursera.org/specializations/mathematics-machine-learning https://www.coursera.org/specializations/mathematics-machine... However, if you know the very basics of matrices (multiplication, transpose) and calculus (derivatives of basic functions, and partial derivates, and chain rule) I'd highly recommend first trying basic applied ML before diving deep into the math. It'll help you see where the math you're learning is actually used, as you learn them. Try deeplearning.ai first, then try this "math for ML" course.

ForHackernews 8y ago

"Learning from Data" is a real Caltech course on Machine Learning - http://www.work.caltech.edu/telecourse.html http://www.work.caltech.edu/telecourse.html It's sometimes offered concurrently with on campus sessions, and it is not a watered-down MOOC or a tutorial for some company's toolkit.