4 ms·
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 m
by RosanaAnaDana 9y ago
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 to ask and techniques I want to apply that I'm currently under prepared to answer."
Working as a technician for the USDA, I learned programming (R and python) primarily because I needed a better way to deal with large data sets than excel (which prior to 5 years ago was all I used). At some point I put my foot down and decided I would go no further until I learned to manage the data I was collecting programmatically. The data I was collecting were UAV imagery, field and spectral reference data, specifically regarding the distribution of invasive plant species in cropping systems. The central thrust of the project was to automatically detect and delineate weed-species in cropping systems from low altitude UAV collects. This eventually folded into doing a masters degree continuing to develop this project. That folded into additional projects applying ML methods to feature discrimination in a wide range of data types. Currently I work for a geo-spatial company, doing vegetative classification in a wide range of environments with some incredibly interesting data (sometimes).
I think you've got the issue a bit cart-horse backwards. In a sense I see you as having a solution, but no problem to apply it too. The methods are ALL there, and there are plenty of other posts in this thread addressing where to learn the principals of ML. What this doesn't offer you, is a why of why you should care about a thing? My recommendation would be to find something of personal interest to you in which ML may play a role.
With out a good reason to apply the techniques that everyone else here is outlining, I think it would be too challenging to keep the level of interest and energy required to realize how to apply these concepts. Watching lectures, reading articles, doing coursework is all very important, but it shouldn't be thought of as a replacement for having personally meaningful work to do. Meaningful work will do more to drive your interests than anything.
- barkteryx 9y agoThis 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 and collating your own dataset?
- RosanaAnaDana 9y agoIt 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).
- deboboy 9y agoPM OTW...
- RosanaAnaDana 9y agoNot sure if there are PM's in HN? PatientPolly@forward.cat otherwise.
- maxxxxx 9y agoI'll second that. Without solving a real problem it's hard to learn something. You can learn the basics but then it's about applying them to a real problem and find out what works and what doesn't. It's the same with a lot of CS graduates. They know a lot of fancy algorithms but they have no idea when to use them and when not.
- DrNuke 9y ago>I think you've got the issue a bit cart-horse backwards. In a sense I see you as having a solution, but no problem to apply it too. The methods are ALL there, and there are plenty of other posts in this thread addressing where to learn the principals of ML. What this doesn't offer you, is a why of why you should care about a thing? My recommendation would be to find something of personal interest to you in which ML may play a role. This is gold advice and the only legit way to stick to something for the long term. OP actually needs a niche, an industry, a cause to care... then instruments will come naturally. The other way round is flooded already.
- vaibkv 9y agoThis is absolutely, hands-down best advice. I would definitely second this. I have done ML and Data Science projects on and off at my workplace and I definitely think I know much more than folks who are simply doing lectures, courses, etc. I think working on a problem is actually the only good way to learn just about anything when it comes to Computer Science and Data Science, in general. I would suggest the OP to pick a topic of interest, define a problem statement and work on getting results there. OP needs projects to work on.