Facebook Field Guide to Machine Learning – video series
research.fb.com
research.fb.com
That's not a bad thing; the more guides from reputable sources, the better. Just don't read them and say you're an ML expert afterwards.
Too late. I get like 2 random LinkedIn invites a day from randos claiming to be simultaneously "AI" and Blockchain experts. You look at their profile and they have some codecamp course in React. I dont get it, do these people get jobs this way?
yes.
https://www.joelonsoftware.com/2006/09/06/finding-great-deve...
Yet most jobs listing these days require a link to your profile, so it's clear there's some value in having a "complete" profile.
Ultimately LinkedIn does have a good business use, but it can also be gamed pretty hard.
EDIT: Found the link. It didn't originate on HN, but I saw it from a post about it on HN.
https://theoutline.com/post/5495/how-to-beat-linked-in-the-g...
I'm like dude, when did you ever worked on "designing collimation towers" for NASA? You are like 19. Unless you were like a child prodigy, which I doubt, since you are asking me for an endorsement.
If it didn't work they wouldn't spam LinkedIn.
It may be just a coincidence but I have started noticing that very often when a company has a post that criticizes it for some behavior trending another post immediately follows showing the "generosity" of the same company showcasing some OSS or a blog post about some popular technological subject.
It got me thinking that maybe those companies have bots ready to upvote nice things about them when some criticism surfaces.
I've seen this happen with both Google and Microsoft so far.
The MOOC courses are "just" there to teach you the basics and background. Projects are absolutely necessary for you, because without a degree you will only be able to convince prospective employers based on having a lot of practical experience.
I get freelancers to do data preprocessing for me frequently, and sometimes put it through some off the shelf model.
Generally it's hard to find the right people for this, but that isn't exactly unique.
"ML expert" is the new "web developer"
(though I do understand what you're getting at)
If not, I really think that these topics should be addressed (even if only briefly) in any "field guide" to machine learning. Especially FB should give those some more attention after their numerous scandals and "mishaps".
Now, before you downvote this or say "but this is just about the methods and tools" please take a moment to think about how much power ML models can have over people when deployed at FB scale.
I would like to see at least a (brief) discussion of the following topics in such guides:
- Data Provenance & Data Ethics (Can I use this? Should I?)
- Data Protection & Security (How can I protect personal information when doing ML?)
- Model Fairness (How can I ensure my model is fair and does not discriminate?)
- Model Transparency (How can I explain the results of the model to users and colleagues?)
* Create a video Series about ML and a blog post about it
Thus make people believe:
* You are a leader in ML
* You care about democratising ML or AI whatever
* You care about sharing knowledge
* You care about Open Source
Not everyone that works for a company that a given person dislikes is "evil" or acting malevolently. No company is homogeneous.
I am curious how you came to that conclusion. I would think that Google is a leader in ML - unless I am missing something.
- Every year, researchers from the Facebook Artificial Intelligence Research Lab (FAIR) present their research at top conferences such as NIPS [2]
- FAIR also produces some of the most capable and widely used open source software in machine learning, such as Torch and Caffe2 [3]
If you'd like to see how Facebook uses machine learning, take a look through its blog posts related to recruiting [4].
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1. https://research.fb.com/people/
2. https://research.fb.com/facebook-research-at-nips-2017/
3. https://research.fb.com/downloads/
4. https://www.facebook.com/careers/life/machine-learning-at-fa...
Things like FastText are completely standalone which makes them really easy to integrate into other things.
not /the/ (only) leader in ML.
Google, FB and several of the chinese tech conglomerates are leaders within ML.
Google has more than studies and blog post about machine learning, apart from research and opensource ML applications, they are heavily dependent on machine learning in almost all their major product that I know of. For the most part they extremely good at it.
What and how does FB use machine learning in their product?
PyTorch by itself would be enough to make FAIR a leader. PyTorch is being used in many research papers and was/is a leader in "eager execution" mode for neural nets. But there are many other things, such as fastText (see more projects here https://github.com/facebookresearch).
On the user side of things, they use it to suggest friends, detect/recognize faces in pictures, etc.
Corporations aren't people. They don't have minds (you can say that it's an AI made out of people instead of transistors), and using concepts like "care", initially created for describing human's mind, is a leaky abstraction.
What I care about is what corporations do. And when they actually release valuable resources for free, we can just acknowledge it instead of trying to guess what's going on in the mind of an entity that doesn't have a mind.
Ads pay the bills, but only to the extent that you have something people want, to put the ads next to, and most of the engineers are there trying to make something people want.
https://www.businessinsider.com/cars-in-facebook-parking-lot...
I never owned a car
All these people got their lives improved thanks to engineers that enabled worked on selling ads. There's a popular stereotype that "ads" is some evil capitalist thing designed to manipulate you to buy some shit you don't need. Looking at the world around me, I see something different.
So far, based on the myriad of literature I have read about Machine Learning stuff, a lot of results and their quality depends on the type of network, training, error correction methods, etc.
Say I learn how to make computers build and train models, but is that enough to get good results? Are there any resources that will guide me into choosing a good topology or Network parameters (say like number of hidden layers, etc)
How do developers who use Machine Learning in production environments confidently come to a particular network topology/parameter set for a given class of problem?