Why I Don't Like Machine Learning

[@ianprado1488]

People who are passionate about machine learning either have a domain specific problem they are trying to solve or care about inventing novel learning algorithms.

[@Tntpker]

Most underrated comment here.

[@ΓιώργοςΑλεξάκης-ι5ζ]

Lol wtf

[@abhinavadarshsood5759]

novel learning algorithms are fun tho

[@Mando0975]

For me, the interesting part of ML is not training/tweaking models on a dataset, it's learning about the algorithms that power the models. I agree that just spending hours tweaking a model's paramteres is not that fun, but building your own neural network, decision tree, support vector machine, etc from scratch and getting it to work on a variety of datasets can be a fun challenge and is way more engaging then just training a model on a dataset. True, it doesn't have as much real world application, but it definitely gives provides you with an interesting challenge as a developer.

[@darabat207]

It can have critical real-world applications if you are at a research level making better algorithms. Having meaningful impact at the world is a hard mission that tends to take time. You have to do the basics and then go deeper on a problem until you are doing something unique.

where can i learn how to build a neural network from scratch

[@thelivingalchemist]

@ neuralnetworksanddeeplearning.com/

[@Lucas-of6ou]

Well but in the great majority of works in this area you won't be able to build your own decision tree or whatever lol, you will in fact, tranining/tweaking models on dataset's for solve problems.The market share for researchers of this type is relatively small, and you may be left with just becoming an academic. I don't understand the students' hype about ML, I guarantee you that for those who don't know ML, it looks a lot more interesting than it really is xD (and this comes from a person that loves math and physics aswell)

[@RJDOUBLEU]

@@Lucas-of6ou I find your point of view hard to relate to as I am graduating from my undergrad this semester and DL has basically changed my entire cs career. I went to a state school for CS not really knowing what kind of software I wanted to engineer. After being taught about neural nets in my junior year, pretty much everything has been rapidly getting better for me. I became fairly obsessed with deep learning, I write code daily, I have been flooded with interviews, I've written two research papers along with PhDs from more prestigious universities and I love the research I do. I'm actually not great at math and have been working very hard to improve my understanding of advanced ML concepts like hessian matrix approximation and bayesian hyperparameters search but most importantly the research I do fascinates me. And all of my research is application driven: Vehicular LiFi, bacteria colony classification, space situational awareness. All of this research has completely different challenges but all eventually boil down to a neural network making the hardest parts possible. That's just amazing to me and I hope other young ML enthusiasts here are able to find similar experiences to mine. Also I'd like to note that ML encouraged me to start learning web development, big data programming, and cloud technology so I could get my models in "deployment" behind a great website. It doesnt have to be all boring theory I promise 😁

[@swyxTV]

as a webdev currently learning ML, all of this is true. i like that you own it.

[@jean4j_]

As a Data Scientist currently learning Web Dev, all of this is true. i like that you own it.

[@justrohit-uc7pn]

@@jean4j_ quick comeback 🤣

[@prod.kashkari3075]

Jean-Loïc De Jaeger I HATE WEB DEV LMFAO

[@prod.kashkari3075]

DC - TLC alright bro

[@prod.kashkari3075]

DC - TLC you got me!

[@rahuldeora5815]

This is a common problem: software dude gets in to ML thinking all he needs is to learn code and libraries and doesn't end up understanding what's going on cause it's all mathematics and statistics. Kaggle is not the best, but the worst way to start ML. The best way is cal+linear algebra and then a ML course which explains the math.

[@tyrantula767]

It’s crazy math, you need to know Taylor series expansion in order to understand simple logistic regression in some instances.

[@sadavar3746]

​@@tyrantula767 Taylor series expansion is highschool level math

[@tyrantula767]

Vroom bloody hell 🤦🏿‍♂️

[@billylardner]

If you’re from the UK, you need A level maths and further maths knowledge to understand linear/logistic regression, using matrices & vectors, and differentiation for gradient descent or the normal method of optimising the cost function.

[@tyrantula767]

Billy I’m learning data engineering now with Python and SQL; I want to pick up the math and transfer over to data science / machine learning.

[@gigik64]

I concur. I've been a data scientist for 6 months, hated it, went back into software development. Machine Learning (or inductive programming, more correctly) is only stimulating if A) you're purely working on the math behind the models or B) you're making an entire application with the models you're building or C) you're programming a model for which no implementation exists in a mainstream framework. This last case leads you to learn a lot about low level programming and parallel computing, and honestly I find it really fascinating.

[@anilshrivastava2579]

Our thoughts match a lot, I also tried ML a year ago with full dedication because of the hype but eventually realized I enjoy the process of building a project a lot more than tweaking models and analysing data, so I switched back to being a software developer and have been better at it since then.

[@kr5051]

Same here. I also fell for the hype created around ML, AI and DS. But now I have realized I love creating and building projects than working on models.

[@MagnusAnand]

The problem with this approach is that you don’t learn the math. ML is great if you like math.

[@Baconator1368]

there are plenty of other topics in computer science that are great as well if you like math. in fact, I would argue that machine learning probably isn't that great if you like pure math, but rather enjoy learning how to solve math problems. For me, the math involved with machine learning is just not my type of "flavor" of math that I prefer, I guess. I much prefer learning about more theoretical, discrete math, hence my interest in programming language theory and implementation. Super interesting stuff if you enjoy the cross between the theory and practice of computer science.

[@sungjuyea4627]

Sadly, it was not the case for me though. The math behind ML is mostly from the analysis and probability theories, isn't it? Surely I'm not a deeply trained mathematician so somewhat afraid of saying this aloud, but what I could say is that not every mathematicians would like ML, who would enjoy algebra and geometry, or even analysis. Too much notations but not fancy results which I could discover from the books like Hatcher or Conway.

[@sungjuyea4627]

@@Baconator1368 I agree

[@arvinderdhanoa6634]

@@sungjuyea4627 to me ML seemed mainly like calculus with a bit of statistics added in, but I've only looked as far as how a basic forward/backward propitiation algorithm works.

[@pauhull]

I like math but don't like ml

[@chinmoyjyotikathar8793]

As an ex-data scientist who has transitioned into backend software development, this resonates with me on multiple levels! Thanks for putting this out so clearly.

[@trungkienngo6267]

can u recommend a few sources to learn software development? like which courses, books, projects should i do to get into it

[@garrettlovetv]

I agree. When I was doing AI stuff I found the possibilities pretty interesting, but the actual practice of doing everything was kind of boring and also not really capable of doing exactly what I wanted to and to the degree that I wanted. I personally love building web apps, or even mobile apps, because I like interacting with people and building something that can solve their problems and improve aspects of their lives. Also it's more tangible and for me that always helps

[@WestCoastAce27]

The AI Hype Train is amusing. I really enjoy listening to or reading non-Techies pontificating. They act like the cure for all diseases is a few months away. Great video. Figured it was a boring field. PS - maybe too honest admitting to telling people you interned at BoA - in your age bracket that could get you spit on.

[@tear728]

I'm somewhat opposite lol. I like the mathy, scientific computation. I would say that I don't enjoy working on front-end stuff for other people's products. However for my own entrepreneurial pursuits, I like that too.

[@JoeARedHawk275]

Mark S I would agree with you currently, but you can’t just write AI off... especially since you don’t know what new innovations are going to happen in the future. Also the current methods aren’t even close to what a true AI system is supposed to be so I don’t see how you can write it off so soon. Everything takes time. It might take a decades, but at some point AI will solve many problems. But I don’t think anybody was thinking that it would be advanced enough to cure diseases in a few months anyways.

[@WestCoastAce27]

@@JoeARedHawk275 Please point out where I said I was 'writing off' AI - just saying it's overhyped. Specifically it is currently extremely limited and those using it - like KZbin here - aren't very good. Why does KZbin recommend videos I've already watched or from channels I already said 'don't suggest anymore'? I'm sure you and other programmers know why - because it only does what it's told to. Even ML - it's only as good as the humans correcting it. Did you know there are rooms full of people in Africa and Asia that check the ML to see if it was right or wrong? If they screw up the ML can think a cat is a dog.

[@CeruleanAnthracite]

​@@WestCoastAce27 I agree with you somewhat. It's overhyped, yes, but I believe it's primarily stuff like deep learning and "AI"/ reinforcement learning, and not everyone realizes it's largely FAANG/IBM/Nvidia doing some insane stuff while most other places can use simple solutions (not necessarily easy - you'd still need people who understand the stats) to solve real problems.

[@griof]

As a mathematician who has paid too many side courses in ML and after 4 years of professional experience in data analysis/statistics and machine learning I am looking forward a change in my career. All you said is true, but I'd extend your critics even more. ML is so hype that even serious researchers tend to include buzzwords in academic papers. Many algorithms are based in quite old mathematics, which is totally fine but many times this old tools are renamed in a very fancy way so they get more acceptance... Which is intellectually dishonest. The thing get worse in industry. Many times data hasn't enough quality to be analyse nor use in any ML model, but the manager who is extremely hiped just want the model to work so the company can say that they are a "data driven company". At the end you end up with an extremely silly model biased towards what the final user wants to hear. Not talking about the quality of the software develop by data scientists... Which extremely poor most of the time (including me, but at least I try to get better). Notebooks every where!

[@hengkur45]

Nice insight. Thanks!!

[@renatosardinhalopes6073]

So overrated that we also got incredible technologies like GPT and Midjourney which are making a real impact right now. It's not overrated at all! People just used it to things they shouldn't have used for!

[@jrwkc]

I love SKY Kit Learn.

[@JoseAyerdis]

I also like to program in Arrrrrrr

[@unltd_j9018]

I feel like I’d lose interest in ML too if I was using sky-kit and not sci-kit

[@sarthakhajirnis1908]

😂😂😂

[@mr_wormhole]

SKY rocketing your models with Sky-kit since 2010. lol

[@zelllers]

I love psychic learn

[@lakshyadhariwal248]

Machine learning is the glorified name of applied statistics

[@AndrewSmith-pn2qc]

And applying statistical models is just another word for profiling

[@siddhantdubey1848]

It's more math than computer science.

[@MaxwellMcKinnon]

Yes and no. For some problems, it’s more powerful to have higher abstraction mental models that describe the emergent phenomenon. The relationship between statistics and emergent machine learning is very much the relationship between physics and emergent chemistry. (And for perspective, statistics and physics are also emergent, some thinking is even basic charges like the charge of the electron are due to emergent phenomenon from simple rules at scale)

[@siddhantdubey1848]

@Demon King The stuff you explained is still math, and yes I'm well aware of heat transfer, finite element method and etc. Computer science to me is more about algorithms, data structures, compilers, computer architecture and stuff like that.

[@valentinfontanger4962]

Only math students can relate to this precious comment

[@koffiegast]

I done my BSc+MSc in AI (yes full complete AI programs in the Netherlands have existed for over 15 years now). I don't like the ML community as it currently is with everyone hyping it up, and using the standard buzz words for everything. Everyone tends to use DL for everything and new folks to the field are directly introduced into it after they seen linear regression. It is like all you do is give them a hammer (or rather a sledgehammer or a shotgun) and teach them everything is a nail. There is no true understanding, there is just applying and not even the good type. Every introducee to the field is hyped and every project they do results into the same: "I tried very advanced stuff for weeks, code is disorganized jupyter books and the output/deployment sucks". They don't learn that the first question in ML you should ask is always "Should you?" and the second question is "Can you?". It is just like all the other CompSci stuff where you need to engineer smth that fits the problem well. There is absolutely no point in wasting countless hours on setting up complex models such as DL for some simple 1k rows dataset where in its deployment setting it doesn't have the necessary data for prediction. Good enough, manageable, (low/ un)biased, deployable, understandable non-cheating ML is what one should aim for.

My favorite is the scrubs that fucking hyper up GLMs as machine learning. "wE dId mAcHiNE lEaRNiNg wItH lOgIsTiC rEgReSsIoN"

[@anirudhtd7193]

Hey can you guide me on how to do that? Any books, any resources, where I can learn this thing fundamentally?

[@CeruleanAnthracite]

I agree! ML/ data science is so much more than just coding complex stuff. Understanding what you're doing and understanding the data itself is super important and imo an important first step

[@sagojez]

ANIRUDH TD Imperial London College launched a course of mathematics for Machine Learning. After that you can do the Machine Learning course from Andrew Ng. I think those two set a good basis for truly understanding ML.

[@sagojez]

I think that the main problem is that all that work from those PhD geniuses making life easier through very complex code reduced into a simple class or functions has really take a toll on newcomers ML aspirants. People don’t realize the probability theory behind all those functions so it’s blind for them. Nowadays people just sit down, load the data and run the machine learning function. They don’t take the time of checking the distribution of the data, the standard deviation and all that important concepts that are fundamental for a good prediction.

[@mattnann4365]

There’s specific algorithms that tune all the hyper-parameters like Bayesian optimization so you don’t have to just randomly tune hyper-parameters. The real work is not adjusting values by 5% to improve the accuracy, that’s a perfect menial job for a computer.

[@sehbanomer8151]

-be rich -grid search

[@prasad9012]

After working as a software dev for a few years, I recently took interest in ML. Now, I have similar thoughts as you. Learning the math behind ML and the model building process you mentioned is really taxing.

[@a_maxed_out_handle_of_30_chars]

true

[@ebrensi]

I actually come from a math background. I have a MS in applied math. A lot of grad students' first thing they do after they graduate is go into "data science" because that's what is hyped. I tried it but didn't find it that interesting. I find web dev front end stuff more fun. I like having an idea first and then implementing it learning what I need to know as I go along. Whereas what I see a lot in the ML world is people learning the techniques first and then trying to find something to apply them to. I have trouble bring motivated to do that. If I see a use for an ML model in something I actually find interesting, I would have no problem doing it.

[@hichembenchikh930]

what is the best sources to get good math background ??

[@tyrantula767]

hichem Benchikh I’m using Khan Academy, brilliant.org, and some for dummies books

[@evanwilliams2048]

@@hichembenchikh930 uni

[@thefran901]

The way I learned it and came to enjoy it was by researching the theory first, but then when I came to its application, I didn't use python (since I knew too little of it) and I didn't use any library whatsoever. I went pure raw c++ (what I knew best) and as I said, I didn't use any library, I just coded the entire thing myself. I had to re-learn derivatives, so I picked math books, and went to math videos explaining the whole thing from scratch. It was actually fun, I always thought that no matter how complicated something is, when you do it yourself, when every single step is done by yourself, no matter how frustrating some moments get, it's infinitely more rewarding. Only after learning that, and after getting competent at it, I started learning python and picking some libraries, slowly, one by one. But if you are just copying and pasting code, you might get results faster, but in my opinion, you are doing it the wrong way as you will have no idea of what you are doing for half the process, and at least for me, that's not enjoyable.

[@Ruum]

This aged well. 💀

[@LostinMango]

Yep it's aged like wine soybots were hyping AI thinking it's gonna give them AI waifu.

[@makhalid1999]

​@@LostinMango It won't???? 😔😔😔😫😫😫

[@amoghskulkarni]

Probably you can try to get into the math of ML and try to make sense of why some set of parameters work and why others don't, to make it less boring. The mathematical intuition will probably help you in the longer run in your future projects.

[@bawad]

yeah if you want to get into ML, learning the math behind it is a good idea

[@____-gy5mq]

um.. no. It's either the math or the application. The industry forces you to do so.

[@ScribbleDribble]

@S R ????? LMAO

[@oxyht]

@S R hahaha what?? oh come on, how do we become the bad guys lol.??

[@mehedihasan-ye3pg]

@@oxyht Probably he watched too much of 'those' call centre videos.😅

[@etienne12]

Nice content. I love how your channel is not just about tutorials, but also opinion and personal experience. Subscribed!

[@bawad]

thanks, welcome :)

[@RobertWildling]

When I attended an AI meetup (my first, and so far last), I was quite surprised how the programmers talked about the code: "Hopefully it is picking it [in this case a sound frequency] up now." Let's see if that will yield any results." Things like that. Only 2 days before that I attended a "functional programming in javascript" meetup, where each and everyone was so happy about the core message of FN (as it were): " you get **exactly** what you want, and you get it always!" What a contradiction that was - "maybe it works" vs "it will always work as expected" - - - - at least emotionally...

[@DoubleM55]

Exactly, and if you're lucky, and everything turns perfect, you can expect something like 65-70% correctness, depending on the problem. I won't trust my life to something that vague. Imagine if your car's brakes worked only 80% of the time. Not good.

[@PRATIK1900]

I'm someone who switched from learning ML to Full-stack development. One of the (several) reasons for that was, there was no real sense of achievement, or completion, or CERTAINTY. Like something you did in ML could always be improved upon, unlike normal coding. Like, there was no end to solving a problem, no concrete finishing line, and reward (I'm talking of feeling a sense of reward). I realized that I was not really feeling fulfilled and that the reason I got into it was simply because of the hype, and the lure of stable future.

[@MrCmon113]

I mean you can program a learning system with a language of any paradigm...

[@MrCmon113]

@@DoubleM55 Brakes worked much worse before they were optimized. And you wouldn't want to get into the plane with a pilot, who hasn't yet learned how to fly.

[@thenayancat8802]

@@PRATIK1900 I mean... there are plenty of programming problems that are computationally intractable and require greedy solutions, all of which can in principle be improved on.

[@carlosjosejimenezbermudez9255]

Regardless of whether he had any basics or not, I think it is very important to recognize that the role of an ML Engineer or a data scientist is very different to that one of a backend or software dev. It is important to recognize that if you already enjoy your work in one of those roles, finding an opportunity that allows you to experiment with doing data analysis or even a deep neural net and keep your common responsibilities in another web dev role is near impossible. To learn ML or DL and apply it correctly you will most likely end up doing a career swap.

[@arsh2489]

I'm lowkey a fan of machine learning Ben!

[@abcd123906]

Honestly, part of the reason I never got into ML was that I had a hunch that what you say in this video would be the case. Glad to have my suspicions confirmed! Thank you :)

[@oxyht]

Totally, I always thought it would be like this. But just like he said, I would like to use ML libraries in my project, that I am cool with.

[@rsouchereau]

Definitely agree with all of this. I’m a PhD Student focusing on ML applications for biomechanics. I think the coolest part is finding unique applications for your models and integrating them into a software application. You’re definitely right. It’s not for everyone, because it is extremely iterative and maybe even monotonous. But when you understand the math and the specific hyperparameters the iterative process can be expedited. It definitely can be boring at times but I guess it does depend on the person

[@baiqing]

When you don’t take linear algebra and can’t figure out back prop:

[@LanPodder]

Back prop? Is that a short for something?

[@syedhasnain2014]

@@LanPodder backpropagation

[@nazarm6215]

Didn't take linear algebra but I understand the gist. Basically go back and redo the training with the successful dataset and adjusted bias.

[@sehbanomer8151]

@@nazarm6215 strongly recommend you 3blue1brown's videos on deep learning, they'll give you a good intuition of what neural networks and backprop are.

[@abhishekshankar1136]

You don't really need linear algebra for backprop You need good calculus Also most frameworks don't require you to compute backpropagation

[@SoulReaver]

"mine's going to be linear regression" you got me there for a second.

[@merhabamerhaba9823]

İ didnt understand can you explain

[@SoulReaver]

@@merhabamerhaba9823 no, I can't lecture you for hours in machine learning so you can understand the joke.

[@merhabamerhaba9823]

@@SoulReaver jdksnxkdnskcnddn

[@anuragbapat2222]

@@SoulReaver Hahahahaha

[@fxshlein]

We had to train a model at work once, and it was really exciting at first because we were given a few months to learn about Machine leaning. After the fun learning Phase we got to tuning Parameters over and over and it got boring tho, i just made a script that trains the model a few times with every Combination of Parameters possible, let that run for two weeks, and we got a nice model lmao

[@fxshlein]

At the end machine learning is always just Approximation and I dont really like that

[@sciencecompliance235]

I ventured into ML a bit a few months ago. It definitely seemed tedious to get a model working. I backed off when I realized I had plenty of other stuff to learn and didn't need to get deep into machine learning at that point in time.

[@sandyz1000]

The only way for a beginners to get interested in Machine Learning is to never give up and deep dive on to understanding the concept. Machine Learning is a different way of programming from traditional way of what we are accustomed with, once you are familiar with the concept you are ready to rock and roll everyday... A piece of advice for engineer to begin their journey in ML by building some of the neural network from scratch or debug the tensorflow/pytorch library you will get lot of idea about the algorithms and the feedback loop will be faster while learning.

[@ConquerJS]

Your reasons sound convinving however I'm terrified that regular software development WITHOUT any kind of machine learning or AI will drop in popularity dramatically. Just like people don't really make static websites anymore like they used to, I feel like people aren't going to want "dumb" crud apps that can't take meaningful action independently.

[@garrettlovetv]

I would think that would largely depend on what's needed in the market. If AI becomes good enough that it can write software itself then yeah sure, but until devs will always be needed to build complex web apps, mobile apps, games, server side stuff, etc.

[@bawad]

I see ML being incorporated with more apps, but there will be tools that make it easy to integrate without every software developer needed to develop his own model

[@garrettlovetv]

@@bawad I agree with that too. There's already a good amount of that

[@calmsh0t]

Don't worry, there will be plenty of jobs for us non-ML devs. It will over time become a regular implementation. Just like you don't really know need to know anything about complex hashing algorithms to be able to securely hash data

[@WestCoastAce27]

Agree w @Pasquale below. And read up on 'the Hype Cycle' - ML will drop in buzz soon enough. It's a very promising tech - for some use cases. Not a solution to everything. As Ben states the time (thus cost) to ramp it up to being 'accurate enough' can be huge - won't be viable in many situations.

[@ajit555db]

You beautifully explained and summarized my exact experience with ML. Moved back to building web apps.

[@j22n3s]

Enjoy your shit pay. ML engineer 400K salary reporting in.

[@juanestebanguevara9235]

@@j22n3s how did you got your first job?

[@Toopa88]

@@j22n3s Who cares about the money when you dislike the job?

[@Daniel_WR_Hart]

I made a hunter/gatherer simulation in Unity with their ML tools and this was pretty much my experience too. At least in version 0.3b I would have to wait for over an hour to see if the changes I made to my training setup was any good.

[@ggh_-ts6pn]

thats is true. Also additional things to add from me as a working data scientist: in kaggle its all about accuracy, accuracy, and accuracy. but in real world most of the time it doesn't matter. What matter is make a model that is good enough that you can explain to your non technical client or manager. And most of the time you will just use logistic regression or similar linear model because it is pretty much the only model that non technical people can understand. It is also the least black-box model when you can easily explain your parameters to non-technical people. And most of the time, you don't even need machine learning, just standard statistical tests are enough. And yes, 80% of your work is data cleaning. And you maybe think "ah thats because you dont work for cool technology like image recognition AI robotic etc". That maybe true, but unless you are really smart and has phd and released paper or working for FANG, you will also just use models made by other people, and your work will also just tuning the model. Maybe you will just connecting API made by Google, Apple n others, because they provide APIs for image recognition etc. I'm not saying data science job sucks, but it is too glorified.I still like my job and in my opinion it is still better compares to my previous engineering job. But if you think you will make all the cool shits all the time, you are wrong. Unless you work in R&D department in FANG company I guess (but tell me, how many percent of the world's data science jobs are like that? Its very very small proportions, most data science jobs in real world are for banks, consulting firm, insurance, e-commerce, etc). It is pretty much like any other jobs, where 80% of your jobs are boring tedious routine.

[@F4TP]

I wrote a web app which utilised ML for my dissertation in my last degree. I understand what you are saying about about the repetitive process involved in tuning the model and the unexpected caveats (e.g. the model being too specifically trained to the data used and resulting in it being less effective at generalising on unseen data). For me, however, the interest came in the effects of introducing less obvious features into the mix. Those features that seem at first unrelatable, where others would not think to put them together, and correlation at first not obvious. Seeing an increase in prediction because you thought of using them to aid prediction interested me a lot. A sort of immediate mechanism to validate a connection between something you had a hunch on, even if it couldn't be explained.

[@vidhu3631]

If you have large dataset. You could probably take small enough sample to train and test a model quickly before using that model to be trained over the entire dataset(‘s 70-80%). Also their are some online (free and paid) resources available to do the processing faster but in general the process is iterative. So it could come down to how, during the exploration process, when you analyse the data, what kind of model do you think would suit the data. For example if you plot a dataset and see a linear trend with low spread you could for linear regression, if you see an exponential trend still you could use linear regression by transforming the dataset by taking its logarithm. Going head first into problem can lead to multiple iteration and the pros are that you gain valuable in depth knowledge of what doesn’t work. Alternatively, you could formally do a course that that demonstrates implementation of models suited for varied datasets, so that for any new project you know what model would suit the dataset aka give best predictions. Additionally, instead of straight away diving into its implementation and then tinkering the model parameters or datafields one could revise the mathematics of that model for some time.

[@avgmean4187]

I see your point, I used to be a Data Analyst in a Social Listening company (Audience Analysis, Sentiment Analysis and Topic Categorization). At first I did repeat a process similar to yours. Scrap data from Social Networks, then boolean search for a topic, pay some indian dudes on amazon turk to get a somewhat decent dataset (labeled), clean it up and start trying out models. At first it was thrilling (I had to come up with the process). Then it became real hard for me to show up at work, it was boring AF. Truth is ML is not that challenging, just repetitive. You can set up your whole suite in a couple months and then just plug an play with data, fix your visualizations and integrations and your done. Level up to data streams realtime (or cloud based ML) and that's it. From time to time you try out new libraries or tweak your algorithms but that's it. Clients were contempt with the product, then my served accounts multiplied tenfold, several google marketing departments (B2B, Waymo, Small Business and several others). Then boredom multiplied tenfold. In all honesty, scrapping was the funniest part of this whole process, imagine.

[@avgmean4187]

BTW fuck Cambridge Analytica, they forced the Zuck to make life so much harder for humble data farmers like myself. Luckily I can still access mobile.facebook.com/

[@mostafashawki]

Yeah... I fully understand you, I am a full stack developer, and I would say that I also feel boring with ML :( But it depends on the project, some projects really cool to include some ML in it.

[@SamuelKupferschmid]

I am between the two world's (ML and WebDev) I see your point. In "traditional" Software development you can quite fast build a somehow working solution. In ML you can come up with a baseline model within an hour, but to have something usable (for example to provide as a service) you need this time-consuming iterations. Make them as short as possible and setup some metrics which helps to decide what your next step should be. There you can face lack of tooling or experience. Scrum, GIt, CI Tools and all this stuff is often not primarly developed for this type of work. But I see how different tools get better month by month..

[@yx5991]

I’m currently a web dev and not looking to switch but interested in the data science side , so I have been studying that.. so far getting Famiar with python

[@smakosh]

I went through the same thing man, I was planing to read about every neural net architecture & publish an article about it, so far I went through the perceptron, I got how does a multilayer & CNN work but I seriously got stuck on the RNN LSTM as it requires lot of maths and I didn't really enjoy using those libraries like tensorflow or sklearn because I end up just copying and pasting code which I didn't enjoy so I had to stop.

[@bawad]

Yeah it takes a lot of study to understand how it all works

[@smakosh]

@Shah Bhuiyan who will fund me while I study that?

[@fathurrachman9498]

dude, you work in academia? If you do, just find another job.

[@unique1o1-g5h]

I'm with you on this one. I too did some ML projects when I was in college and it was fun at first then it just started getting boring, waiting for the model to finish training and tuning all these parameters again and again until your loss function is low was just not for me. But the thing is I do like doing data science stuff if I had the opportunities but not ML. And like you said doing system design and backend engineering is more of my thing

[@nkristianschmidt]

Yes, I work with ML projects, and you are right but it's supply and demand. And you get to build user interfaces in ML projects as well.

[@kaunas163]

I have tried learning ML. Totally agree with you. I would create something visual, that could be used right away, rather than creating model, adjust, wait wait, test, adjust, wait, test, adjust, wait. It's like a lot of waiting for something that you are guessing on, rather than creating real deals :/

[@mikhailfludkov5672]

I reivew lots of CVs that come to the company where I work. Some of those are from fresh grads. I see it all the time that there are candidates with interesting universtiy projects around system programming or in distributed systems, but they feel almost obligated say something about ML & AI in their CV. When you talk to them turns out they dont fully understand what it is or just say that don't enjoy ML. I usualy appreciate honesty. It reminds me how 5 years ago we had the same conversation about "big data". I think it is ok to finaly say it. I dont enjoy working on AI :) and let someone, who knows what they are doing, do it.

[@ElikemTheTuner]

Which company? I'm job-hunting...

[@venkataramana8488]

The most difficult part of the process is having to wait for the model to train and evaluate. The major drawback many ML enthusiasts have is not having sufficient compute power to work on models. Although, there are free trails available but that doesn't add up to quality time. Unless one has access to good GPU, it would be really boring if not frustrating at times.

[@elultimopujilense]

You should try machine learning applied to robotics. Its the most awesome thing that exist in my opinion. You can do virtually anything.

[@yvsonnunes945]

Dude, can you give/show me the material/course that you are using to study ml applied to robotics because I'm new in this world and I'm kinda studying this by myself in some courses, but I haven't found any course that is specifically focused in this kind of application

[@elultimopujilense]

@@yvsonnunes945 sure! You can start by taking the deeplearning coursera course from deeplearning.ai. They teach you ml and deep learning from the basics, so you can have a solid understanding of everything. Best course ive taken, even better than some universities programs ive seen. You can apply for financial aid and pay for the certificates later, like I did. You can then learn a python framework for ml, like pytorch. Thats the one used by Tesla. If you dont have a gammer computer and you wanna take everything a step further, i strongly suggest you buy a Nvidia Jetson Tx2 or Jetson nano to train and test your algorithms. Those are not expensive at all. I got one from my university. After that, you can learn the robot operating system(ros), its easier than it looks and theres plenty of documentation to get you started. After that, i think you will have enough knowledge to build real intelligent machines, and if you need to learn something you will know where to find the info. Let me give you and advice: start as soon as possible. You will see than AI is one of the most important achievements of the human race, rivaling the discovery and adoption of electricity. You can be a real player in the world of tomorrow. You will see that our future will be waaaay more awesome and strange than any of us could ever imagine, and few people are going to take advantage of it. Start now!

[@jayasri6764]

@@elultimopujilense Well,If you apply ml to robotics,there is a very low chance that it will work .If it supposedly works,you can very easily make sure it doesn't work,by adding a trivial obstacle.(suppose you "teach" the machine to walk on Flat surface,you can just add a small stone along the path,and the bot will have to "relearn" behaviour)Instead of a stone,add a human being,and the machine will never learn to distinguish between the presence and absence of the human and will never work. (Unless,ofcourse the maker comes up with a clever solution,which Introduces newer problems,which the machine cannot solve (

[@elultimopujilense]

@@jayasri6764 dude, this is 2020, not the 80s. Im really surprised of how confident you are about your opinions, even after having no knowledge of robotics. Robotics is way more advanced than you even imagine, and ML is just an essential part of it. You just cant have robotics without ML. Do a research about the filed of robotics, you will be surprised of how advanced it is. The problems you described were solved a long time ago. It is so complex now that we have many subfields, like path planning, computer vision, mobile robotics and many others. Just look at tesla dude. They use deep lesrning and computer vision to make their cars autonomous, and there are many ted talks about robotics and ml. I can tell you this because i studied robotics in my university. I wrote and published many papers. I worked on autonomous drones. I also implemented an algorithm called SLAM, and worked with the kalman filter to make data fusion. A friend of mine created an algorithm to make a drone detect all kinds of objects and evade them, and that was like 4 years ago. Robotics is a really complex field, and you need to know not only programming but also lots of math, physics, statistics, networking and signal processing to make things work, but you can also create almost anything. Imagination is your only limitation.

[@elultimopujilense]

@@jayasri6764 have you heard of boston dynamics? Just watch a video of the robots those guys make. They solved all the problems you described a long time ago.

[@subzoronltd7779]

I’ve sort of felt a similar thing with my own work with deep learning. Though Fortunately for me as I’m working on a research project where I’m in charge of a lot of the technical aspects and their implementation, I also work on developing the mobile app for it, the data pipeline, the UI etc as well. So luckily for me I get to mix things up a bit and that ends up making the deep learning parts very interesting. Plus I also get to look for different algorithms/solutions myself and learn a ton, and I don’t have what algorithms/approaches get used dictated to me by the higher ups so much. It could just be that you are more suited to working for a smaller scale project/startup rather then developing some random model that’s a small piece in a large company.

[@juanandrade2998]

What I got from all this is that ML is basically like hammering a puzzle piece until it fits.

[@lucasv4q]

Hello ive been watching a lot videos from this channel cuz your pronunciation its very smooth and it helps me to improove my english skills. Thanks

[@cristian44161]

Just what i needed to hear to confirm my suspicion, you saved me some potential years of suffering, thanks!!

[@HVAC-EDUCATION]

interesting, i did learn a lot about ML, I am 53, true, it feels better building something, plus not many applications yet and feel in the future their will be graphical tools and much easier to use software

[@Walnussbaer95]

Totaly agree with you. I did a Business Intelligence/ Data Science major during my studies. But I'm not that interested in it anymore, because I find Full Stack Web development much more appealing and fun. At the moment, I'm specializing in using Spring Boot and Angular in combination. It's just so much fun to create applications for real-world problems. Keep up with your nice videos, you just earned a sub!

[@charliechen912]

I feel the same way! The end of DL may be interesting but the process is quite boring and is hard for non-methematics backgroud people. But I kind of have to learn DL for my research :( Still waiting for next trend in data analysis field!

[@devalmedia]

Although tuning your Machine Learning model to obtain better metrics is a big part of ML, I think you are missing where the beauty lies with ML/AI. The thing that attracts people to the field of ML is more about leveraging the data you have and being able to extract valuable and meaningful information from it. What I believe many people in the field consider to be the exciting part is not necessarily limited to the points you discussed, but rather using their deep understanding of the math and algorithms behind many machine learning techniques and understanding which algorithms to use for the particular data and use case. This process of analyzing what features from a dataset and what mathematical techniques, such as distance metric optimization, feature reduction, etc to apply to your data to get better result with the paired algorithm being used is what attracts people to ML. Personally, my favorite part about ML is the process of model selection, applying my knowledge of different ML algorithms paired with analyzing the meaning behind the data at hand to create the best model I can. The analogy that comes to mind is, this is like me saying “I dislike web dev because all I do is make buttons look nice, some pages, and some API endpoints.”. However, the people that love web dev can understand the complexity that goes into it and have an appreciation for it. Just like ML, if you gain a deeper understanding, you will gain a deep appreciation for it.

[@siddharthkulkarni625]

Coming from a Java background and programming Enterprise systems, I completely feel you

[@Nick-tv5pu]

I think a lot of people would prefer to use an existing library in their application. The problem is, though, that there isn't always a good pre-trained model for your use case

[@wolfisraging]

As an experienced deep learning engineer myself, I think there are certain important things to always consider before jumping into it.. 1. You should have a good math understanding from linear algebra to calculus to probability to statistics. 2. Never start with ML, start with deep learning. DL is much more interesting and fun to do as compared to classic ML algorithms. 3. Always (almost) use GPU (or Google Colab if you don't have one). 4. Use modern DL frameworks like Tensorflow and Pytorch, instead of slow and old ones like scikit learn etc... which only works on CPU. 5. And its not at all like web dev, it actually takes years to get to an intermediate level experience where you'll actually find it damn enjoyable and addictive!

[@CeruleanAnthracite]

I agree with some of this but not all of it. Machine learning (with scikit etc.) isn't as 'fancy' or 'fun' as deep learning, but understanding data and the intuition behind why you're doing what you're doing is also important (if not the most important bit - because otherwise we have severely biased and unintentionally unethical models), and starting with the data itself, EDA, and basic ML can be immensely helpful IMO. Deep learning is fun and will be used in a lot of tech firms and research and certainly important if you want an ML engineer or research scientist job, but having a strong foundation of ML can even help you go into other areas like business etc., and help produce good business results if you ever want to transition out. Just throwing a neural network at everything isn't the best solution - the area of ML, in my opinion, is so much more than just coding.

[@wolfisraging]

@@CeruleanAnthracite I didn't mean to say that dl is better than ml. I totally do agree with you, I focused here more on dl because that's what he didn't try before quitting machine learning.

[@PandemicGameplay]

Stanford NLP is ancient. You should get back into ML and look at more modern methods. The way the math works behind it is absolutely mind blowing. The interesting part is making the model scalable and deploying via cloud.

[@AhirZamanSairi]

Any ML-experienced guy, please feel free to comment and educate me further, as this is basically what I've understood about all this AI stuff, and please tell me if I'm wrong: Unfortunately, ML roles consist of the boring data janitor part, because the company has pre-decided the best model to make more money anyway, so they don't leave you that fun-stuff. Top AI guy Andrew Ng Forbes: “The model and the code for many applications are basically a solved problem,” So that leaves you with the boring stuff of sifting through unbelievably boring data to make it optimal for the pre-decided model to process. And in the real world, even if you decide to do the modeling yourself on your own, without being bound by an employer, scaling that up is a whole other can of worms that only big companies are capable of, and you are left with a ferrari with no engine. If all this is correct, I will decide to walk away from AI/ML and focus maybe on backend, I'm on the fence, so I'd appreciate the input of an ML-experienced person.

[@patrickm.39]

5 months later, but yes that's correct...

[@AhirZamanSairi]

@@patrickm.39 doesn't matter 5 months, I appreciate it, also, assuming you're "ML experienced" like I said, can you please also tell me how SE will be affected? I always held the belief that it'll always remain a tool making software engineers efficient but still crucial. Just like tractors didn't make farmers obsolete. But people have been ranting and raving about how dark it looks for software engineers. This also means an aspiring software engineers has to watch out for wasting time with learning things AI will make unnecessary, but one might be confused on how to go about that and what to watch out for exactly. My reply just got longer than anticipated so, sorry, but I'd really appreciate your input, again, assuming you're ML or SE experienced

[@patrickm.39]

​@@AhirZamanSairi Today, tools like ChatGPT and CoPilot can't replace SE, indeed: they make too many mistakes and can't help you if your problem is not well documented (i.e. they weren't trained on that specific data). But I don't think they will always suck. Basically, all you need to make them better is more data and more parameters (more computing power). Companies like Google, FB, Apple... have enough resources to throw at them and make them more accurate. Also, the reason you still need humans today is that you have to be a SE to write good prompts, on the one hand and SE principles are focused on "maintainability", on the other. i.e. writing code that humans can easily understand and maintain. I see a future where the Frameworks and Systems are designed for AI, and AI writes code that works, regardless of whether humans understand it or not... I think you'll need a human "in the loop" for a long time, for manual testing, for example. Or to tell the AI what to do in the first place. But I wouldn't recommend a high school student to pursue SE today.

[@patrickm.39]

​@@AhirZamanSairi Sorry, I don't know why KZbin deleted my previous comment. SE, as it is today, is too complex to be replaced. That will take time. However, I see a future in which the underlying systems and hardware are designed for AI, instead of being focused on ease of maintenance by SEs. In my opinion, SEs should not waste time learning well-documented and standardized things. But if you're an SE, you're still better at writing prompts than a non-SE... You know what to ask for. I see a future (maybe in 10+ years?) where the AI ​​tool is so good that anyone can generate decent software, good enough to satisfy customers. This means less demand for SEs in the market. This is why I wouldn't recommend a 15 year old to enter SE now. Did I answer your question? Let me know... (I would recommend focusing on things that are not well documented (bcs A.I. relies on data). e.g. Research, R&D

[@AhirZamanSairi]

@@patrickm.39 I may not be able to reply again for about a month after this (although I'd like to) but I can reply now. So basically you're telling me yes it's bleak in the long term for SE's but not yet? I've heard this many times by seemingly qualified people in the field, as well as the exact opposite from people just as seemingly qualified. That's what leaves me in a conundrum. If only there was a way I can determine the outcome accurately with solid reason-based certainty that surpasses mere specualation. I get there's no certainties in life but 2 plus 2 is certainly 4, and that's the sort of certainty I'm after in this context.

[@procyon.lotor4]

Bruh, the Namescheap shirt. So silky soft. Got it from the LA Hacks hackathon back in 2013 and it was my favorite shirt for a long while.

[@bibislyvie1339]

Hello Ben, thanks for your video. I realized you do not like working on tasks in a repetitive manner, that's what makes ML ML and Data Science, Data Science. I strongly believe that you can't be good at something if you do not do it repeatedly. This applies to coding as well. For me it doesn't matter what ML does or what ML can't achieve. As long as I can bring tangible solutions to businesses using ML, then it's worth investing time and energy to learn it. It doesn't matter whether ML is difficult, less challenging, supper difficult, boring or not. To you it's boring, to others it's interesting, some people have secured the financial freedom just using ML, so it all depends on your vision, what you want and what you really want to achieve in life as well as the results you want. The tech field is very loaded and competitive, therefore I suggest everyone should strive to bring solutions to businesses using any technology they are comfortable with, it doesn't matter which one. If a company needs ML and you wanna work there, by all means, learn ML and bring solutions to their table. Thanks for your opinion I appreciate. Remain ever blessed.

[@ManPursueExcellence]

Bibi Slyvie This is the best and most realistic response I’ve read. You are definitely someone who understands things from a business perspective and a business owners perspective. I suspect too that, like me, you are not someone in their 20’s. After some life experience, you value things differently. You are correct. Repetition may be boring but, at least it gives a chance to be good at something and if their is strong demand for it, you can command a decent pay. You will spend less energy in your occupation because your skill level is higher and save that energy for non-occupational related things and have the security of your income. I’m not in tech but, from my own life experience, I understand exactly what you are saying. I am interested in learning Python but, primarily because it can help me do my work better. So, I’m coming into this thing already knowing what I want to apply it to and what problems I want to solve and I believe Python and data science can help to achieve that.

[@bibislyvie1339]

@@ManPursueExcellence Thank you for your reply, I sincerely appreciate your response. I wish you all the best in your endeavors.

[@Brandon-youtube]

mind inception: make a machine learning algorithm to tune your other machine learning algorithm

[@Onitzerk]

That's basically cross validation of hyperparameters or ensembling

[@jesusaromeroz]

I’m totally agree with you. I prefer to use the models someone else did to put it in an application 😊

[@tuananhdo1870]

lol, why you not say I'm prefer using the app someelse create instead of creating my own (suppose you are a mobile developer)

[@jesusaromeroz]

@@tuananhdo1870 That's an awesome point of view. And this can be responded easily: in a team, I prefer to create the UI, meanwhile (in that team) there is someone who prefer to construct the Backend, another the Architecture, another the ML Models, etc, etc, etc.

[@jesusaromeroz]

@Sebaka & Co. You sure, I'm proud to have a team and to be happy with that. I can notice you're proud to be you and your comments, that's great 😊👍

[@virdisantenor6322]

Dude, i've been watching your videos i realized that there are so many things that you don't like man.

[@tfox32101]

its good he actually has opinions on things

[@yonassintu3024]

lol .

[@georgeskhater487]

@@tfox32101 that's exactly what I wanted to say

[@ChumX100]

He just uses the hater personality as his YT identity to seem more interesting. Similar to Tech Lead.

[@kranthikiran3713]

You just forgot the most important and the interesting part i.e Feature Engineering where we analyse the data and brainstorm over data to build new features which could benefit the ML model. They also say that almost 70-80% of your time should be devoted into cleaning data, understanding data, and feature engineering and only 10-20% of your time into selecting and tuning your model. I guess what turned you off was the parameter tweaking and and time taken for any particular algorithm which is mostly done by Kagglers just to get an edge at the end of competition.

[@CeruleanAnthracite]

Yes! ML isn't just about coding and much more about data/stats/intuition and even the code is just a means to an end that is to use that derived meaning in what often happens to be another area.

[@darkfafi]

I love the results of ML (next to it being a scary black box you don't know what it bases its guesses on), but I can imagine this process being rather tedious. Thank you for your insights!

[@Vijwal]

Don't you dare say anything about my boy- 'machine learning', No offense but that stack overflow you use on Google, or that ide you use, ur Google assistant, simulation and physics prediction, weather forecast, games, tests that look at your weak point in a particular topic and make you good at it. Yep it's all machine learning and who even I am telling that to, you know it and you also know that without it ... Wait a sec, nothing would change without it games can just use normal codes instead of AI, simulations can still be set on particular rules and etc. Huh I just realised that my life was a lie. Jeez that went from a joke to dark *real* quick.

[@prod.kashkari3075]

You gotta like statistics + math concepts to enjoy the beauty of ML. I love statistics and naturally when I would do data science / ML projects I liked the insights I was making. I’d say if you don’t really enjoy learning math concepts then it’s not for some people.

[@jamiekenber]

1. Don’t manually tune parameters (that will be tedious no matter who you are) 2. Look beyond FF models, with standard loss functions (think about what network design suits your problem, and what stats distributions/loss to use) 3. Think all the way through to the tech & business implementation, and the implications that has on your model design Then you might find the process more fun

[@doubt_everything]

Totally my thoughts man. Currently learning neural networks, just want to make a game AI and then I don't know if I will stick to it.

[@koffiegast]

What kind of game AI do u need? Chances are is that NN is not the way to go. Lots of games of AI based on just simulating movement or emergent behaviour (look up how F.E.A.R. does it with states). So much easier and lots more quick results and u can actually inspect+control.

[@user-zo2ky4mz7d]

You basically read my mind. I've been working on an ML related project for a while now with my team and just like you I've been more interested in using the ML libraries/models in my applications rather than implementing the models myself. I just found it really boring and kinda like grunt work. Great video though.

[@anoopm3605]

This is exactly what I had in mind. Thanks for telling it loud

[@GuRuGeorge03]

I basically use ML whenever I want to recommend something to a user from a database filled with user generated content. similar to how youtube wants to recommend videos to you, without knowing beforehand which videos will be available. I think that kind of reinforced learning is actually the most useful and also pretty easy to implement compared to a lot problems that people try to tackle that don't have a real world application (yet)

[@ajaiakaoaosnaiansjaoanskak]

Totally agree. Took a course on linear algebra in college and it bored me to no end. Maybe I just don't like math/statistics. I'll just stick to app dev, cause it's fun to me, and I can do it for hours on end. I feel like non-ML/DS software engineering will still continue to exist for a long time, so there's nothing to worry about. I see some software engineers rushing to learn ML/DS because it's the new hot tech, despite them not even enjoying the subject matter. That seems like a good way to be miserable at your job. For what? Clout? Additionally, ML/DS will eventually get super high-level and libraries like tensorflow will get refined to a point where it's basically plug and play with a low learning curve. Hell, it may already be close to that state, I haven't looked. At that point, the remainder of ML/DS jobs will probably be for PhDs that do research into developing new algos. I'm glad there are more sub-categories of software engineering popping up, whether it be an intersection b/t CS and Stats (ML) , CS and Finance (algo trading), CS and Physics (game engine development), etc. That just gives us more options to specialize on what interests us the most.

[@sakhawat3003]

I think with a pretty good understanding of Statistics, Linear Algebra, Differential Calculus(optimization), Probability and a very good familiarity with the parameters used in many ML algorithms, Machine Learning is just fun. First, I always do some statistical analysis on the data so I have a good insight whats under the hood. Blindly doing some parameter tuning on an iterative process without any understanding of their underlying influences on the models is the worst. And when my algorithms are busy building models I normally do other fun stuffs.

[@sakhawat3003]

@S Ryou are absolutely right. I didn't say that you have to learn them beforehand. Its the other way around. First you grow up your interest then learn whatever it is needed to understand them. And its the top down method that I have actually followed.

[@TheDeviszont]

You are right Ben, looping through random shyte that you don't understand is boring. Very inspiring!

[@achieverbas6104]

Finally truth has been spoken

[@fasolplanetarium]

Same sentiments from a fellow SWE here; you're not the only one. I tried getting into ML multiple times, in many different contexts and with many different use-cases in mind. Each time, I found myself bored and disinterested, which is crazy because most anything comp-sci is absolutely gripping to me. ML, though - eh, I'm just not into it. Note: I actually took courses and read about the math/algorithms behind ML, not just tossing something together with premade datasets. Huge math hobbyist here, too. And still, I was bored.

[@CeruleanAnthracite]

I think (this applies to me) the appeal is more about the data and what can be done to generate insights from it. From CS people who don't enjoy coding/ engineering as much, using stats to derive meaning from the data can be super fun. Sure, there's deep learning and more fancy stuff, but not a lot of places outside of FAANG/DeepMind/OpenAI are using it. I see ML as way less about coding than it is about the data and using the data to do something useful, which often happens to be more on the applied side than the research or engineering side.

[@riomh]

I'm a first year uni student who was bumped up into some third year classes that "don't have big prerequisites" (I feel like that's a red flag for subpar stuff..). One of those classes is ML. It has been interesting learning how this stuff worked (from the surface level sklearn) but at least from my personal experience I still don't really understand much about how it actually works mathematically, and this surface level stuff I could have learned on my own. Literally all I needed was the name of the book we were working from, and some structure in going through it for practicing (e.g. labs n Kaggle and tests). I came to uni to learn low-level stuff so this frustrates me. This year seems to be 100% surface level bs - one of my 200 level classes is literally just teaching yourself how to use two software. I'm now needing to reign in my hopes of what uni will be over the next 3 years... On the bright side of ML, we do have a great professor :)

[@xyzzy4567]

We need diverse skill sets. ML gets a lot of hype these days so good to hear someone explaining why other areas of software development are exciting.

[@billylardner]

I’d argue ML is the most exciting area of software development and certainly the most in demand atm.

[@manas_singh]

Hi Ben even I don't like ML. They are not doing real mind boggling stuff but most of them are simply making a few parameters and optimizing them through random algorithms on github until they reach good accuracy.

[@ZeMakerpen]

I'm currently working on a project that requires img recognition to recognise subtle difference between colours in real life (i.e. light blue and very blue). I"ve clicked >>20 k images as part of the data preparation..

[@MaxwellMcKinnon]

I mean, it’s a fair take, and i find this perspective interesting so thanks for sharing it, but in a lot of ways it is the equivalent of someone who’s used the terminal for a week and is now declaring it inefficient and boring.

[@tfox32101]

I dont think so. He said he prefers building a product/project verus iterating on models. He didnt really say anything negative about the tools, he actually said the tools were cool.

[@alessandroferrari4699]

@@tfox32101 "The terminal is cool but I don't like, I prefer using GUI programs on which I can see all the buttons, I don't think that the terminal is for me", What Maxwell McKinnon said isn't a bad thing either

[@AJ23mady]

@@alessandroferrari4699 Analogies are like elastic bands, if you stretch it too far, it will break

[@alessandroferrari4699]

@@AJ23mady Please don't start with the Vin Diesel quotes if you aren't going to elaborate

[@AJ23mady]

@@alessandroferrari4699 the terminal vs gui analogy sucks and doesn't make sense.

[@roysmith5711]

I have been coding for about 6 years now. So, why am I subscribed to you? First of all, I still learn things from you but most importantly I mostly agree with you on these things and I know most of my colleagues do too. Thus I shared your channel with them.

[@bloodphantom81]

not to be rude or blunt, but it sounds like you’re coming from a place where ML is a black box, and generally you seem a bit inexperienced. honestly, I’d be pretty overwhelmed in that position too, and I can’t imagine working with something I didn’t fully understand e.g. imagine building a website without knowing system design or architecture components like databases. ML started getting fun once I really understood what was going on under the hood. obviously scikit-learn (not skykit-learn lmao) makes it easy to build models in just a few lines, so you might get the impression that it’s not worth it to know the complex and beautiful algorithms in training. one other note is that ML deployment is another just as rewarding aspect, since it’s interesting to know if the model stacks up against real world data. I know you touched up on that a bit, just wanted to point it out again since you talked about Kaggle quite a bit (which I find a bit boring unless it’s to learn a new skill or get inspiration from notebooks). lastly, using ML to make business decisions is quite fun too. predicting the probability of items being purchased, whether a user will leave the app they downloaded in a week, market segmentation on the different attributes of users to understand how to market efficiently, recommending items to users, reducing a 10 dimensional dataset to 2 dimensions to be able to visualize it, etc are all ML :) part of why Amazon and Netflix were so sticky and grew quickly was listing content or products users were interested in. I tried my best to not seem elitist, just point out how ML can be more applicable when understood better and when applied to real world problems.

[@tedk-42]

Umm it is a black box. Just like our brains when we make a decision. ML tend to be less about logic and more about tuning and trying out random stuff to help improve the model you've built. It's not for everyone. If you watch his stuff, the dude knows his shit.

[@bloodphantom81]

Ed Kim while some models are more complex than others, you cannot tell me linear regression, logistic regression, bayesian models, decision tree, knn, k-means, etc are black boxes. it is very easy to crack them open and see EXACTLY what’s going on and why it’s predicting what it is. and no, in terms of performance, ML is just as well about clever feature engineering e.g. NLP and image classification are all about feature engineering and transforming the input, not trying out random amounts of layers or neurons. you also didn’t address deployment and combining it with business analytics, etc which are also parts of ML, not just accuracy. I’m sure this kid is good at other things, but ML is not one of them, and the same can be said about you.

[@tedk-42]

@@bloodphantom81 Yeah the basic ones (barely ML really) you listed are understandable. Many complex models have these layered together along with others. Many ML/AI SaaS offerings don't let you dig into their code or implementation detail, making the blackboxes. You just train the model and tune the parameters and get your output with a percentile confidence rating that you're always trying to improve upon. See the comments here from DeepMind researchers: towardsdatascience.com/can-we-let-algorithm-take-decisions-we-cannot-explain-a4e8e51e2060

[@bloodphantom81]

Ed Kim the context in which I was using black box was that this content creator doesn’t know the effect of parameters or what makes up ML algorithms (he called sentiment analysis “semantic” - aka the “hello world” of neural nets), and the context you’re using it is that we (the public) don’t understand why industrial ML makes the decisions it does. different discussions here.

[@josefrinderer7111]

@@bloodphantom81 Idk why you're taking his opinion so emotionally. There's no need to put him down about all the shit he doesn't know about ML. I'm sure all of us have experience with ML (some with more experience than others), thus, some will enjoy it and others not. He even states towards the end, he'd rather build products that build models. I commend him for speaking his opinion. He knows what he likes and doesn't like. His reaction was very genuine. Sure, he forgot the word "sentiment" analysis. But he described it fairly well- which tells me, he's done it before...

[@marloelefant7500]

Actually, I find building applications quite boring. I did it for 3 years and found it rather repetitive and not very challenging. And when it's actually challenging, I feel like, it's challenging by design, like CSS - that language is definitely the most counter-intuitive thing that I've ever seen. I think it was probably the beginning of the Machine Learning hype when I got interested in artificial intelligence. But it was not because I believed that it's the tech of the future, I solely excited about automating work. My dream was a future in which I do not have to do any work, but let the machines do all this simple repetitive stuff, machines that transform customer needs to final products. And this was also my motivation to dive into the Machine Learning field. I wanted to have thinking and flexible machines that could do Human work, but when I saw that it's actually all about statistics, guessing and try'n'error, I was very disappointed about all that stuff. And there seems to be not too much thought process going on in the Machine Learning community. More often than not, it's just applying stuff and try out. It works, then you'll be successful, otherwise, nice try, next time.

[@LegoEddy]

I get your point and I think it's fair because you modestly called the video "why I...". However, the kaggle and applied stuff is just one part of ML, arguably the most boring. It only gets interesting (from my perspective as statistician) when you put it into your toolkit of design of experiments, field data analysis, hypothesis testing, belief updating etc. Then it can do what its best for (prediction) and for other problems, you don't have to bend ML towards it, but use the native solution like DoE etc for it.

[@JayJames]

I had the exact same experience but this was 8/9 years ago. I didn't like the slow feedback loop but curious if there are better ways to train the model thesedays

[@_mto]

i'm a machine learning engineer and i dont like machine learning either

[@semajxocliw]

pain

[@tungvu4339]

what do u do as a ML Engineer?, why u don't like it?

[@shrin210]

Nobody likes their Job most of the times. I'm not ML engineer.

I hated ML in my Engineering.

[@calamorta]

@@shrin210 The issue is that its a waste of time to study something which you don't even like...

[@zachwhite8054]

Ben Awad: I'm different. Me: We know Ben.

[@m_r__r_o_b_o_t]

For me it gets interesting when I’m looking at new _applications_ of machine learning. I agree that the process of building models, in itself, isn’t very interesting

[@tumul1474]

well one major thing is sentiment analysis seems boring to me...maybe i am wrong but i myself did in an intern for a month and got really bored. The thing is that there is not much to visualize/analyze(or maybe i was bad at it) but image recognition, recommender systems are one of the most fun stuff i have ever done, so it think it all comes to the kind of project u r doing

[@spitalhelles3380]

It's definitely more fun if you have a strong maths background. It gives you the tools to approach a machime learning problem almost philosophically. While from a coding perspective there's really not much to it..

[@maxime_weill]

serious question, if all you do to change a machine learning model is tweaking parameters, can't you automatise that? i'm not saying automatise everything, but part of it. for example, if you don't know how many layers your model should have, you could try every depth from 2 to 10 by training 9 different models and comparing results.

[@ilhamwicaksono5802]

I love reading ML fans and data scientist got butt hurted when someone just very personally think that he is not inrerested in ML

[@julioandresarriagarangel7183]

Nah. The code bros think the data industry involves just learning libraries, and they get butthurt when they see it doesn't.

[@br2716]

"butt hurted"

[@melikechoc0]

People ask me why I don't do ML. It's simple really, I'm bad at the math behind it. Sure, I can just implement it instead, but without knowing how it works behind, I cannot trust myself to make a good implementation.

[@alexforget]

In ML I enjoy seeing the progress made and what I can do with modest computing budget by reusing trained models. There is an incredible amount of progress being made, what we belive is impossible turn out to be resolve the next year. But I agree, managing, cleaning the data is a huge part of the work and it’s not very enjoyable.

[@jiachengli744]

Same here, for me creating or architecting software is much more fun. I do like to include machine learning models if it fits in the product but building machine learning models just doesn't excite me.

[@antopolskiy]

wow. that was painfully spot-on for me as an ML engineer 😅 especially the feedback loop length

[@TheIllarious]

Thank you very much for your point of view. It has been very useful for me :)