There are too many resources from which to choose. It would be thoughtful of anyone to share AI learning pathways, like a syllabus, using those resources.
Ask HN: What free resources did you use to learn how to program ML/AI?
51–53 of 53 posts
Re: Ask HN: What free resources did you use to learn how to program ML/AI?
#521. Udacity: Machine Learning
2. Deep Learning Summer School Montreal 2016 https://sites.google.com/site/deeplearningsummerschool2016/h...
2. selfdrivingcars.mit.edu + youtube playlist "MIT 6.S094: Deep Learning for Self-Driving Cars" (https://youtu.be/1L0TKZQcUtA?list=PLrAXtmErZgOeiKm4sgNOknGvN...)
3. Coursera: Machine Learning with Andrew Ng
4. Standford Cs231n (https://www.youtube.com/watch?v=g-PvXUjD6qg&list=PLlJy-eBtNF...)
5. Deep Learning School 2016 (https://www.youtube.com/playlist?list=PLrAXtmErZgOfMuxkACrYn...)
6. Udacity: Deep Learning (https://www.udacity.com/course/deep-learning--ud730)
I created a blog (http://ai.bskog.com) to have as a notepad and study backlog. There I keep track of what free courses I am currently taking and which one I will take next.
p.s.
Although video courses are good. Everyday life makes it sometimes difficult to listen to videos on youtube while for instance doing chores around the house or working out, because you often need to a. see the slides/code examples, and b. put it into practice right away... therefore, podcasts are good to give you a flow of information.
Linear Digression, Data skeptic and (thanks to this thread i now discovered Machine Learning Guide)
Don't be discouraged if there is stuff you do not understand or feel like: i can never remember these terms or that algorithm. Just be immersed in the information and stuff will fall into place. And later when you hear about that thing again it will make more sense. I tend to use a breadth first approach to learning, where i get exposed to everything before digging into details thus getting an overview of what i need to learn and where to start.
Re: Ask HN: What free resources did you use to learn how to program ML/AI?
#53Earlier quoted context omitted.
Linear regression is incredibly important, but I think it's much better understood either practically (by implementing it or using it), or if you want to understand it mathematically, at the "end" of a statistics course. There's a reason that when learning probability/statistics, you usually encounter Linear Regression near the end of an introductory course, not in the beginning. Again, this really depends on how mat…
You can certainly complicate the hell out of linear regression, but Andrew Ng introduces it in the setting of optimization/stochastic gradient descent, which I think is both mind blowing and a much simpler introduction than most statistics courses. It's the very first bit of the course, I think everyone who is interested should try learning it. If not it's fine, but I wouldn't want anyone to not even try to spend a f…
That's certainly reasonable. And I totally agree with "try it out and gauge for yourself whether it's valuable for you".
My worry is that people will be put off from the field of machine learning if, 3 lessons into Andrew Ng's course, they will see that they don't understand anything, and that it's not practical to boot.
So my advice (generally applicable) is to try a few different things, because different resources click for different people.