YouTube playlist:
https://www.youtube.com/playlist?list=PLUl4u3cNGP63oMNUHXqIUcrkS2PivhN3k
Book link:
https://amzn.to/2WecEkk
I used 18.06 to learn the linear algebra necessary to prepare for further study in machine learning. It was sufficient for that purpose, (I got a B and an A in CMU's 10-601 and 10-605 ML courses, respectively) but this 18.065 course is more specifically geared for that purpose. The companion book is quite good, as well.
OCW link: https://ocw.mit.edu/courses/mathematics/18-065-matrix-method... YouTube playlist:
https://www.youtube.com/playlist?list=PLUl4u3cNGP63oMNUHXqIU...
Book link:
I've been reading (along with my kids) Ben Orlin's Math with Bad Drawings book which is great at motivating and presenting math in interesting ways that are all about presenting things as an interesting puzzle to be solved vs. the way much of math is presented in a rote "this is the algorithm / solution" way.
https://books.google.com/books/about/Math_with_Bad_Drawings....
Alan (co-author of Julia) mentions that only Swift and Julia make the cut for ML, according to Google.
I do like Swift and I’m willing to learn Julia, but most of the ML I see uses Python. Is there any traction for Julia or Swift, or is it mostly aspirational at the moment?
Google has thousands of developers writing Python for ML every day. TF is in Python, and remains in Python.
Chris Lattner (the creator of Swift at Apple) joined the Tensorflow team and is working on futuristic approaches to compiling ML programs. He (personally) evaluated a bunch of languages for next-gen and decided Swift is the right one. So he has a small team of folks working on that project exploring how Swift can be used. That's very far from any official decision of Google on anything. Google has people writing ML in Java, C++, Python, Haskell, Lua and Javascript as well.
Here’s the evaluation we are discussing:
https://github.com/tensorflow/swift/blob/master/docs/WhySwif...
HN comments:
It is very early days for Swift for TensorFlow. We definitely don’t recommend anyone tries to switch all their deep learning projects over to Swift just yet! Right now, most things don’t work. Most plans haven’t even been started. For many, this is a good reason to skip the project entirely."
I don't use either and to be honest was surprised that their original set of languages evaluated was C++, Rust, Julia and Swift. Seems like an odd list of languages to start with when discussing ML.
I found these slides that look like Alan’s from a different talk. The first slide was his point:
https://arpa-e.energy.gov/sites/default/files/2a%20-%20Edelm...
Because Google is fairly well known for being a “decide as late as possible, often after multiple competing options from different teams are in place and being used in real signifcant applications” organization.
... written by Chris Lattner when he joined Google Brain and found Python lacking as a language for doing ML work. He evaluated a bunch of alternatives and decided that Swift would be a good choice, so he's now working with a team to explore that.
As far as Google at large is concerned, there was no cut of anything. Google keeps producing new ML code in Python and new ML frameworks in Python on a daily basis
In my opinion, he takes great care to motivate each topic and express his train of thought when working through problems.
