Don't learn machine learning

Key Takeaways

don't learn machine learning pretty provocative title right you know I written article the other day eske sent it to me from Twitter so Barry thank you very much SK and the the article was titled with the same name don't learn machine learning learn machine learning to build software products or something along those lines that was the subtitle the title was don't learn machine learning now an important point to note is that at the top of the article which which is beautiful I think this should almost be done with a lot of things and I'm gonna do it too so that's what I want to bring it to your attention is that the author notes that he builds products too that allow you to use ml in software products it's the the don't ask the barber if you need a haircut problem which is what I want to sort of get you to think about and start this video is because the don't ask the barber if you need a haircut problem is you have to be aware that all advice is going to be a little bit even if it means well and I'm sure all of it always means well it's gonna be a little bit tainted by the person delivering it and so that's you should know about me is my disclaimer is that I'm someone who runs a business from a bedroom I make videos I write articles online I use machine learning my goal is to use machine learnings in machine learning in in future products so that that's the that's the caveat that's my background from there now that you know that let's dive into the article I'm not gonna read it verbatim I'll leave a link in the description so you can check it out for yourself but there are a few points I'd like to just riff on because that'd be fun it's a good article let's just put it that way let's put that first and foremost because I like reading these things there's a little bit of confirmation bias from my end but the the entire article could be products or research so that's that's the question you need to ask yourself now I get a lot of emails from people with a donkey problem or from donkeys they have donkey syndrome and now I don't say this to be mean or malice I say this with empathy because I I have been in their position before and I I also find myself in that position even though I know about the problem then you might be wondering what is the donkey problem let me tell you the kind of emails that I get I go some people go should I learn tensorflow or should I go apply torch and the other questions are should I learn R or Python and other questions are hey I want to get into machine learning but I learn all of the math and old statistics and and everything else and all the theory before I start to code and of course it comes down to - each individual's choice and necessarily neither is better than the other in terms of should you learn Python or are attentive apply towards whatever learn whatever you need to allow you to get to the next stage of where you want to go and this is the donkey problem have you ever heard the story of the donkey who was looking to the left at a bucket of water and looking to the right at a pile of food and then looked back to the left at the pork bucket of water to the pile of food and you know what happened to the donkey the donkey died of thirst and starvation because all it did was just look am i thirsty am i hungry am i thirsty so that's in a way does this all relate back to the article well the point in the article is products or research and it goes to see do you want to build products using machine learning such as an application that uses machine learning maybe something the example one of the examples in in the article is a real-time license plate reader so this car is driving and it's using machine learning computer vision probably a little bit of text processing I'm not sure to read the the numbers on license plate so who was that helpful for that could be helpful for for police so do you want to build things like that where you use machine learning to solve some sort of problem or do you want to go to a university and research different ways to advance the underlying algorithms that that are allowing that computer vision to to take place because although they are one in the same like one enables the and this is a big big big point right there is no real right answer it just comes back to what do you want to do in terms of the the computer vision to identify numbers on a license plate couldn't have occurred if it wasn't for the people researching to find ways to do that and vice versa when someone needs a product to be created that uses some kind of machine learning it requires if that that product that need comes about it requires someone to research and find ways to do it so you see here is this is where it's not it's you might be thinking that it's a trade off but really one implies the other they both require each other to exist let me give you an example of where I've been stuck before bouncing back and forth I've wondered I've considered should I go back to the University and learn learn all the math behind machine learning should I dive deep into it or should I use machine learning to build some sort of product I mean you don't understand or probably maybe you do maybe you can relate how much effort I wasted being a donkey just back and forth back and forth terrible terrible waste of time should I just chose one and then adjust it as needed when you break down a machine learning problem or project I'm working on one right now so this is like 60% of it is data collecting and and making sure that the creating data making sure you have some sort of data set so a majority of it is is that like having some sort of data set ready 30% is writing code to sort of prepare and manipulate that data set and then 10 percent maybe even less than 10% is building a machine learning model like I'm working on a 42-day project right now to replicate one of Airbnb s machine learning news cases and most of it has been preparing the data getting it ready to use machine learning I'm using detector on 2 which is a computer vision framework to do all of the backend machine learning behind it because why I'm going from the aspect that if I had if I was air B&B and I had some sort of problem to solve such as when someone takes a photo of their and uploads it to Airbnb how can I detect the pieces of furniture aka amenities in that image and automatically list those amenities on there that Airbnb room listing so that was ed B&B his problem they thought okay yes we have image has been uploaded I'd like to we'd like to get some information out of those images so the machine learning was a tool to solve that problem but if we come back to breaking the project down that problem would have never arisen if they didn't have the image has been uploaded to their platform prior and then of course you can't just upload an image and have a machine learning algorithm just do its thing straight away you need to build write some code to have the image uploaded you need to write some code to have it stored in a certain way then you need to write the infrastructure to have the images loaded into a machine learning pipeline then you have to have some code that well that's where your machine learning is right it's just that little little model that you can call model dot fit in detect on Tuesday so you can just call trainer train once you've loaded the data that is then you need some code to to load whatever the model predicts back into the application that the customer sees so you as you can see there's a lot of different pieces of the puzzle going on here with with any sort of machine learning project it's not just the machine learning aspect of it and that brings us into the the second major point I wanted to bring up is there well that the article brings up as well and I really love this point as I said this article was a healthy dose of confirmation bias is to learn by building now if you're an engineer like me you probably have the engineers disease which is learning tools for tools sake and I've been suffering from this illness for for a long time well I haven't really even been an engineer for that long a couple of two-and-a-half years but I caught it really quickly you know why because learning new tools is fun but what happens is when you learn say for example a new machine learning framework comes out like I'm just a whole bunch of stuff just got released with tensorflow 2.0 and PI torch and I want to learn them all frankly there's tens flow js4 web this tensorflow for swift there's everything like all these new frameworks and I'm just just absolutely frothing at all of the new stuff coming out but do you think how how have you gone when you've tried to learn everything I can tell you I've gone terribly right so that's the engineers disease is learning tools for tools say so what happens is if you're learning tools for tools sake what you what you become is a hardware store you have all these tools on offer like anything you want whatever you need you need a you need a 25 inch shovel yeah we've got that you need a 15 pound sledge hammer yep you've got that we need you've got a 300 pound hammer drill I don't know where I'm pulling all these tools from all right my tool is a keyboard but you become this hardware store of just a collection of tools that are all waiting to be used waiting for their potential to be fulfilled rather than an artisans workshop in the backyard somewhere where in your workshop you've got a small amount of tools you've got what you need you've got a few things in this case it might be just one deep learning framework it might be one programming language it might be one database language so you just combine those three and you've got some sort of problem like if you're working in your little workshop in the backyard it might be to to fix the the broken chair leg that that that chair that you're your mother sits on for dinner and it's been broken you're like well I'm going to take it to the workshop and use my tools that I know to fix it rather than just circle around the hardware shop looking at everything just trying to go hmm I wonder if I could use that six burner barbecue to to fix my to fix my mum's chair no that's silly you'd never use a barbecue to fix a chair right so it's the same thing oh maybe you go to your workshop and you decide it you want to build a little boat to take your lady friend rowing down the river if you had too many options you choose any and you couldn't take your lady friend down down on a nice riverboat riff it's the same thing going and now I say this because I suffer from a to this disease I suffer from this illness - I just want to learn everything rather than building something with it so I mentioned the real-time license plate identifier but there's also a radiology this is this is what makes me disgusted in myself he's saying examples like this so use me as an example but use me as an example to work on your own projects is that's something I need to change so bigger you can use me as an example don't be like me don't be a donkey the radiologists there was an example on a blog post on a different one I'll leave that in the description as well will radiologists from the Philippines built a machine learning application to identify so radiologists looks at x-rays and he built a machine learning application that's a web based one so you can access it on your smartphone which is amazing that's where the world is going right because everyone more more people were getting smartphones they need to have access to to these things on there I made a device that they have they built this machine learning applications so you could identify norwood classify different types of of x-ray so if it's a broken arm you go all that that image you upload that x-ray and it goes all that image that kind of looks like a broken arm and that i love-love-love see there's nothing more than i love and seeing that and you know what you know how he built the prototype not with some advanced coding technique or the latest framework with he built it with tents no teachable Moschino about two seconds flow over teachable machine it might use tensorflow behind-the-scenes but requires no code to get started you upload your data and labels in a fashion that is is ready to be used it's it's some sort of problem his problem was I'm looking at all these x-rays and my colleagues are looking at the same ones all the time and I'm identifying the similar x-ray a lot of the time so if I could train up if I could use machine learning to help train out one of my colleagues or a new colleague and go hey well we've seen let's put our knowledge of what we know about x-rays into this application let's loop back the whole this there's everything in this started from products or research now it's this is the and avoiding the donkey problem what I mean by that is being stuck between an abundance of options and choosing none so essentially just looking left looking right I'm going huh the better way to do things is not necessarily products or research its to follow your own curiosity figure out okay is there a need of some customer or is there some problem that I can go and figure out rather than that the engineers disease of trying to learn every single tool and having a bunch of solutions that are just looking for a problem look for a problem first for example my current inspiration is I'm reading a book called food Fink's you're gonna hear me Yap on about I've yapped on about food and health in past videos but I'm reading a book called food fix and I'm starting to realize okay there's there's a lot of issues going on in the world of how we get food with all the technology innovations that we're doing it's like people seem to forgot that um we need food to exist so I'm starting to think I'm using again it's not there yet but the inspiration is there coming from the book is like ah could I try something and what holds me back is going oh I don't want to dedicate this effort if I know it's not gonna work but that's another part of the donkey problem is looking at something and not trying it be out of fear that you could be wrong or that fear that your effort will be wasted I get a lot of emails as well asking if I do this will my efforts be worth it that's very can you answer that question just try to predict what's gonna happen what you're gonna do in a week will your efforts in the last week worth it very hard to predict those sort of things you must you must know 99% of effort is wasted so to wrap this all up the question I'm gonna I'm gonna leave you on is or the the takeaway from this is if you're learning machine learning figure out a what let me figure out how rather than in the reverse because there's a lot of house but they're all looking for a watt so one way to avoid the donkey problem is to go you know what I'm gonna try this this whatever this is it might not work but I'm gonna try it out anyway then the beautiful thing about it if it doesn't work out you know what doesn't work one way you can do this and what I'm doing now just as a little bit of skin in the game and as I said a reminder as I'm disgusted in myself I haven't built something that someone else can access like the like the doctor from the Philippines so use me as a as a anti role model in that case but one project I'm working on right now is replicating airbnb s amenity detection and I'm spending 42 days doing it the benefit of that is I spent 42 days doing it I've learned a lot a lot of practical a lot of hands-on I'm trying to try to replicate or improve what they they actually built in terms of a using machine learning for a product worst case scenario at the end of it it's only 42 days and if I wanted to keep going well I could go well I'm gonna do it for another 42 days so this is this will we're best at we're best at exploring things building toys and seeing if eventually those toys become useful so that's what I'll encourage you to do what toy can you build that might one day become useful the toy that's gonna ignite your curiosity how can you avoid it being a donkey in terms of learning machine learning learning anything how can you learn what you need to learn when you need to learn it and the final question is leave a comment below what currently don't you know about machine learning and how is that holding you back so leave a comment below again thank you SK for sending me the article it was it was a very good read as I said a lot of healthy confirmation bias so take anything that I've said in this video as a grain of salt if you do read the article leave your favorite point below and if you have anything less similar to - that sort of article if you do read if you do check it out and you wanted to send it my way you want to see one of these videos being made on it please let me know you can find any link in the description or you'll be able to contact me but as always keep learning keep creating and I'll see you next time