Machine Learning Projects (Intermediate level) | 2021
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In this video, I'll recommend you some intermediate-level machine learning projects.

You'll get some:
✔️ intermediate level ML project ideas

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✅ NST (optimization): https://github.com/gordicaleksa/pytorch-neural-style-transfer
✅ NST (neural net): https://github.com/gordicaleksa/pytorch-nst-feedforward
✅ DeepDream: https://github.com/gordicaleksa/pytorch-deepdream
✅ GANs: https://github.com/gordicaleksa/pytorch-gans

All of the papers and blogs are linked in READMEs of the above projects!
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⌚️ Timetable: 
0:00 Intro: what will you learn through these projects?
1:30 Project: Neural Style Transfer (optimization)
3:20 Project: Neural Style Transfer (feed-forward)
4:19 Support me on Patreon
4:34 Project: Deep Dream
6:36 Project: GANs
8:38 How should you approach these?

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#machinelearning #machinelearningprojects #deeplearning
 What's up folks? Got myself a new microphone here, so hopefully the audio will get better. I still didn't have the time to buy the rest of the equipment, but it will come soon. Hopefully the audio will be much better in this video. So in the last video I showed you some beginner-friendly machine learning projects, which you could do to get yourself started with machine learning. And they were beginner-friendly in the sense that you can basically write the MNIST classifier in about 50 lines of code. And you can find a bunch of resources out there which show you how to solve those problems. And I'll link the video in the card somewhere here, I hope. In this video I'm going to recommend you some more advanced projects. I kind of label them as intermediate-level machine learning projects. And the good thing about these is that I actually have all of them implemented on my GitHub. So that's going to help you kickstart and do all of these without a problem. So I'm a big fan of learning by doing, and by coding these from scratch, you're going to learn so much more about the deep learning framework of your choice. And there will be a PyTorch if you're following along my GitHub projects. And aside from the framework, you're obviously going to learn a lot more about deep learning by just digging into the project, coding the neural networks from scratch, and everything else that goes along. Without further ado, let's jump into the project. So the first project I'm going to recommend you do is Neural Style Transfer. And I've actually got a whole series on Neural Style Transfer. I'll link the videos somewhere here and also down below in the description. Jumping straight into the project on my GitHub here, it's called PyTorch Neural Style Transfer. And if you open it up, you'll see how it looks like. And so just let me reiterate once more for those of you who don't know this. So Neural Style Transfer is basically about combining this content image with the style image and getting a stylized image out, as you can see here on the screen. So this colorized image on the left is the stylized image you get from the NST algorithm. And you can see a bunch of beautiful examples that the algorithm can create. And this is one of the reasons I recommend you starting with this project is because it's highly visual and most people just like seeing the things they do visualized. So it's a nice intermediate project to start with. You're going to have a paradigm shift using this project. And the reason being is that you usually optimize the weights of the neural network. And here in this project, you're actually going to tune the input pixels so that you get these amazing imagery, as you can see on the screen here. So aside from the paradigm shift there, you'll learn how to match the feature maps of input imagery with the feature maps of the content image and also the grand matrix of the style image. And if all of that sounds gibberish to you now, that's totally fine. So you'll learn that stuff. So if you don't know it right now, it's okay. And the second project after we finish this one is also my GitHub here. It's the PyTorch Neural Style Transfer feed forward. If we open it up here, it's super similar to this one. The difference being is that you're actually training a neural network here. So in the previous one, we're doing optimization procedure. We got those imagery in here. You just pass your content image into the neural network. And it just kind of stylizes the image into the style that the network was trained for. You're not going to tweak the input pixels. You're going to tweak the actual weights of the neural network. Doing this project is going to teach you how to build your neural networks from scratch and how to set up the whole training procedure and pretty much the whole pipeline. So if you're having any problems whatsoever starting with these projects, please write the comments down in the comment section. I'll make sure to address all of them. And also feel free to open up issues on GitHub. I'll be monitoring those also. One more thing, I opened up a Patreon account recently. So if you want to support me and become a member of this growing community, consider becoming my Patreon here. And you'll get early access to my content and a bunch of other perks. So the second project is going to be Deep Dream. And it's a really interesting algorithm developed by this guy called Alexander Morvintsev while he was back in Google. And he kind of woke up during the night, had a nightmare, and came up with this thing. And you can see how it looks like. You get this psychedelic looking imagery here. So you take some whatever image as the input, like say these figures here. And after passing it through the algorithm, you get these psychedelic ones. Even this one on the top left actually came from this image here. So it's super exciting. How it works is you basically take some images as the input, pass it into the pre-trained neural network. And that neural network will kind of create something called feature maps. And you'll want to amplify whatever the network already sees in the image. So because most of them were trained on like dog images, you'll see how the network is slowly modifying the image. It's going to add some like dog eyes and cat eyes and fur and stuff like that because that was the data that the image was trained on. So you're going to learn how to actually do, manually do gradient steps, so optimization steps. You're going to use the raw gradients that you get from maximizing those feature maps. And you're going to apply them to these input pixels. And that's how you're going to get these images. So there are a lot of more things you can play with, as you'll see when you open up this ReadMe, depending on which layers you use to do this optimization, you're going to get different patterns. Like if you go to lower layers, you'll get these geometric patterns. And the higher up you go, the deeper you go actually. You'll get more like abstract imagery like these here. And yeah, so you can also create GIFs. Pretty much fun stuff. And last but not least, generative adversarial networks or GANs for short. And if you haven't heard of GANs so far, which I doubt, they're just a framework where we have two neural networks. One is called the generator, the other one is called the discriminator. And the goal of the generator is to create imagery undistinguishable from the real data. And you can see here in this, so I basically have three GAN projects inside of this repo. And this original one invented by Ian Goodfellow, I used MNIST digits, so the same as in the beginner video, the same data set. And basically you can see this row here that's real imagery from MNIST. And after the generator is trained, it can generate data that looks like somebody could have written those. Like, looks totally real. You couldn't probably distinguish between this row and this one here. And aside from that one, I also have this conditional GAN, where you can additionally control which class you want to generate. So you can condition it and say, hey, I want a zero, and we just generate zeros. You can see in the column here. Finally, this deep convolutional GAN or DC GAN for short is super famous architecture. And I've just picked a different data set here. You can also use MNIST, but I've picked this Celeb A data set of human faces. And you can see how it's learning to generate faces of humans. And this is how it looks like after the generator is trained. Obviously not undistinguishable from the real data, but this was a model that was developed, I think, in 2016. So you have much better models now, state of the art, like StyleGAN version 2. And I've also developed this Jupyter Notebook, which you can use to better understand how this all works. And hopefully, you'll find that useful. A couple of tips on how you should approach these projects. The first MNIST project, I just recommend you go ahead and read the original paper by Gatiss. And it's not super tough, even if you're intermediate, since you're watching this video. But even if you're kind of a beginner, go ahead and try and give it a try. As for Deep Dream, they have a blog that they wrote a couple of years ago, like Crisola and Alexander Mertvinsev. And you should go ahead and read that one before you go ahead and try and implement that code yourself. Also, just feel free to go ahead and examine other people's code, including mine, obviously. And that will help you develop your own version. But try and develop the core parts by yourself. Don't just copy-paste code. That beats the purpose, because you want to learn here. And when it comes to GANs, I definitely do not recommend you go ahead and try and read the original paper. It's super tough. Lots of math, unless you're familiar with mathematics for machine learning, just skip that one. Go ahead and find useful blogs online. There are many GAN blogs out there, and that will help you understand the basic logic. And then the most important thing here is that you try and implement the training loop yourself. That's where the whole brain is. Okay, that was pretty much it. Going forward, I'll be developing some even more advanced projects like graph neural networks and transformers. You can expect a video in a couple of months, me suggesting some projects you should do yourself, which are for more advanced machine learning practitioners. I'd love to hear your thoughts on these three projects. Do you have any project which you think I should have included here? Just let me know your thoughts down in the comment section. If you're new to this channel, consider subscribing and hit that bell icon to get notified when I upload a new video. Until next time, keep learning deep.
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So if you don't know it right now, it's okay."}, {"start": 197.0, "end": 203.0, "text": " And the second project after we finish this one is also my GitHub here."}, {"start": 203.0, "end": 206.0, "text": " It's the PyTorch Neural Style Transfer feed forward."}, {"start": 206.0, "end": 211.0, "text": " If we open it up here, it's super similar to this one."}, {"start": 211.0, "end": 215.0, "text": " The difference being is that you're actually training a neural network here."}, {"start": 215.0, "end": 217.0, "text": " So in the previous one, we're doing optimization procedure."}, {"start": 217.0, "end": 219.0, "text": " We got those imagery in here."}, {"start": 219.0, "end": 222.0, "text": " You just pass your content image into the neural network."}, {"start": 222.0, "end": 228.0, "text": " And it just kind of stylizes the image into the style that the network was trained for."}, {"start": 228.0, "end": 231.0, "text": " You're not going to tweak the input pixels."}, {"start": 231.0, "end": 233.0, "text": " You're going to tweak the actual weights of the neural network."}, {"start": 233.0, "end": 239.0, "text": " Doing this project is going to teach you how to build your neural networks from scratch"}, {"start": 239.0, "end": 244.0, "text": " and how to set up the whole training procedure and pretty much the whole pipeline."}, {"start": 244.0, "end": 248.0, "text": " So if you're having any problems whatsoever starting with these projects,"}, {"start": 248.0, "end": 251.0, "text": " please write the comments down in the comment section."}, {"start": 251.0, "end": 253.0, "text": " I'll make sure to address all of them."}, {"start": 253.0, "end": 256.0, "text": " And also feel free to open up issues on GitHub."}, {"start": 256.0, "end": 259.0, "text": " I'll be monitoring those also."}, {"start": 259.0, "end": 262.0, "text": " One more thing, I opened up a Patreon account recently."}, {"start": 262.0, "end": 267.0, "text": " So if you want to support me and become a member of this growing community,"}, {"start": 267.0, "end": 270.0, "text": " consider becoming my Patreon here."}, {"start": 270.0, "end": 275.0, "text": " And you'll get early access to my content and a bunch of other perks."}, {"start": 275.0, "end": 279.0, "text": " So the second project is going to be Deep Dream."}, {"start": 279.0, "end": 284.0, "text": " And it's a really interesting algorithm developed by this guy called Alexander Morvintsev"}, {"start": 284.0, "end": 286.0, "text": " while he was back in Google."}, {"start": 286.0, "end": 290.0, "text": " And he kind of woke up during the night, had a nightmare, and came up with this thing."}, {"start": 290.0, "end": 292.0, "text": " And you can see how it looks like."}, {"start": 292.0, "end": 295.0, "text": " You get this psychedelic looking imagery here."}, {"start": 295.0, "end": 299.0, "text": " So you take some whatever image as the input, like say these figures here."}, {"start": 299.0, "end": 304.0, "text": " And after 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that"}, {"start": 342.0, "end": 345.0, "text": " because that was the data that the image was trained on."}, {"start": 345.0, "end": 352.0, "text": " So you're going to learn how to actually do, manually do gradient steps,"}, {"start": 352.0, "end": 354.0, "text": " so optimization steps."}, {"start": 354.0, "end": 359.0, "text": " You're going to use the raw gradients that you get from maximizing those feature maps."}, {"start": 359.0, "end": 362.0, "text": " And you're going to apply them to these input pixels."}, {"start": 362.0, "end": 365.0, "text": " And that's how you're going to get these images."}, {"start": 365.0, "end": 368.0, "text": " So there are a lot of more things you can play with,"}, {"start": 368.0, "end": 371.0, "text": " as you'll see when you open up this ReadMe,"}, {"start": 371.0, "end": 376.0, "text": " depending on which layers you use to do this optimization,"}, {"start": 376.0, "end": 378.0, "text": " you're going to get different patterns."}, {"start": 378.0, "end": 382.0, "text": " Like if you go to lower layers, you'll get these geometric patterns."}, {"start": 382.0, "end": 386.0, "text": " And the higher up you go, the deeper you go actually."}, {"start": 386.0, "end": 390.0, "text": " You'll get more like abstract imagery like these here."}, {"start": 390.0, "end": 394.0, "text": " And yeah, so you can also create GIFs."}, {"start": 394.0, "end": 396.0, "text": " Pretty much fun stuff."}, {"start": 396.0, "end": 402.0, "text": " And last but not least, generative adversarial networks or GANs for short."}, {"start": 402.0, "end": 406.0, "text": " And if you haven't heard of GANs so far, which I doubt,"}, {"start": 406.0, "end": 409.0, "text": " they're just a framework where we have two neural networks."}, {"start": 409.0, "end": 412.0, "text": " One is called the generator, the other one is called the discriminator."}, {"start": 412.0, "end": 417.0, "text": " And the goal of the generator is to create imagery undistinguishable from the real data."}, {"start": 417.0, "end": 423.0, "text": " And you can see here in this, so I basically have three GAN projects inside of this repo."}, {"start": 423.0, "end": 428.0, "text": " And this original one invented by Ian Goodfellow,"}, {"start": 428.0, "end": 433.0, "text": " I used MNIST digits, so the same as in the beginner video, the same data set."}, {"start": 433.0, "end": 438.0, "text": " And basically you can see this row here that's real imagery from MNIST."}, {"start": 438.0, "end": 444.0, "text": " And after the generator is trained, it can generate data that looks like somebody could have written those."}, {"start": 444.0, "end": 447.0, "text": " Like, looks totally real."}, {"start": 447.0, "end": 451.0, "text": " You couldn't probably distinguish between this row and this one here."}, {"start": 451.0, "end": 457.0, "text": " And aside from that one, I also have this conditional GAN,"}, {"start": 457.0, "end": 460.0, "text": " where you can additionally control which class you want to generate."}, {"start": 460.0, "end": 466.0, "text": " So you can condition it and say, hey, I want a zero, and we just generate zeros."}, {"start": 466.0, "end": 468.0, "text": " You can see in the column here."}, {"start": 468.0, "end": 476.0, "text": " Finally, this deep convolutional GAN or DC GAN for short is super famous architecture."}, {"start": 476.0, "end": 479.0, "text": " And I've just picked a different data set here."}, {"start": 479.0, "end": 485.0, "text": " You can also use MNIST, but I've picked this Celeb A data set of human faces."}, {"start": 485.0, "end": 492.0, "text": " And you can see how it's learning to generate faces of humans."}, {"start": 492.0, "end": 495.0, "text": " And this is how it looks like after the generator is trained."}, {"start": 495.0, "end": 502.0, "text": " Obviously not undistinguishable from the real data, but this was a model that was developed, I think, in 2016."}, {"start": 502.0, "end": 508.0, "text": " So you have much better models now, state of the art, like StyleGAN version 2."}, {"start": 508.0, "end": 516.0, "text": " And I've also developed this Jupyter Notebook, which you can use to better understand how this all works."}, {"start": 516.0, "end": 520.0, "text": " And hopefully, you'll find that useful."}, {"start": 520.0, "end": 524.0, "text": " A couple of tips on how you should approach these projects."}, {"start": 524.0, "end": 530.0, "text": " The first MNIST project, I just recommend you go ahead and read the original paper by Gatiss."}, {"start": 530.0, "end": 536.0, "text": " And it's not super tough, even if you're intermediate, since you're watching this video."}, {"start": 536.0, "end": 541.0, "text": " But even if you're kind of a beginner, go ahead and try and give it a try."}, {"start": 541.0, "end": 552.0, "text": " As for Deep Dream, they have a blog that they wrote a couple of years ago, like Crisola and Alexander Mertvinsev."}, {"start": 552.0, "end": 559.0, "text": " And you should go ahead and read that one before you go ahead and try and implement that code yourself."}, {"start": 559.0, "end": 566.0, "text": " Also, just feel free to go ahead and examine other people's code, including mine, obviously."}, {"start": 566.0, "end": 569.0, "text": " And that will help you develop your own version."}, {"start": 569.0, "end": 573.0, "text": " But try and develop the core parts by yourself."}, {"start": 573.0, "end": 575.0, "text": " Don't just copy-paste code."}, {"start": 575.0, "end": 579.0, "text": " That beats the purpose, because you want to learn here."}, {"start": 579.0, "end": 585.0, "text": " And when it comes to GANs, I definitely do not recommend you go ahead and try and read the original paper."}, {"start": 585.0, "end": 586.0, "text": " It's super tough."}, {"start": 586.0, "end": 591.0, "text": " Lots of math, unless you're familiar with mathematics for machine learning, just skip that one."}, {"start": 591.0, "end": 594.0, "text": " Go ahead and find useful blogs online."}, {"start": 594.0, "end": 600.0, "text": " There are many GAN blogs out there, and that will help you understand the basic logic."}, {"start": 600.0, "end": 607.0, "text": " And then the most important thing here is that you try and implement the training loop yourself."}, {"start": 607.0, "end": 609.0, "text": " That's where the whole brain is."}, {"start": 609.0, "end": 611.0, "text": " Okay, that was pretty much it."}, {"start": 611.0, "end": 618.0, "text": " Going forward, I'll be developing some even more advanced projects like graph neural networks and transformers."}, {"start": 618.0, "end": 624.0, "text": " You can expect a video in a couple of months, me suggesting some projects you should do yourself,"}, {"start": 624.0, "end": 628.0, "text": " which are for more advanced machine learning practitioners."}, {"start": 628.0, "end": 632.0, "text": " I'd love to hear your thoughts on these three projects."}, {"start": 632.0, "end": 637.0, "text": " Do you have any project which you think I should have included here?"}, {"start": 637.0, "end": 640.0, "text": " Just let me know your thoughts down in the comment section."}, {"start": 640.0, "end": 647.0, "text": " If you're new to this channel, consider subscribing and hit that bell icon to get notified when I upload a new video."}, {"start": 647.0, "end": 670.0, "text": " Until next time, keep learning deep."}]