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Zubairbilli

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For someone who doesn't know a thing but is intrested in AI, where to start?
 

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Martin.G

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For someone who doesn't know a thing but is intrested in AI, where to start?
Do you know how to program?

If you don't, you can start learning Python, that's a programming language that you are probably going to use. There are others, of course, but in my opinion there are a lot of course and information on YouTube where they use it to teach. For example, the channel that I mention before.

Also, you can start reading the thread, there a re tons of course and recommendations here. Again, the YouTube channel that I mention earlier is a good start, you need to know the basic in python but then, you can learn more while you are watching the class. I like it because he starts from the basic aspect of ML. Also, he teaches how to use the libraries that are available, but also how to code the algorithms from scratch. I think is important too, because you see practically how they work and why the libraries are better because of their optimizations.

That's my two cents. I am not an expert and, from what I read here, there are very capable people around, maybe there have another advice.
 

Zubairbilli

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Do you know how to program?

If you don't, you can start learning Python, that's a programming language that you are probably going to use. There are others, of course, but in my opinion there are a lot of course and information on YouTube where they use it to teach. For example, the channel that I mention before.

Also, you can start reading the thread, there a re tons of course and recommendations here. Again, the YouTube channel that I mention earlier is a good start, you need to know the basic in python but then, you can learn more while you are watching the class. I like it because he starts from the basic aspect of ML. Also, he teaches how to use the libraries that are available, but also how to code the algorithms from scratch. I think is important too, because you see practically how they work and why the libraries are better because of their optimizations.

That's my two cents. I am not an expert and, from what I read here, there are very capable people around, maybe there have another advice.
What's the channel name?
 

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Didnt read the entire thread.

I'm interested in it, but don't know where I can reliably learn about it in order to adapt it into business.
 

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Hi all, just wanted to chip in as someone who works in this space. (Data Scientist)

For those considering getting into the field, I'd recommend considering this article (especially point #8). Don't take it too literally - but this echos a lot of the commentary I've personally made about the direction of the field.

This is pretty much spot on, based on my experience. After a few years bouncing around data science roles plus freelancing, it looks to me like many companies don't really have use for AI/ML as-promised. And often they're lacking the infrastructure to support model deployment even if they have a use case. Of course they don't know that yet, so it's a great job market now. This might mean a near-term bubble.

But as companies advance their tech (largely: get on cloud!), there may well be a point where machine learning becomes relevant for these companies. But I expect the data science landscape to have changed largely by then. The statistics side of this is already all but irrelevant for the practitioner. That matters for the people at Google/Amazon/Microsoft developing the algorithms; but for everyone else, you can call them as an API from your favorite cloud provider. This means (IMO) that the "data science" many have learned (fitting models with sklearn) will be irrelevant, and a developer skillset more and more important.

Anyways, exciting times. I'm working on a "prediction-as-a-service" type of product now that will hopefully lower the barrier to entry to machine learning for smaller companies who are used to working in Excel.

Excited to hear more about what you guys are doing in this space and potentially partnering up!
 
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I am into medical science. So I am actually planning to make learning for medical students easy and fun
If you come up with a course I'd love to check it out. Also check out this channel. This guy is literally the best teacher I've ever seen in any subject, ever. I spoke to him and convinced him to make a few videos outlining his teaching method. The way this guy teaches is revolutionary.

 

Martin.G

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Anyone knows a good book to read about ML? Recently I read The Hundred-Page Machine Learning Book, and I am starting Machine Learning Yearning by Andrew Ng.
 

SamHalen

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Anyone knows a good book to read about ML? Recently I read The Hundred-Page Machine Learning Book, and I am starting Machine Learning Yearning by Andrew Ng.
A good resource that's based in R: Hands-On Machine Learning with R

For Python users, this is probably best: Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems: Géron, Aurélien: 9781492032649: Amazon.com: Books

Honestly, I wish that I had started learning in Python
 
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ChrisV

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Honestly, I wish that I had started learning in Python
Yea, I'm an R coder and I really wish I had learned python as well.

Don't get me wrong, I love R but the way the industry is going you're probably going to be in a much better position if you know python.

These books are also great too:



You can also find their lectures on EdX / YouTube.
 

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1_4m_P4tty

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If you come up with a course I'd love to check it out. Also check out this channel. This guy is literally the best teacher I've ever seen in any subject, ever. I spoke to him and convinced him to make a few videos outlining his teaching method. The way this guy teaches is revolutionary.

Thanks for sharing :) I'd like to come up with a program that will help consolidate important information that is needed to ensure quality patient care. My observation is that many medical students graduate still clueless what to do in their field. I am all for learning all the basic and pertinent stuff so that when you go out there and do your job, you are well equipped with a solid knowledge base that could help you predict possible problems, how to avoid them, how to fix them and how to improve them.
 

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"Data Driven: Harnessing Data and AI to Reinvent Customer Engagement"

good primer on this (from a marketing perspective) , its what i'm doing, more as a "grab the coat tails" play then any specific genius maverick move on my part but I firmly believe AI / IoT is going to make more millionaires and billionaires in the coming decade then anything else on the scene.

besides the company I resell for : 6sense, Bombora, Intent Data IO, and LiveRamp are the only real players in this space.

The play is "intent" or behavioral marketing. So , when you google "emergency plumber dublin" you're letting google know you have probably a rather immediate need for an emergency plumber in dublin right?

Now lets frame out the path to purchase on something big time, a house or car. Well you probably do research right? so it wouldn't make sense to slam you with ads for a black jeep right when you still haven't decided between a car or a truck or a motorcycle now would it?

but those signals are still obvious, what happens when we examine the url level behavior (and any other data signals allowed with IoT), thousands of datapoints a day (a japanese researcher figured out a way to make a personalized fingerpirnt from the way you sit in a chair, hows that for unique personal data?) on hundreds of people who have compelted the path to purchase? what correlates do we see?

So you feed all this data into a machine elarning algo and tell it to look for patterns and voila, we see smoke , we see fire. Now i'm not going to sit here and pretend we dont need to eke out causation just because we have a lot of correlates but uh, i'll let some grad student pour over that junk later, right now I'll just make money on it.

What about an enterprise level transaction/ what if I could tell you that the Vp of marketing at the company you want as a client is considering purchase of your product before he's even consciousely aware of the fact? , what if we figure out a signal that would allow us to know in advance before a company moving millions around was even starting to consider a purchase? before it even occurs to accounting or gets to the CEO that a solution is even needed or a problem exists?

Why you could, open up old communication channels, forge new ones, maybe send a "just because" bottle of wine. If you're the first person in front of a customer you get frame control, now every other possible seller is competing in the frame you set , whats that worth?


Really exciting stuff, I mean what i'm talking about is just one application but lots of money is pouring into getting the entire product experience synchronized, no more ad spend on someone whos already purchased. Just enough ads on just enough channels (omnichannel marketing) to push you over the edge to purchase, tailoring individual ads (what if your smart tv's commercials started using your first name? , thats an easy one in the works even right now) , as more devices become internet connected and send more signals the possibilities just boggle. Whats a beverage company going to look like once they have biometric data about what the first taste does to you? , lots of crazy interesting stuff in the pipeline.
 

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I have a feeling that we're going to be talking about a bubble in the AI space in a couple years...
Agreed, some of the stuff is just sloppy application of API integration with "AI" slapped on to get funding.
 

Martin.G

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The other day I saw an interesting video about ML. It's an interview about one guy who sold his company.

The key point, I guess, is that we cannot expect to create a SaaS only about ML, but a some features with the technology (small maybe) and the rest of the system like any other.

In his case, he used a simple clustering, nothing fancy.

View: https://www.youtube.com/watch?v=fB7nyxXaczY&list=WL&index=3&t=0s
 

Julio Andres

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Following! I'm a software engineer, mainly app developer, but interested on entering AI-ML field because it sounds fun. I'm also taking the coursera deep learning courses, and looking into kaggle.com, it seems a pretty well know site for data science-ML practitioners.

And always thinking on how to apply this to some business. I'm searching on kaggle.com for interesting datasets, and get inspiration for a business.
 

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Also check out this channel. This guy is literally the best teacher I've ever seen in any subject, ever. I spoke to him and convinced him to make a few videos outlining his teaching method. The way this guy teaches is revolutionary.

I link to StatQuest in my free ebook on machine learning. I messaged the author and asked for permission, which he was ok with.

One of the people taking my course likes to debate. Yesterday he began trying to convince me that Root Mean Squared Error is a bad metric to use and that AIC/BIC is better. I had to dig out my old textbook (actually just google it) to answer the question.

Do I think that knowing all of this math is necessary? Not for 99% of people. The only reason that I learned this was because it was required for my job. It's useful for doing academic research, if teaching (in my case), or if working in a ML engineering role (in my old job). But many people today are just amassing the knowledge for no reason. They take online courses on Coursera/Udemy/Kaggle endlessly, for no purpose, only so that they can add it to their resume.

This is from our Moodle discussion forum:

33502

33503

33504
 

Martin.G

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Maybe someone can help me with this issue.

I have been playing with a DQN (rainbow) and NES games in and old computer (I5 4670, 8 GB and a 960 GTX 2 GB) because it's the only with a graphic card. I don't expect so much, but at least is faster than my Apple computer for ML task.

Lately I've been thinking to make an upgrade and I try to find what to upgrade first: CPU, GPU, RAM. So I saw my CPU is 100% while my GPU is around 50-60%. I made some research and I found that the DQN use mainly CPU, because the only part of the code that use GPU is the NN, and because is small it's not going to benefits for a better GPU.

While the replay memory is filling the CPU is around 50%, but when it starts to send batch to the NN it jumps to 100%. Also, the FPS down from 180 to 30

So if I buy a CPU (I've thinking a 3800X with 16 GB) I am going to see some improvement, but in the future when I upgrade the GPU is going to be the same. I mean, I can spend money but at the end the improvement is not going to be so great.

I am missing something?. At first, I thought that maybe the problem was lack of RAM because SO has to paginate, but with a smaller replay memory the same thing happens. Another thing that I thought it could be is the VRAM, but at least TensorFlow should not use RAM (The code is in PyTorch, but I think is kind of the same).

I wonder if with a better PC part I will see a significant improvement (various magnitudes) because there are something (like RAM or VRAM) that made the CPU work harder than it should. Or the CPU is going to be always the really bottleneck and if I don't but a Threadripper I am not going to see a real improvement.
 

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What’s the difference between machine learning, AI, and data science?

what would be a good intro course to get an understanding of the two to see if it’s something I want to Persue? Should I learn coding before I look into these subjects?

You are right there are numerous opportunities for machine learning AI and so forth. What it could do for people with disabilities is phenomenal. An automated car so that the blind don’t have to depend on others to get them places. A device that translate the spoken word Perfectly to text for the deaf. It’s endless!

Its funny how people fear that it will make jobs obsolete. No it just means new opportunities if we keep our eyes opened.
 

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Owner2Millions

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@ChrisV Are you going do another AI project this year? Im just now seeing this thread. subscribing to be notified on this thread.

I realize there is a lot of demand for AI and ML, especially for Robotics and Drones. So system engineers are going be in demand for a while and highly paid. But it has always been lucrative to learn the hardware and software side of tech. IMO

Everyone is launching SAAS apps for particular fields. It seems to be a bit oversaturated. I know this isn't the peak yet. But, I am seeing a ton of companies within SAAS looking like the bread aisle at Walmart lol.
 

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What’s the difference between machine learning, AI, and data science?

what would be a good intro course to get an understanding of the two to see if it’s something I want to Persue? Should I learn coding before I look into these subjects?

You are right there are numerous opportunities for machine learning AI and so forth. What it could do for people with disabilities is phenomenal. An automated car so that the blind don’t have to depend on others to get them places. A device that translate the spoken word Perfectly to text for the deaf. It’s endless!

Its funny how people fear that it will make jobs obsolete. No it just means new opportunities if we keep our eyes opened.
Data science basically involves discovering, interpreting and forecasting new info from masses of data, be it organised or not.

When I started out in analytics in university, I covered areas like:
  • Regression modelling
  • Forecasting
  • Data Mining
And so on.

Machine learning is simply training a mathematical model to produce future predictions without needing explicit programing. AI merely applies some of ML, as there's other stuff it needs to simulate human behaviour.

DataCamp has lots of great intro and advanced courses to start with.
But you should understand basic statistical principles like:

  • Hypothesis testing
  • Type 1 & Type 2 errors
  • Common biases, estimators and errors (e.g. RMSE)
  • The steps of data modelling
  • Sampling & cross-validation methods-and how they work
Data Camp already has basic courses for them.

After all, statistics is the very root of data science and ML.

I learned up data science with R, as it is more intuitive than say, Python, and somewhat less code-heavy.
So you can try learning it first.
But mind that R's UI can be quite outdated.
 

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Maybe someone can help me with this issue.

I have been playing with a DQN (rainbow) and NES games in and old computer (I5 4670, 8 GB and a 960 GTX 2 GB) because it's the only with a graphic card. I don't expect so much, but at least is faster than my Apple computer for ML task.

Lately I've been thinking to make an upgrade and I try to find what to upgrade first: CPU, GPU, RAM. So I saw my CPU is 100% while my GPU is around 50-60%. I made some research and I found that the DQN use mainly CPU, because the only part of the code that use GPU is the NN, and because is small it's not going to benefits for a better GPU.

While the replay memory is filling the CPU is around 50%, but when it starts to send batch to the NN it jumps to 100%. Also, the FPS down from 180 to 30

So if I buy a CPU (I've thinking a 3800X with 16 GB) I am going to see some improvement, but in the future when I upgrade the GPU is going to be the same. I mean, I can spend money but at the end the improvement is not going to be so great.

I am missing something?. At first, I thought that maybe the problem was lack of RAM because SO has to paginate, but with a smaller replay memory the same thing happens. Another thing that I thought it could be is the VRAM, but at least TensorFlow should not use RAM (The code is in PyTorch, but I think is kind of the same).

I wonder if with a better PC part I will see a significant improvement (various magnitudes) because there are something (like RAM or VRAM) that made the CPU work harder than it should. Or the CPU is going to be always the really bottleneck and if I don't but a Threadripper I am not going to see a real improvement.
I'm not an expert, but I do run a fairly beefy system. 2x RTX 2080Ti, i7 7820x, 96gb ram. I run a YouTube channel and teach some Udemy courses on reinforcement learning. Here's what I've found.

You have a couple things going on: the environment simulations are running on the CPU and then shuttling the data to the GPU for the calculations. The slowest piece of hardware is generally going to be the limiting factor, and I'll tell you how to figure out which in a minute.

Memory:

The replay memory lives in your RAM, while the neural network is going to live on your vram.

If you're doing convolutions, it's going to scale with the number of filters (and the screen image sizes you're trying to process), so vram usage goes up quickly as the number of CNN layers / size of screen images / number of filters increases.

You can mitigate the RAM usage, to some extent, by using numpy arrays and carefully choosing the data types you use for your replay memory. You don't need int64 to store actions, for instance. You probably don't need fp64 to store your screen images, either (assuming you're normalizing between 0 and 1). Your terminal flags can be stored as boolean, and this makes setting the Q for the terminal states trivial:
Q_value_for_state_s_t+1[dones] = 0

Processor:
I haven't implemented Rainbow, but if it is like regular DQN In the sense that it's running a single environment at a time, then increasing the # of threads isn't going to help. Your performance will be dominated by the single threaded performance of your processor. Intel is generally ahead in that regard, though I would argue that the better multithreaded performance of AMD (in nearly any other task) is going to be an improvement in general system usage. I wouldn't go with threadripper. I would go with the fastest Ryzen I could afford, as 12-16 threads is going to usually be enough and you'll have some thermal headroom such that your system won't heat up your entire room (my office is easily 5 degrees hotter than anywhere else in the house).

GPU:
You're correct that the neural network sizes are pretty small and don't generally max out your GPU. This changes if you're using convolutions on large batches of buffered screen images, but for a simple neural net it's going to be pretty easy on the hardware.

I didn't see significant changes in run times (maybe a 15-20% improvement) going from a 1080Ti to a 2080Ti, which is a better metric than the utilization of your gpu. I saw a much bigger improvement going from a GTX780 to my 1080Ti. In hindsight, I wish I had bought 2 due to the run up in prices after the crypto mania.

The biggest benefit you can get is to run 2 reasonably fast gpus, with each running a different set of agent hyperparameters. This lets you double the speed of the search of the parameter space, and will ultimately save you more time than a single top tier gpu. You can even run different algorithms (i.e. rainbow and D3QN) to compare performance.

Power supply: not something you mentioned, but this is the single most critical component of the system. If you don't supply adequate current on the 12V rails you risk frying your hardware. Buy Corsair. They make high end power supplies at a reasonable price point. I would also advise paying extra for the fully modular ones, as they make cable management a breeze (important for thermal management).

Cooling: Also quite critical. Modern cpus and gpus are set up to run at the max frequency their thermal management will allow. Better cooling means better performance. Pay the extra money for the all in 1 liquid CPU coolers and set up a regular schedule for cleaning out the dust bunnies. Make sure to set the CPU cooler fans up to exhaust the hot air out of the case.

So how do you know what the limiting factor in your rig is?

You're setting up your code something like this:
Edit: sorry for the poor formatting. The forum software removes the 4 space indent.

while episode is not done:
choose_action(state)
new state, reward, done, info = env.step(action)
store_transition_in_memory(state, new_state, reward, action, done)
state = new_state
call_learning_function_for_agent()
print_episode_debug_info_to_terminal

You can figure out where the bottleneck is by adding in:

import time
inference_time = 0
train_time = 0
env_time = 0

while episode is not done:
inference_start = time.time()
choose_action(state)
inference_time += time.time() - inference_start
env_start = time.time()
new state, reward, done, info = env.step(action)
env_time += time.time() - env_start
store_transition_in_memory(state, new_state, reward, action, done)
state = new_state
train_start = time.time()
call_learning_function_for_agent()
train_time += time.time() - train_start
print_episode_debug_info_to_terminal
print_times_to_terminal_after_1000_episodes

This will give you some clue as to where the bottleneck is. If the sum of the inference and train time is greater than the env time, then your gpus are taking up most of the execution time and an upgrade could benefit you (up to a point).

Hope that helps!
 

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This will give you some clue as to where the bottleneck is. If the sum of the inference and train time is greater than the env time, then your gpus are taking up most of the execution time and an upgrade could benefit you (up to a point).

Hope that helps!
That's a really good answer, thanks Phil. I know your channel and I followed some of your tutorial in the past, also your video about how to spec a deep learning PC.

I run the code, and the inference and train time is greater that the env time (specially the train time), so the logic dictate that I should upgrade the GPU. But the task manager shows the CPU is at 100% all the time. So I don't get it, if I buy a better GPU the CPU is not going to "keep the pace" and I am not going to see any improvement. I am missing something here?

By the way, you set-up is amazing.
 

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That's a really good answer, thanks Phil. I know your channel and I followed some of your tutorial in the past, also your video about how to spec a deep learning PC.

I run the code, and the inference and train time is greater that the env time (specially the train time), so the logic dictate that I should upgrade the GPU. But the task manager shows the CPU is at 100% all the time. So I don't get it, if I buy a better GPU the CPU is not going to "keep the pace" and I am not going to see any improvement. I am missing something here?

By the way, you set-up is amazing.
HMMMM.

Yeah, the cpu could very well bottleneck a faster GPU. You see this quite often in gaming. It looks like you're running a haswell CPU, so just about any modern cpu is going to be faster.

Do you know how to overclock? If you have the thermal headroom you could crank up the cpu frequency and see if it helps. If it does, there's your answer.
 

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HMMMM.

Yeah, the cpu could very well bottleneck a faster GPU. You see this quite often in gaming. It looks like you're running a haswell CPU, so just about any modern cpu is going to be faster.

Do you know how to overclock? If you have the thermal headroom you could crank up the cpu frequency and see if it helps. If it does, there's your answer.
Is the normal (not K version) so it is locked to overclock. But what I try was to run the code in an Apple Mac Mini with a newer I5 with 6 cores, and it was slower. Of course in the training part was because the lack of a discrete GPU, but also at the beginning while it was filling the replay memory.

So I guest is the GPU my first problem right now. At the end I am going to upgrade everything, because I could use a bigger buffer too. And upgrade a DDR3 it not worth the money, there are much expensive than DDR4.
 

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Is the normal (not K version) so it is locked to overclock. But what I try was to run the code in an Apple Mac Mini with a newer I5 with 6 cores, and it was slower. Of course in the training part was because the lack of a discrete GPU, but also at the beginning while it was filling the replay memory.

So I guest is the GPU my first problem right now. At the end I am going to upgrade everything, because I could use a bigger buffer too. And upgrade a DDR3 it not worth the money, there are much expensive than DDR4.
The laptop cpu is probably running at a significantly lower frequency. It's pretty hard to compare across generations of CPU, as you get architectural enhancements. Those apple laptops are also pretty bad for thermal management, so you were probably throttling the cpu. Who knows.

I'd say a 7 year old cpu warrants an upgrade. I would look for used parts on ebay, if cash is tight.
 

Martin.G

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The laptop cpu is probably running at a significantly lower frequency. It's pretty hard to compare across generations of CPU, as you get architectural enhancements. Those apple laptops are also pretty bad for thermal management, so you were probably throttling the cpu. Who knows.

I'd say a 7 year old cpu warrants an upgrade. I would look for used parts on ebay, if cash is tight.
It's a Mac mini, so it's not the laptop version. I am going to try to install Ubuntu because the code is running Windows. I read that it can improve performance https://github.com/pytorch/pytorch/issues/22083

My goal is to upgrade everything, but in Argentina is kind expensive and difficult to get pc parts. For example, if I decide to upgrade a GPU I don't know if I should wait the new 3000 serie in September or buy a 2000 now, because prices and stock can change drastically in a week or month.

Edit:
With Ubuntu I have an improvement of almost 2x and the CPU utilization is 150% of 400%. Also the GPU jump to 80-90% utilization.
 
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Lithuania
So guys where do you see industry going in the upcoming years? Is it worth to dig deep into AI/Data Science if you will want to create something (considering you aren't a huge company like Google, Amazon etc.) and you're building from a ground up. Or is it better to go more into creating hardware/IOT stuff that will be needed for computation and also to gather data? If you would decide now to study one of them, which one you would go to? :)
 

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