In October, I quit my job to live on savings and work on AI/ML. I am not interested in developing novel AI approaches; my objective is to learn the application of machine learning to solve narrow problems. In light of that, I believe the technology to have progressed to where one can learn how to use existing libraries to solve specific problems. Lots of businesses have specific problems, and will pay me to solve the…
I've been doing Coursera and got done with Udacity but both are just too philosophical. (I'll probably cruse the deep learning class too.)
Does FAST.AI get into the grit of actually doing or is it more philosophical?
I'd say to "seriously" start anything you can take a first step by building something, whatever it is. Even if you just follow a tutorial it gets you wet enough to keep going I think.
"Be fearful when others are greedy, and greedy when others are fearful" - Warren Buffett I don't want to try to dissuade you in particular, but I think more young people should apply this principle to the question of what field to enter. I've seen dozens of "How do I get into AI/ML?" posts in the last couple of years.
I don't quite understand, can you elaborate what you mean by that?
Just recently I have written a "Machine Learning for Web Developers in JavaScript" blog post [0]. If you or someone else is a web developer, it might be interesting. It outlines my approach of learning it and gives a couple of great resources for JavaScript enthusiasts. Otherwise, I will just post a couple of the materials I used for myself to learn about ML below.
I'm very adamant that if you really want to be an AI researcher, it starts with the mathematics. Multi-variable calculus, linear algebra,discrete math, probability and statistics are key. The classic books and courses others have suggested are excellent starting points. I'll also say something here that isn't established, so I may take some heat. I believe there are two main paths in AI that will eventually converge…
One day 40 years after I graduated I "got" linear algebra.
The eigenvectors of the inverse of the covariance matrix...
datasciencemasters.org was reccumended by someone working inside the Tensorflow team at Google. It is a sophisticated collection of courses and books (Stanford curriculum...etc).
You would find that as a superset of what you need, you can just scroll till you find what you need to learn. (They are kinda sorted from basics first)
In October, I quit my job to live on savings and work on AI/ML. I am not interested in developing novel AI approaches; my objective is to learn the application of machine learning to solve narrow problems. In light of that, I believe the technology to have progressed to where one can learn how to use existing libraries to solve specific problems. Lots of businesses have specific problems, and will pay me to solve the…
I've been doing Coursera and got done with Udacity but both are just too philosophical. (I'll probably cruse the deep learning class too.) Does FAST.AI get into the grit of actually doing or is it more philosophical?
Fast.AI is 100% focused on getting into DOING stuff. Its guiding philosophy is that teaching the math first instead of teaching you how to actually do some machine learning is backwards. The lectures start with a dozen lines of code that learns how to discriminate between cats and dogs and then follows up on that by slowly removing layers of abstraction. It's very cool.
That said, the lectures are pretty rough around the edges compared to something like a coursera class. And his goal is to get you to play around with stuff on your own using his stuff as a jumping off point. It works well for some people but not as much for others.
I'm probably the worst example of how to get into this field of work, but since I do actually work on developing and applying ML algorithms every day, I think my case might be relevant. Firstly, my background is not in mathematics or computer science what-so-ever; I'm a classically trained botanist who started came at the issue of programming, computer science, and ML from a perspective of "I've got questions I want…
This reads as an amazing journey. Kudos for your pursuit of a better process. It seems to me that so many (online) courses jump to applying tf/pytorch to a predefined dataset, whereas most of the work is in preparing the data. I have a personal project I'd like to try out classifying images, and haven't had much luck finding resources on building my own training dataset. Can you recommend any resources on assembling…
It should be noted that I deal primarily with geo-spatial image analysis, so there is a not insignificant amount of bias with regards to what data I'm interested in. I like using the USDA NAIP API for imagery, since I can call in imagery using GDAL directly into python or R. I rely heavily on freely available public utility data sets (Parcel level utility data). Beyond that and other than as a starting point, you're training data is always going to be something you've invested in heavily. Good training data is 100% the game. No modeling exercise is going to go well on poor quality training data. Currently as a personal project, I'm trying to develop a platform for developing and training data for geospatial modeling. If you're interested, hit me up on a PM and I can explain it in more detail (after work).