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Ask HN: How to Seriously Start with Machine Learning and AI

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Re: Ask HN: How to Seriously Start with Machine Learning and AI

#41

Post by emurillo510 »

neel8986 wrote: post #11

This is kind of a masters degree course i created for myself to get knowledge of Machine Learning from bottoms up First, you need a strong mathematical base. Otherwise, you can copy paste an algorithm or use an API but you will not get any idea of what is happening inside Following concepts are very essential 1) Linear Algebra (MIT https://ocw.mit.edu/courses/mathematics/18-06-linear-algebra... ) 2) Probability (Harv…

hey neel8986, I know linear algebra is very important for large scale calculations. But how much calculus and statistics do you need for ML? Also, if you can touch what applications of calculus and statistics are used in ML that would be awesome :]. THANKS!

Re: Ask HN: How to Seriously Start with Machine Learning and AI

#42

Post by pongolyn »

I started with Andrew Ng's ML courses, which take a bottom-up approach beginning with the math. After finishing the deeplearning.ai track, I started rounding out my skillset with some data science and R programming classes, so I could be more comfortable working with unfamiliar data.

The fast.ai courses are a more top-down approach to ML, and there are plenty of good reasons for taking this approach. You'll start getting practice with libraries like Tensorflow right away. However, if you have a fairly strong math background and linear algebra doesn't give you nightmares, I highly recommend the Andrew Ng courses. A deep (ha!) understanding what's going on "under the hood" in ML will help your debugging, inform your strategy, and make your code better in the long run.

Re: Ask HN: How to Seriously Start with Machine Learning and AI

#43

Post by bllguo »

I'm starting the fast.ai courses as a recent stat grad looking to expand my knowledge. Heard many good testimonials. Besides that, I think Andrew Ng's Coursera offerings - intro to ML and the newer NN specialization - are great first steps. Personally I have a hard time learning from videos, so I refer to my copy of Pattern Recognition and Machine Learning by Bishop.

If you want a linear algebra text, I enjoy Strang's Introduction to Linear Algebra

Re: Ask HN: How to Seriously Start with Machine Learning and AI

#45

Post by ultrasounder »

nrjames wrote: post #6

IMO, to start with AI, it's best to start as simple as possible so that you understand the algorithms that drive things that are accessible to tweak. If you are a gamer, think about the various AI routines that control pathfinding, resource collection, and strategy for computer players in strategy games. Some of the concepts of BFS and DFS build into Minimax strategies and other types of "simple" approaches to search…

By far the best response. After this,one could always try the deeplearning.ai or fast.ai courses depending on whether they prefer top-down or bottoms-up approach. Or why not do them together?

Re: Ask HN: How to Seriously Start with Machine Learning and AI

#46

Post by darepublic »

d_burfoot wrote: post #39

"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.

If that were true young people shouldn't get into tech at all let alone AI since it's overhyped at the moment.

Re: Ask HN: How to Seriously Start with Machine Learning and AI

#47

Post by cr0sh »

gm-conspiracy wrote: post #33

Do you recommend using rented GPU instances, or building a some kind of Nvidia rig?

I'd personally say it depends on your budget, what you want to do, and the amount of data you plan to process. It may also depend on how comfortable you are setting up your own rig, if you go that route.

For instance, last year I completed Udacity's "Self-Driving Car Engineer Nanodegree" course. Large parts of the course needed us to train neural nets (tensorflow and similar) on data we either generated or downloaded. We were provided the option to use AWS instances for training (free credits), but I opted to use my local box.

It took me about a day or so of playing around before I finally got everything working properly (CUDA, etc) with my NVidia 750ti GPU. This is a very low-end GPU, but it honestly performed quite well for the course. It could only handle a limited amount of data, and sometimes training cycles took a while for turnaround (depending on the task), but it ran the resulting models quite easily (while still handling the 3D rendering tasks of the vehicle simulator).

For learning purposes, it will all depend - if you already have a machine with a decent GPU, CPU and RAM (say something equivalent to the 750 or better, 4 cores or more, and say 8 gig of RAM), it might make sense to try to do things locally - if you think your skills are up to the configuration challenges (I got my system working, but I ended up breaking Ubuntu's update system, because I'm on 14.04 LTS, and I had to hand-install many things to fix dependencies and such for Python, Tensorflow, C/C++, etc - in order to complete the course).

However, if you are planning on processing a huge amount of data for a large model, but aren't planning on doing this constantly - then an AWS instance might be a better option, as a custom rig for this kind of thing I'd imagine would be a bear to spec out and configure - not to mention cost.

I'm certainly not an expert on all of this, though...ymmv.

Re: Ask HN: How to Seriously Start with Machine Learning and AI

#48

Post by barkteryx »

RosanaAnaDana wrote: post #35

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 and collating your own dataset?

Re: Ask HN: How to Seriously Start with Machine Learning and AI

#49

Post by maxxxxx »

RosanaAnaDana wrote: post #35

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…

I'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.

Re: Ask HN: How to Seriously Start with Machine Learning and AI

#50

Post by DrNuke »

RosanaAnaDana wrote: post #35

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…

>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.

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