Comment by hal9000xp | Hacker News Reader

Comment by hal9000xp | Hacker News Reader

I'm in the same boat. For long time, I was interested in AI but at the same time intimidated by math. I'm relatively comfortable with discrete mathematics and classical algorithms and at the same time calculus and linear algebra is completely foreign to me. Also, I do not accept way to learn ML without good understanding of core principles behind it. So math is a must.

A few months ago, I stumbled upon very amazing YouTube Channel 3Blue1Brown which explains math in very accessible way and at the same time I got feeling that I finally started understanding core ideas behind linear algebra and calculus.

Just recently he published 4 videos about deep neural networks:

https://www.youtube.com/watch?v=aircAruvnKk

https://www.youtube.com/watch?v=IHZwWFHWa-w

https://www.youtube.com/watch?v=Ilg3gGewQ5U

https://www.youtube.com/watch?v=tIeHLnjs5U8

So my fear of ML was gone away and I'm very excited to explore whole new world for neural networks and other things like support vector machines etc

Worth noting that 3Blue1Brown also did a series on linear algebra which is eye-opening to say the least. Playlist at:

https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2x...

Even if you think you grok matrices, have a go at the first few videos of that playlist, if just for the visualization. It really helped me see what matrices (and operations on matrices) represent!

3Blue1Brown is a treasure. The production value is excellent, and he's great at taking seemingly uninteresting ideas and painting a beautiful picture to connect them in twenty minutes. I used to go through a video before falling asleep each night.

I just watched the first video. Thank for sharing.

I have also used Mathematical monk, who was simple and good in introducing basic concepts and tools related to ML. https://www.youtube.com/user/mathematicalmonk

I also recommend taking up computer graphics for honing your skills in linear algebra. Graphics are essentially applied linear algebra.

I came here to write a similar comment. Really make sure to watch the playlists in the correct order on the above YouTube channel.

Having watched the third one out of sequence, seeing the first two and then watching the third again helped me get a good understanding of the fundamentals. 3blue1brown as a narrator does a excellent job of allowing a rather tricky subject be more approachable, and inspired me to buy a course to allow a deeper dive into the math behind ML+NNs.

Nice to some one pointing the fundamentals. A good understanding about probabilist models is good too. After getting into too the basic math knowledge, I suggest this: https://classroom.udacity.com/courses/ud730

Regarding linear algebra, I highly recommend Klein's Coding the Matrix which uses Python to teach linear algebra.

I believe it was developed for Brown's linear algebra course for CS undergrads.

Hi! That's wonderful. What' a support vector machine used for?

Classification and regression. Given examples, predict labels or values for new data. SVM used to be more «hot» than neural nets and are still very useful.

Wow thanks for this resource!

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