111–120 of 211 posts
I recently asked reddit how to label images by downloading a pre trained network that used image net, I got no answers. I don't know how long it would take to train such network with a cheap laptop. There are tutorials, but I don't see any cookie cutter thing. I thought there would be demos for this, since image labeling is an old problem.
My 2c (not exhaustive for what you want to do, probably): 1) Get some statistics/probability basics. It's full of people (you can see a lot of analyses on Kaggle) that "do machine learning" but make very silly mistakes (e.g. turn categorical data into a float and use it as a continuous variable when training a model). 2) take a look at traditional machine learning approaches. Nowadays you're swamped by DL (a lot of g…
This example: > It's full of people (you can see a lot of analyses on Kaggle) that "do machine learning" but make very silly mistakes (e.g. turn categorical data into a float and use it as a continuous variable when training a model).
Doesn't seem related to stats or probability at all to me. Just critical thinking skills
The vast majority of people who do this have graduate degrees. I'm biased, but I think getting a graduate degree in the subject would be the default suggestion. Are you considering it?
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Now this is interesting. I've always found it fascinating that when profit is on the table, democracy is nowhere to be found. I've looked, not too hard TBH, for essays and literature discussing the correlations to business model management structures and government/nation political hierarchies - not education level (propaganda), but critical analysis. I've been an employee of several of the top corporations on our pl…
Unfortunately, the only way to prevent hierarchy is to create a limited hierarchy (this is the purpose of constitutions) a priori; hierarchically naive organizations fail on this account. External parties will demand hierarchy simply because they want to know your organization (or nation) isn't wasting their time--no one wants to deliver a sales pitch to people who can't authorize purchases. If they're not careful, a…
How do you define "US falls"?
>Eventually, unless national governments start dropping serious lead pipe on employers' heads, people are going to tire of paying 30+ percent of their incomes to a government that lets bosses get away with this shit.
People endured much worse in medieval times, and endure much worse right now in China.
My 2c (not exhaustive for what you want to do, probably): 1) Get some statistics/probability basics. It's full of people (you can see a lot of analyses on Kaggle) that "do machine learning" but make very silly mistakes (e.g. turn categorical data into a float and use it as a continuous variable when training a model). 2) take a look at traditional machine learning approaches. Nowadays you're swamped by DL (a lot of g…
While stats and probability are very good, I can't say you need more than a good 101 level course for either. Really you're just looking for some good reasoning skills about how distributions and probability works. This example: > It's full of people (you can see a lot of analyses on Kaggle) that "do machine learning" but make very silly mistakes (e.g. turn categorical data into a float and use it as a continuous var…
But if anyone wants to become an expert into ML/DS, learning statistics and probability is fundamental. Books like A First Course in Probability, Introduction to Statistical Learning and Elements of Statistical Learning, to name a few, are very important.
A lot of the mistakes done in practice are based on a lack of understanding in sampling techniques, how statistical metrics can be misleading and so on.
First thing I learned in statistics is the difference between quantitative and qualitative information. If someone knows this before hopping into Kaggle, they know that categorical features can't be used as continuous features.
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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…
Years and years ago in the movie universe, Luke had to painfully learn to use his lightsaber from a mentor who passed down techniques and philosophy to the student.
In the current day of the movies, lightsabers are understood to be powerful, yet temperamental and exotic, weapons. Mildly trained individuals can use them, even if it's to a limited extent (i.e. flick switch; shiny side is the business end; heat bad, ouch, no touch).
To belabor the metaphor, you've also had a tradition of people, from all walks of life, using vibroblades (basic to advanced standard statistical analysis and regression) in order to achieve some level of parity against users of lightsabers.
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 academic research experience. Few companies are big enough to support true research, and the ones that are have a very high bar even for people with PHDs
What I call "data scientists" apply math/ML to real problems. The people I see here have a quantitative background like physics/math/CS. Often they have more general quantitative skills that go beyond ML. People like this will might work on things like fraud where an eng pipeline exists and small improvements in the model are valuable.
There are more of these roles than "true research" and they exist at small companies because it's applied. You can get into this with demonstrated evidence in side projects + a convincing background, but professional education might be the most sure way.
Finally - there's a lot of demand for engineers who can do both modeling and the requisite engineering. A model is a small part of what goes into a production ML feature - you need a data pipeline, automated retraining/prediction, a place to deploy the model, monitoring on eng stats + data stats, and the usual application backend/frontend to do something with the results.
You might be able to get into this with some demonstrated experience in side projects assuming you're a SWE already, and depending on your standards for where you want to work.
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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…
What? A lot of the hard part isn't the model, and especially in a world where bert, xgboost, optuna, pytorch, etc have solved much of the classic problem and forced 'real' DS to specialize on either the business consulting side (not math/engineering) or theory side (barely implemented). The rebrand of 'data analyst' (SQL, powerbi, . ..) to 'data scientist' by even top tech companies underscores this. It's not yet to…
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> It’s IT support for the people doing real work. This is an appalling perspective. Good MLE skills seem a lot harder to find that good ML ones.
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…
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 what you do and neither do the engineering managers. It is kind of thankless unless your management really understands your role and appropriately advocates for you.
MLEng is often an engineer who wanted to get into the sexy ML space and since it is in the title it feels cool. Then you realize you're more an Ops engineer who deals with the inane code of many "true" DS/ML scientists. Thankless, indeed.