151–160 of 211 posts
Just add smort.io/ before any arXiv URL to read it in Smort.
Follow the HuggingFace Colab notebooks. They are well-written and language-related AIs are a great way to get started because you'll naturally have a feeling for what it should produce. Afterwards, do a statistics class. Most algorithms these days are based on softmax, meaning the cross-entropy between two discrete/continuous probability distributions. There's a lot of choice in which distribution to use to model wha…
If you just want to work as an ML Engineer then take as many courses as you can on the subject before you graduate and get internships/apply to jobs. Nothing special here.
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I hope you're hyperbolic, the worst 3rd world countries have no governance (unless you count local warlords), 5 year olds working in dangerous and toxic conditions, hunger and slavery.
You don't realize what is going on in the United States. We have portions of the USA where the police don't even bother, and are run by local gangs. We also have children working, in dangerous and toxic conditions. We also have hunger, and yes we have slavery: prison labor. The USA is not what you think it is.
Can you elaborate on that? I've never heard that parcitular thing about the US. For reference, In Kongo, there are 5 year olds today carrying heavy buckets in makeshift cobalt mines, a'la XIX century England or France (plus the toxicity of cobalt, people who work in these mines get cancer if they don't die in an accident first). Even with whole families working in such conditions, the pay is not enough and not stable enough to sustain the family, and they are often working while hungry. Is there anything comparable going on in the US?
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It highly depends. I was hired for a small research group that didn't have a product in production. Got hired for programming, was on the table discussing and contributing to research within a couple months without any background in ML.
These types of anecdotes make the actual practice of both ML and AI seem rather, well, less than scientific. There is supposed to be Ph.D. level math behind all of this, yet an amateur with admittedly no ML background is part of the team. In Star Wars , it takes Luke Skywalker years to learn to use a light saber skillfully. Then in The Force Awakens , some ex-Stormtrooper with no training picks up the light saber and…
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Someone who can engineer infrastructure, pipelines and fire fight production issues is hard to find, but that’s not the point I was making. My apologies; It is real work; the point I was making is it’s not ML work , any more than writing a yaml file is ML work. If you want to write yaml files, any number of possibilities exist. If you want to work with machine learning, then don’t become a data engineer. The skills a…
Great comments. I agree with your take on what being an ML Eng actually means. Of course this will vary to a degree from team to team and company to company, but I think you still capture it well. I absolutely think MLEng is important and much needed, but too often under appreciated. Being this half breed part engineer part ML leaves you on a lonely island often in many orgs. The ML managers don't really understand w…
Stuff to do could include: - Getting a network architecture to run. - Applying optimization depending on target arch (pruning, quantisation, custom cuda kernels, etc). - Integrating models (rule of thumb: a product is 95% ordinary code, 5% is ML related). - Constructing benchmarks, monitoring
https://www.deeplearningbook.org/ and http://incompleteideas.net/book/the-book-2nd.html are excellent resources for supervised and reinforcement learning, respectively, and some knowledge of statistics and probability go a long way. But I think by far the most important thing is to just start training models, even very small ones, and developing an intuition for what works and what the failure modes are. - Get really…
A good alternative to Goodfellow is "Dive into Deep Learning" (https://d2l.ai), which is free and more up-to-date, interactive, and practical, IMO. Videos of a 2019 Berkeley course based on it are available too (https://courses.d2l.ai/berkeley-stat-157/).
I work in ML - I might make 3 buckets for ML careers right now: 1. ML/DL Researcher 2. Data scientist - 20/80 engineering vs modelling 3. ML Eng - 50/50 (or 70/30) engineering vs modelling People suggesting working in engineering to support ML are right that there's a lot of demand, but it's not what you're asking for. Becoming an ML/DL researcher working on novel techniques or new models will be hard without academi…
My nightmare is finding a role like that and realizing I’m just a dev ops guy.
Earlier quoted context omitted.
I hope you're hyperbolic, the worst 3rd world countries have no governance (unless you count local warlords), 5 year olds working in dangerous and toxic conditions, hunger and slavery.
You don't realize what is going on in the United States. We have portions of the USA where the police don't even bother, and are run by local gangs. We also have children working, in dangerous and toxic conditions. We also have hunger, and yes we have slavery: prison labor. The USA is not what you think it is.
The places where the police do “bother” are, ipso facto, also run by local gangs.