is simply going to be a policy, like a single shot policy without any Monte Carlo tree expansion and


Dream to Control: Learning Behaviors by Latent Imagination

so on. But what it wants to do is it wants to learn this policy, and then it will then


be a policy that is not going to be able to do anything. So it's not going to be able to do


anything. But what it wants to do is it wants to learn this policy, not by interacting in the real


world, like here on the left, but actually by interacting only in the dream world right here.


So dreams are an accurate representation of the real world, right? We already saw this in a paper


called World Models by Jürgen Schmidhuber, I believe. And in that paper, what they did was they


first collected experience, such like experience like this one, and then they learned from the one


observation to predict the next ones. And I built or to predict the next hidden states, right?


They did so by basically moving in the world at random. So they have this little spider thingy.


And they just do random movements, right? They randomly move around. And thus, they collect these


trajectories, and then they learn from the random trajectories. The difference that this paper does


is it does these steps iteratively. So it will not learn from random policy, but it will actually,


first, yeah, it'll start out learning this random, learning a good policy for its environment model,


then acting, going back and using that policy in order to learn a better environment model,


and then again, learn using the better environment model in order to learn a better policy.


If this wasn't clear enough, we'll jump to the algorithm. The algorithm isn't actually too


too complicated. It's, as I said, it's, it's, I think it's a relatively minor iteration on previous


research, but it appears to work and it works in these kind of continuous control tasks. So you see


you have three models here that you need to learn. And that's what you see over here. There is


representation, transition and reward. And you'll see they all have the same parameters. So that


gives you an indication that these things are a single model. Now what are what is the model


representation, transition and reward. So let me this this is the thing on the left here.


In this part of the algorithm, you assume that you have a policy, you already know what action


you do, or you can even assume that you have some experience, right? You have your agent is running


with a given policy. And you simply collect that. And now you're trying to learn. So let me scratch


all of this. What do you have given? Given is the observation sequence and the actions you took,


right? And the rewards you got, that's also given. So each action gives you reward, right? So these


things are, are given provided to you. And now what do you want to learn? You want to learn a


representation and a transition. And let's say a reward. So you also want to predict the next


the next reward, this thing, this thing, right? So as we already said, you can do this by encoding


the state using, for example, a CNN, and then using an LSTM in order to incorporate this over


time. So what you learn is the transition from one hidden state to the next hidden state. And you


also learn the how the observation goes into the hidden state. And thirdly, you learn that if I'm


in this hidden state, and I take this particular action, I will get this reward in the future.


Right? You can learn this from just a set of pre computed or from a set of experience that you have


in your, let's say, your replay buffer. Right? This is one model. And you learn this here in


this first step in this called dynamics learning section, right? So you see while not converged.


So you do dynamics learning, you draw data sequences from your experience, right? Then


you compute the model states, these are the hidden states. And then you update this parameter


theta using representation learning. Now, they don't really specify what representation learning


is, but they do give examples of what you can do. I think their point is whatever you need to do


you need to do in order to learn this representation. And one example is actually drawn here.


One example is you can learn a model that reconstructs the next state, or actually,


sorry, reconstructs the same state. So you can learn a model that predicts. So if you give


the observation as an input, it goes through the hidden state, you can learn a decoder that


reconstructs that observation. This is usually done in things like variational auto encoders


in order to produce generative models. So the this part here would be the generator. And that


would be kind of the thing of interest if you're doing a variational auto encoder. But of course,


here, our quantity of interest is this, this encoder model, because we want a good representation of


the state. And but but it comes down to the same thing. If you can learn a model that learns to


accurately reconstruct the observation, then your representation here in the middle is probably an


informative one, right? Because you learn the same model across multiple observations. That means it


can accurately encode what makes one observation different from another one. Right, so this is how


you learn the theta parameters, right? Now, the other models here are the action and the value


parameters. And this is here in the step called behavior learning. So in the behavior learning,


what they say is, imagine trajectories from each of the states that you have. So what you're going


to do is, from each of the observations here, you're going to obtain the hidden states, right,


this these hidden states. Now from each of the hidden states here, so here is an observation


from its hidden state, you're going to use the model that you learned here through the LSTM.


Sorry. Well, this is terrible. Through the LSTM, you're going to use that model to imagine future


trajectories, right of hidden states. So you have given sorry, given are now is the observation here,


and the hidden state, and you're going to imagine future hidden states, you're also going to imagine


future rewards, right. And you are going to use your your policy kind of to, you're going to use


your policy in order to determine which actions you're going to take, right. And the ultimate goal


here is to learn a good policy. So a policy that will give you better rewards in the future, as you


would do. So this is regular reinforcement learning, except that the difference is, in regular


reinforcement learning, I have my observation, I encode it, and then I determine what action I want


to take, then I feed that action back into the environment, which would give me the next observation.


And then I'd use that to determine maybe in conjunction with the last hidden state, the next


action. In this thing, since we learned a dynamics model of the hidden states, we can simply determine


the action, and then simply compute what the probable next hidden state is going to be. And


then use that to determine an action again, and so on. So there's no need to go through the


environment, which means potentially, we can learn much, much faster without having to expensively


interact with the environment. So that allows us to basically, also, these models here, they might


be quite large. So our back prop now only needs to happen through this path, basically, if we want


to, or through through this path here, in case we have discrete actions. Yes, so that's in that will


be the dynamics learning is down here. So you can see, we predict the rewards and the values and


compute value estimates, and then we update these parameters using so what we have is here a value


function. See, the value function is dependent on this psi here. And this we update using a gradient


of its output minus the true value. So this, this here is an estimate of the value. And as you know,


a value function is supposed to tell you the complete future reward given a state, right. And


it's important for us that we have a function that can estimate that. Because, of course, then we can


take actions, if we can make this function go high, and this is an accurate function, that means we


get a lot of reward in the future, right. So it's important to learn this function. And here you can


see we adjusted into the direction of matching this quantity better. Now we'll get to this quantity in


a second. You can also see we update this parameter, which is the action model. So here you see that


the action model depends on this. This is this is our policy, right? This thing here determines


which action we take, and we update it into the direction, this is a gradient with respect to this


value function, right. So we train the policy to maximize the value, which is all the future rewards


that we get. Of course, we can do this, because we can now back propagate through all of these


time steps. Because we have this, we have this transition model, we can back propagate through


all of this, which is pretty cool. I think, in my opinion, the the kind of workhorse of this paper


might be this quantity here. So what how exactly do you compute the value of a state? Especially


in these continuous control tasks, you sometimes have a lot of steps. So this, these trajectories,


might be pretty long. And they might be longer than what you can back propagate here reasonably,


from from time step to time step, right? Even an LSTM might only be able to back prop through,


let's say a couple of dozen, or maybe a few hundred steps in time. And maybe you have longer


trajectories here. So it's pretty, I think the this value estimate here is a main component of


extending that range. So they say this is according to equation six. And this is what it what it does.


Again, this is my opinion that this here is kind of the workhorse of the of the method. So it's a


three step process, actually. It's pretty, pretty heavy. So you see, this is the quantity they


estimate with the value function, it is it is set between an average over so h is the time horizon,


right? That you're looking for, it is set between these two things across the sum over the time


horizon. Now, each of those things, again, here is a sum over this tau, this tau here, which is


this tau, and the and h minus one. And h here is the minimum of tau plus k and tau plus horizon. So


this goes, this looks this this quantity looks k steps into the future. So for each step to the


horizon, we look k steps into the future. And and for each step, we look into the future, we sum


again, across these quantities here. And these quantities here, what is that? It's a mixture of


the reward you get in that particular step, plus your own your estimate of the value function at


the at the horizon step, discounted by that. So it's a pretty safe, you have like a time number


of steps that you took, and each time you get a reward, right? This is a very complicated way of


summing of going into the future, summing up the rewards, going more steps, summing up the rewards


again, in different fashion, and then mixing these, these individual quantities. So this one,


this one, this one that you got from accumulating all of these in a weird fashion. And that allows


you to look way beyond, especially, you see here, your estimate of the value function will actually


include your own value function that again, probably looks into the future. So what you


accumulate from the last step in your time horizon already includes information from all the future


steps. Because you take your own value estimate into account. This is, I think it's very convoluted.


But again, I think this, this is this complicated value estimate allows you to, to, to have a better


value estimate far into the future. They do show some, some kind of samples here of what they can


do. I haven't found any videos of it, unfortunately, but it appears to work pretty well. They have a


discussion of different representation learning methods and different experiments and ablations


My Biggest Challenge With Programming (Q&A #11)

What's going on everybody and welcome to another Q&A video


I get this is like the top requested type of video that I do


And the funny part is I get more requests to do Q&A videos than I get actual


Questions that I would answer in a Q&A video


So I can't really do Q&A videos without the Q so if you've got some questions feel free to leave them below


Otherwise I'm going to answer some of the questions that I have in it from a community question that I asked basically just


youtube.com slash centex slash community


There's a place there


I can just post stuff so some people have asked some questions there if you've got further ones


Or if you asked a question there, and I'm not getting to it here


It's just because I can't answer every single question in one video


And I try to use do some that were somewhat related to each other so anyway


Let's jump into it so the first question here is


What is the most challenging problem you've ever come across in coding and?


How do you solve the seemingly unsolvable? I'm going to assume that's not an assertion that I actually solve the unsolvable


I'm going to assume you mean just how do I solve hard problems because I?


Actually don't really solve that hard of problems typically so


Probably the most challenging problem in programming is not necessarily like any programming


concept or something to learn I think the most challenging thing is


Is like technical debt, but not just technical debt like it's it's sort of avoiding technical debt


But it's this balance you always have to strike between


writing


The most efficient code possible, but also just get the thing done like just actually make the thing like a lot of people


I think focus too much on trying to write something to be super efficient, but yet. It's not readable right so


writing efficient code actually getting it done and


Then like maybe it's a triangle. We'll see if I think of any other answer here, but so getting it done


Writing the writing good efficient nice quality code, but then also writing code that can be


Built upon moving forward and not just moving forward today and tomorrow and the next day


But like in a year from now when you go back to revisit your code


Are you going to understand these lines that you wrote so a lot of times like on stack overflow like certain question answers they?


Get posted there the code is really cool. It's super efficient


It runs super fast


But if you didn't know that that code wasn't an answer to the question above you would at first glance be like what the heck?


Is that like it's like the nastiest code ever?


But it solves the problem


Really quick and that seems to be what most people go for is like how can we solve this problem in as few lines as possible?


And that's not always the answer so anyways getting back to the question. I honestly I think the biggest problem is just


Projects growing over time because it's really easy to come up with a really novel and fancy answer really quick


It's also really easy to write really bad code that is not going to be modular


And you're going to have a really hard time building on it moving forward and write completely illegible code that sort of thing


I think that's the biggest problem. I think it's just


Some sort of project that you're working on growing it over time and and and having that kind of foresight as you start working on


That sort of a problem


Because a lot of times you just at least for me everyone's a little different


I definitely think there's I don't know who at first came up with the there's makers, and then there's menders


Concept with programming, but I definitely think it's true


I'm sure each of those has their own little category, and I'm sure some people are going to assert that they're both things or whatever


But I definitely tend to see there are two different types of programmers people who just make stuff really quickly and are really good at like


Prototyping and just getting this sort of MVP type thing out and then there's the menders who come in and can actually keep a project


Going long term. I like to think of myself as more of a maker


So maybe that's why I think that's the biggest problem is because I'm not good at it. I just I'm really good at just like


Bopping out a really quick project solving a problem, but then over time. I'm not very good


At keeping a project growing so anyways I think I answered that question


Next question here from Max what programming project years are you most proud of how did I ask a few questions apparently?


How'd you get into ML and what's your favorite type of programming to I'm not really sure what you mean by type of programming?


To do there, but probably the most I mean obviously


I think I'd have to go with the GTA 5 series as far as like what I'm it's probably one of the larger


Projects that I worked on that I was you know kind of pouring into the previous question


that


Was a project that I had at least enough foresight


I mean, it's definitely a mess in the current state that it's in but that was at least a project that I knew kind of


Like okay, I need this to be its own separate little module, and I need this and this and so I did pretty good


at actually making it happen, but also


Continuing to work on that that was probably other than like python programming at net and like syntax calm or something that project though


And probably more so than python program that I put more


continuous hours like more time spent


Continually like building on top of it over time than I have on any other project


So I guess I'm the most proud of that for the fact that I was able to do that and not just be like I quit


so


Anyway, so yeah, probably that how did I get into ML?


It just kind of I just sort of like eased into it honestly like I got into ML


Before there was like I mean deep learning was a thing and like neural networks were a thing


But they weren't big right they didn't really start getting big until I


Don't know maybe like 2015 and then onward. I mean, I'm sure they were they existed like something


I was in a neural networks in 2012. I'm sure you were that's great. That's cool


But really they didn't really get big until a little bit after probably 20 sometime like 2015 and on I think anyways


So really I was just interested


I just found it just awesome like with scikit-learn the things that you could do with just really basic


ML algorithms, and I think it's unfortunate most people or many people don't really care to learn those anymore because we have deep learning and everyone


thinks deep learning solves everything


But if you've if you've ever done anything with like scikit-learn and just like k nearest neighbors k nearest neighbors is the best algorithm ever


And it's so basic, but it can it can be you know parallel processed to you know however many processors


You have and it can solve awesome problems


Similar problems to what you would do with neural networks and stuff so anyways not what you're asking about


But yeah, just I found it really fascinating. I just think like these like really pretty darn simple algorithms that can do really cool things


Yeah, and one of the things I'd really like to get into that


I've just never gotten around to is gradient boosting like with XG boost


I really need to spend some time there as well


It's just yeah something about neural networks that just draws people in I think but yeah, I just got into it


Really with sentiment analysis that was my first real ML type task and like doing things like with finance stuff like that


That's how I got into it now favorite type of programming


I just don't know what kind of question that is so I don't think I can answer that I


Don't know if you mean like competitive versus. I don't know building things. I don't know I just like to build things


I don't think I have a good answer for that


moving on


How do you get around bad documentation or bad like repo?


It I mean that's just that's a huge question it really depends it depends like do I have to use that library if I don't


Have to use that library


I will go use a different library if I do need to use that library for whatever reason


My next step would be to find like samples of code from other people


So see who has used this project before and honestly I usually do that before I'll even read


Documentation in the repo to be honest like I'll just see is there some somebody actually use this code because that's where I


You know like the documentation is one thing


But all you're getting like these little slices whereas if I can see how someone has used it in like an actual project


It just clicks better for me anyways


But some documentation is exquisite like the pandas


Documentation is really good because that's what you're using you're just using these like really unique tools on a data frame


So like in some cases documentation makes a lot of sense


But in many cases it doesn't quite make a lot of sense and then also if a thing like generally the documentation


Becomes useful to me after I've learned how to use the module or the library or whatever we want to call it


So then I'll go to the documentation but first I have to learn like the basics of using this thing and


Usually I go find sample code first anyways


but if for whatever reason the documentation is bad more samples or


Read the source code of you you can just go into the source code and read it


The other thing I tend to do is just to see what are my options you can use the dir


Dir and then in parentheses pass the thing that you're curious


What are all the methods that we can do on this thing?


I'll just do that and see okay, I can usually they're properly named so or you can guess what it means


and then you can just kind of trial and error just kind of play around and


And just figure it out that way honestly, so hopefully that answers your question


So next on to the last question here


What approaches do you take in order to get a better understanding of new programming concepts that might be confusing and frustrating?


The best tip I can give there is take your time. I think people get like way too


enamored in like


Just sitting down and just grinding a thing out when sometimes the best thing you can do is just sit down and just


Walk away go to sleep take a shower eat eat some food


I could definitely say like like almost daily


I'll be like working on a problem, and I really want to finish this thing, but I'm getting hungry


And I just I'm like I just want to really get this done


And I try to grip my teeth and then eventually it's like now you need to just just go eat. Just just walk away


So sometimes the best thing you can do is just walk away and unlike sometimes


I'm you know if you're just hungry at least for me my brain just starts getting worthless or tired


So I'll just kind of walk away, but even on like really tough challenging


concepts and stuff


The first thing you need to take into consideration is maybe today


You're not going to get the whole thing and the best thing you can do is just kind of walk away


So just keep taking like little doses also


Generally if a thing is confusing or frustrating


It is confusing or frustrating because you've attempted to skip a step


So I've never come across anything be it programming or any other concept in life where


if if I did things in the proper order it


It was still confusing yeah, so so generally anytime anything's ever been confusing to me


It's because I'm trying to understand a concept


Before I've understood like a prerequisite concept to that concept so a lot of things like math related a lot of things programming related to


A lot of these things if you find yourself confused


Then you're probably you've skipped something and then you're then the next thing you want to do is figure out


What was that thing that you've skipped because you've you've you've probably skipped a thing?


Other than that if that's not the case again just think about it


Other than that if that's not the case again just go to sleep


Or you know take the day off or whatever and try to approach that problem again


But yeah, I think a lot of times people just want to grind things out. I mean myself included. I'm not pointing the finger


um, at least that's that's been the truth for me and


Most of my largest gains have occurred while I slept and I just revisit this thing the next day


Okay, so uh, that's a few questions answered


I know there was a lot of other questions that people had um posted already if you've got your own question


Feel free to leave it below. Um


I'll get to the other ones


I just felt like these were all kind of like coding related questions if you posted a coding related question


And I didn't answer it again. I might still answer it. I just uh didn't want to answer like 30 questions all in one video. So


anyways


Finally a quick shout out to recent channel members


uh


Riddian Ferdinand Muhammad Almore backwards are Michelle


Berardi Matthew Blom Ivan Buknik Gene Enzer Fidget Jackson Kirill and Henry de Mina


Thank you all, uh very much for your support. You guys are


Amazing. Um, I think I said new members actually, I think there was only yeah one two two people that are new members


the rest of these are


Uh re-upping members. So yeah. Thank you guys very much for your support. Um


[ML News] ConvNeXt: Convolutions return | China regulates algorithms | Saliency cropping examined

Facebook makes convnets return to glory. A new text to speech model lets you speak any


language you want. And automated music transcription gets a boost. Welcome to ML News.


Hello and welcome to ML News. It is so great to have you here. How are you doing? I hope


everyone's okay. Let's dive into the first story. Facebook Research publishes a paper


called a convnet for the 2020s, in which they take on the notion that somehow transformers


are to replace convnets for computer vision. They make the argument that rather than the


attention mechanisms in transformers, it is due to some more kind of subtle improvements


that the transformer architectures have over classical convnets. Now they show that if


they systematically include the best of these changes, then they can make a convnet that


performs as well or better than vision transformers. This results in the following graphics starting


from the original resonance in the bottom left corner and comparing to various vision


transformer architectures on ImageNet 1k and ImageNet 22k that allows also pre trained


models. Now this has obviously garnered quite some attention. The code is actually available


online if you want to try. But for example, Lucas Beyer has pointed out that if you do


compare to VIT that is trained, let's say properly with augmentations and so on, then


the conv next isn't that far ahead, the graphic should look more like this. And Ross Whiteman,


maintainer of a popular library of computer vision models also points out that if you


take a resonant and you train it properly, then you will be at the level of like a small


conv next. And that would mean that the resonant bubble itself would also be lifted to about


the 82 mark right here. Another comment came from Min Shin Tan who augments the graphic


by efficient net v2 on ImageNet 1k and 22k, which would result in the following graphic.


So safe to say what we can read from this is that the market for models in computer


vision isn't decided at all yet the race is still wide open. And it seems like we can


achieve comparable performances with various different architectures. Now maybe it is the


case that all you need to do is just take a big model with lots of parameters. And it


doesn't really matter what you do as long as you do a certain number of things right.


On the other hand, it could also be that we haven't yet come across the ultimate architecture


yet and there is still an architecture out there somewhere waiting to be discovered to


dominate computer vision once and for all only time will tell. For now go and check


out the code of conv next it is on GitHub. Interestingly meta research still uses the


Facebook research GitHub handle. There's been a paper making the rounds called auditing


saliency cropping algorithms that investigates popular saliency cropping methods. Saliency


cropping is what these platforms for example Twitter do to pictures in order to make them


fit the predefined format. For example, the picture here on the right is in fact much


longer if you click on it. Yet in order to fit the familiar Twitter timeline, it needs


to crop it somewhere. So these platforms they try to decide what is the most salient what


is the most interesting point in a picture and they try to crop towards that rather than


just always cropping to the top or to the bottom or to the middle. Now for a bit more


background, people in the past have often criticized the saliency cropping algorithm


due to them being said to have certain preferences for certain skin tones and also exhibiting


a phenomenon where they would focus on the non face parts especially of women. There's


this famous example of two politicians one light skinned one dark skinned and no matter


how you order them if you make a long picture that has one at the one end and one at the


other end and then a white area in the middle the different algorithms would choose to focus


on different faces repeatedly. This paper systematically investigates the saliency cropping


algorithms of Twitter, Google and Apple in both skin tone differences and also with respect


to the phenomenon of what they call the male gaze. Now they make a big deal out of this


idea of the male gaze which is a concept that essentially says society will reorder itself


will build products will make media to represent the male view of the world specifically how


men look at women. Mostly the narrative is around objectification. And when people shared


anecdotal evidence of Twitter cropping pictures of women in the following way this played


into this narrative of the male gaze. So the hypothesis would be that through whatever


mechanism mostly how the training data is collected and so on, the algorithm would learn


to focus on the non face part of female bodies and therefore reproduce the male gaze that


built the data set or built the society where the algorithm was trained in. Obviously that


would be a problem and discovering an effect like this would be quite interesting. The


paper noticed that the anecdotes posted the examples posted of this happening were mostly


women on runways in red carpet type situations. So they collected a data set of pictures like


these and run them through the saliency algorithm. And surprisingly, they discovered that whenever


the algorithm did not focus the face itself, it would actually focus mostly on some sort


of corporate logos in the background. Now these corporate logos happen to be very often


not on face level, or at least the ones that the algorithm chose to focus on would not


be on face level, resulting in a non face centric crop. Now there's two ways to go from


here. One way would be to say, Oh, look at this, the algorithm is kind of crap, it misses


the face a lot of the times it focuses on these logos. And that gives the appearance


of the algorithm objectifying women or having anything of that effect in there. And therefore


we can discard the male gaze hypothesis or whatever we started with. The paper doesn't


do this, however, instead, it makes a big point of calling these things male gaze like


artifacts or male gaze like effects, essentially retaining the opinion or the appearance that


this is still problematic in regards to this effect. So instead of saying it's actually


not sexist is just crap, they do word plays and simply characterize it as whatever they


want dash like. And this I find to be a little bit worrisome. In my opinion, this clearly


shows that the authors were out to find this effect, they were out to find something of


this nature. And the data just didn't back that up. And honestly, given how many ways


you can slice and dice data and do analyses, I'm quite astonished that they didn't find


anything that they could show as evidence for that. But then instead of discarding,


they choose to keep this hypothesis in there and they choose to call the artifacts they


find male gaze like. Now the paper itself can do a lot of hedging. The paper can say,


well, we described what this is, right? We never meant male gaze, we meant male gaze


like they can hedge by saying, well, our paper is mainly about the methods of testing this.


It's not really about the results. It's more about the how we collect the data set and


so on. So you can construct a paper that no one can essentially criticize you until you


can just backtrack into your I did nothing wrong. And then when you promote the paper,


you can be a bit more loose, right? Still not saying anything wrong, you can be a bit


more loose, you can just kind of leave away things because you're just promoting it, it's


social media or a talk or whatnot. And whenever you get criticized, you can say, well, we


clearly defined things in the paper, I'm sorry, Twitter is a short medium and so on. And then


maybe other people come in and pick it up. And they just see kind of the title, maybe


a little bit of the abstract, maybe a little bit of the promotion. And ta da da da, in


the eyes of most people out there, you will have successfully reached the original hypothesis.


Now I'm not saying investigating these things is not good or anything like this. Like I'm


happy that there are people who do these types of investigation. I'm very happy that people


publish, look, here is how to collect the data set. And here is how to study these things.


But if the experiments had turned out the other way, like if they found that the most


salient point after the algorithm would always be on women's private parts or something like


this, do you think the paper would have sounded the same? Do you think the paper would be


of, you know, we just want to get our methodology out there? We don't really, it's not really


about the results or so on, like, nah, nah, no way. As I said, the paper also does a systematic


investigation into how the algorithms focus on skin tones, the results there are mixed


as well. But I'll leave it at that. I don't want to criticize this paper super particularly,


even though I do think it is politically motivated. But it's just difficult to evaluate things


when it is quite clear the authors wanted to find a certain thing. There's no text to


speech system called your TTS towards zero shot multi speaker text to speech and zero


shot voice conversion for everyone. Now this system reaches state of the art in zero shot


text to speech. And it is quite intricately trained. But what you can do is you can have


your voice say something in a completely different language. So I'm gonna try this right here.


Hello and welcome. You're listening to ml news. Alright, so now I'm going to go to French


and I don't actually have to say the same thing in French. Yeah, yeah, no, yeah. Ooh,


please. It mom, ma baguette. She up here, my baguette. Let's check it out.


What's the music playing in the background? All right, well, in any case, it sounds pretty


good. So and it's really fast. The code is available a link to the colab and everything.


Give it a try.


mt three is a system for multitask multi track music transcription is part of Google's project


magenta that applies machine learning to the arts. This is also available and it's again


pretty cool what it can do. There is a hugging face space where you can upload your own audio


and have it transcribed and there's this demo on Reddit.


Yes, it is MIDI like it's not supposed to sound the same but it does transcribe the


music into multiple tracks into multiple parallel tracks is really hard task and it's really


cool that this is sort of possible out of the box model is available on GitHub, you


can check it out.


Quartz writes China's new algorithm rules are at odds with its tech giants is business


models. This is an article detailing China's new rules for what they call algorithms, which


are essentially recommender systems. So the new rules mean that algorithm providers need


to proactively spread positive energy, ensure their algorithms are for good, and they curtail


algorithms for promoting or causing excessive spending or for the algorithms to lead to


developing an addiction to the platforms. This is obviously targeted at many of the


newer social media systems that explicitly use recommender systems to drive most of their


business. Now, while this seems like a pretty unprecedented move, especially for China,


the article also says that some argue that the impact might not be so large because the


rules essentially only require that users have the ability to opt out. And a lot of


users simply are not going to do that. But it's pretty cool that at least you have the


option to do so. And honestly, in my opinion, I'd much rather have an opt out feature that


is like buried somewhere in three layers of setting than every single website asking me


whether and what cookies I want. That's just annoying. Not saying I don't see the reasoning


behind the rules existences. I'm just saying it's freaking annoying. Shlomo Kashani and


Amir Ivory release deep learning interviews, hundreds of fully solved job interview questions


from a wide range of key topics in AI. This is version two. And it includes it is a giant


PDF that includes questions and solutions. You can see it's over 360 pages from all disciplines


of ML. So if you're looking to prepare for job interviews, or simply up your skill a


little bit in a different area of ML, this might be a neat resource for you. All right,


we'll come to some helpful material, helpful libraries, helpful things that I found. Deep


Checks is a tool for validating machine learning models and data. It essentially acts a little


bit like a unit test framework for machine learning code. DAG's hub is a platform to


version data models, experiments and code, they claim to have GitHub like experience


for machine learning. Now while I enjoy the presence of yet another ML ops system and


the launch of release two, which also integrates data labeling into their system. The coolest


thing about this is their background on the website. See follows your mouse and this is


just cool. And I think every time you enter you get like a new color. Look at that. Wow.


It's completely dark when you start so you don't you never expected and then what's up


Bayesian modeling and computation in Python is a free book that is available online about


Bayesian modeling and computation in Python. It is on Amazon if you want the hardcover,


but you can just read it online if you want to ml contests.com is a website that just


keeps track of machine learning contests, for example on Kaggle AI crowd and more gray


scorch is a wrapper around scorch to use Ray for distributed training. Now what is scorch


you ask good question scorch is a wrapper around pytorch in order to make it compatible


with sk learn rumble is a database that is built on top of Apache Spark and HDFS and


it allows you to feed in JSON and process a lot of data very efficiently with a JSON


like query language. So you can query heterogeneous data, you can query nested data, and it will


scale from your laptop all the way up to data centers. It's open source, you can check it


out. jacks models is a GitHub repository that says it's an unofficial repository of jacks


implementations of deep learning models. It is a young project, but it does have some


models inside and it is growing. If you're into jacks and you're looking for a model,


maybe you'll find it here. s3 prl is a library to process speech specifically a self supervised


speech pre training and representation learning toolkit. Alright, that was it for the helpful


stuff. I hope some of you have been helped by the helpful stuff. I've come across this


blog post right here explaining alpha zero and I found it to be very understandable and


instructive. So if you want to get into alpha zero or any of the related algorithms, maybe


give this blog post a read it explains everything pretty well and understandably. And it's a


good first contact with these kinds of algorithms if you don't know yet exactly what they do.


The blog post is by Josh Varty and I'll link it in the description.


Sherbank AI have been making some progresses into large models recently they release Rudolph


after rudalee. Rudolph is what they call a hyper modal transformer. They call it hyper


modal because it has multiple multimodal components. The first component is a text to image part


in the second component is an image back to text part with this they can do various tasks


such as visual question answering, they can do abstract like visual reasoning and many


more things. Obviously, they can also do whatever the individual parts can do such as image


generation from text like the lead or image compatibility tasks such as clip the model


tokenizes images into latent tokens using a VQGAN and from there on it essentially treats


it as a sequence of token models. The outputs of this models are pretty impressive and the


code as well as the small models are available online and there's even a collab for you to


try it out. The collab itself is also a little bit of a write up of how the model works.


So if you're interested in that give it a try.


Lastly, Jeff Dean has a rather long blog post on a 2021 summary of Google Research's advances.


It's divided into five trends, for example, more capable general purpose models, more


efficient models, and so on. Now a lot of it is not only geared towards Google Research,


but also Google products. And I won't go into the blog post itself here. But if you're interested,


this is a good overview over at least a slice of the ML research landscape in 2021. And


that was already it for ML news. Thank you so much for tuning in for being here. Everything


Distance Sensor GPIO cont'd - Raspberry Pi and Python tutorials p.7

What is going on everybody and welcome to part 7 of the Raspberry Pi tutorial series in this video what we're gonna be doing is


wiring up the distance sensor now to do this we're gonna need quite a few things we're going to need a


actual distance sensor that's the HCS ro4 and


Then we're going to need four male to female jumper wires, and then we're going to need three


1k ohm resistors, or


You could have a one 2k ohm resistor, and then one 1k or any way to make 1k and 2k ohms


so the


VCC


Trigger echo ground not sure how well you can see that on camera. It's probably not focused


I turn off autofocus, but anyways it says right here on each of the pins what they are so


The VCC that's your that's your power trigger is the sensor basically it's going to shoot out


So basically when you trigger the trigger it's going to shoot out


Some some sound and then the echo is the input and that measures when the input actually occurs and then GND is ground


So with that let's go ahead and connect everything right once you've got everything hooked up


You've got the Raspberry Pi turned on you're ready to measure distance, so let's go ahead and see the into


Desktop and then let's nano


distance sensor dot Pi


And we're ready to begin first. We're going to just like before import our P


lowercase I dot all caps GPIO as all caps GPIO we're going to import time


GPIO dot set mode


GPIO


Whoops GPIO dot BCM then we're going to define


Trig as


4 and echo as 18 so the reason why we're doing this is if you didn't notice before in the previous tutorial


If you follow along with the led lights


Just hard coding each of the pin values every single time can be pretty tedious and if you as you make a larger and larger program


It's going to get out of hand should you ever need to change those pin numbers


So you probably just want to give them a constant right at the beginning of the script or rather than typing it out


So that's why we're doing that next what we're going to do is GPIO dot set up


We're going to set up the trigger pin to be a GPIO dot out and then GPIO dot set up


The echo pin that's going to be a GPIO dot in


So the trigger sends out a burst and then the echo takes it in using the known constant


That is the speed of sound we can calculate how much distance was traveled


Based on how long did it take for that sound wave to go out and come back


So


Once we've got that we can go ahead and begin that with a GPIO


GPIO dot output oops we forgot our dot or did we know my mouse was in the way. I didn't see it anyway GPIO output


trig


True and


Then we're going to do a time dot sleep for 0.0001 something like that something good enough


To just give it a moment's rest then GPIO or a moment to do what it needs to do dot output trig false


Then what we're going to do is we're going to say wow the GPIO dot input to the echo is equal to false


So so long as that's the case


We are going to say start equals time dot time, and then we're going to say wow


Wow


GPIO dot input oh


My goodness there we go echo equals true


and we will time dot time


Then we're going to say the sick time is going to be end minus the start and


Then we're going to measure the distance now for centimeters if you want inches you can go to the text base version


I break that one down there. It's I can also just say it as we could anyway


Distance will be equal to the sig time whatever that is divided by zero point zero zero zero zero


Five eight so four zeros after the decimal point if you wanted to do inches


in winches inches would have been


0.000 148


Then what we're going to do is we're just going to print


Distance


CM


Format


distance and


Then finally when we're all done. Let's run a GPIO dot clean up clean up our mess


Control X to save yes that file and let's do python distance sensor dot pi and


There we have a distance if for whatever reason you get some sort of error, or it gives you a warning just run it again


And that cleanup should solve your problem, so right now. We're reading about a 102 and then I'll put my hand in front of it


And now yes, we've got a 21 centimeter distance, okay?


So now that we've done that we've got the light we've got the distance sensor what I'd like to do in the next tutorial is


kind of combine the two things and create something a little more complex and


For that we're going to make a sort of like one of those like garage


Stoplights where basically as you pull into the garage


It's a green light as you get closer closer to the max distance you can pull forward it turns yellow


And then when it's time to stop it turns red


So we're going to make something like that for the raspberry pile instead of using a car


We'll just use our hand as we approach and the closer that we get it'll turn yellow and then finally red


So that's what we're going to do in the next tutorial


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maeth war MOM


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Helo, un sec, dwi'n rhaid i mi gael sian ar y cyfnod, os na fyddai ddim yn gallu ychwanegu chi.


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Roeddech chi'n gweld, roeddech chi'n cyfrif ar y model, fel y model, ond 1000 data points.


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Roeddech chi' ddim am amser h favour, a review wrth fy modd cyfarfodwyr.


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Yw hynny, damn mae hyd yw'r traintweudiedig?


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Roedd angen traintweyd ache other, a byddai hynny'n mynd i fyny arabad.


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Mewn i os Moeg, pre fit traintwaith heb eisio, 137 00 happened down and lost,


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loved you can see here, so this model is doing perily separated


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Carnot trained on the unit what caused


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o'r 1000 data points, felly mae'n efallai efallai o'n ymwneud â'r rhain ar y dde.


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O, ie, yma ar y dde o'r ddwylo.


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Gallwch weld yna, fe wnaethon nhw ddechrau ar ôl i'r cwpl o 100 stryd


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ar gyfer 3 epocs, ond roedd yn gyrraedd am ychydig o bryd


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pan roedd gen i ddysgwyr llangyf.


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Ac roeddwn i'n rhoi'n rhedeg, roeddwn i'n gwybod amdano, ac roeddwn i'n dod i'r bwrdd.


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Felly nawr roeddwn i'n ymwneud â'r rhain.


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Roeddwn i'n cyfrifol am yr hyn a ddodd hynny.


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Ac mae gen i'r pwynt datograff ar y ddŵr hynny a'r pwynt dda.


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Felly, mae'r golyg yn cael datoset cyfrifol gwestiwn.


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A rydyn ni eisiau gwneud yn ymwneud â'r rhain.


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Felly, i greu mwy o ddata, rydyn ni eisiau ddysgu'r model i gyfrifol cwestiwn


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os ydym yn rhoi cyntex a chyfrifol.


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Byddai'r model yn rhoi cyntex a chyfrifol


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ac mae'r model yn rhaid i'w ddod o'r cyntex.


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Nawr rydyn ni eisiau gwneud yn ymwneud â'r rhain.


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Rydym am ddysgu'r model generig.


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Rydym yn rhoi cyntex a'r gyfrifol.


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Ac rydym amdani i'r rhain i gyfrifol cwestiwn


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sy'n rhoi cyntex yn y cyfrifol.


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Felly, rydym am ddysgu'r model eraill i greu mwy o ddata.


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Ac dyma fy nes ymwneud â'r rhain.


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Rydych chi'n gallu gweld, rydw i'n gwneud y rhain ddata'n rhannu


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o'r cyfrifol C, ac yna y cyntex,


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ac yna y cyfrifol A, ac yna y cyngor.


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A pan fyddwn i'n cyfrifol hyn i'r model,


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gallwch weld bod y model yn ymwneud


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gan ddweud bod y cwestiwn yn,


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beth oedd yn ymwneud?


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Felly, mae'r cyntex yn y cyngor yw,


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a'r cyngor yw y cyngor.


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A'r model yn dweud bod y cwestiwn yn ymwneud


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beth oedd yn ymwneud.


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Mae'r cyngor yn y cyngor yw'r cyngor.


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Dwi ddim yn gwybod os yw hynny'n dod o'r


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pre-training T5 neu rhywbeth fel hynny.


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Yn unrhyw gwrs, mae hyn yn ymwneud yn dda,


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oherwydd, yw'n gwybod,


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mae'n cwestiwn cyfathrebu,


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ac roedden ni'n cael ychydig o ddiddordeb


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ymlaen.


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Ie, felly,


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rwy'n hyfforddiadol iawn, mae hyn yn ymwneud


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ac mae hynny'n rhoi cyffredin iawn.


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Dyma un o'r datgwyddiadau traint,


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y pwyntiau.


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Dyma'r cyntaest, felly mae'n


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o'r ystod o'r ysgol.


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yn dda iawn.


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Y cyngor yw


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St Bernadette Subrius.


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Mae'r cwestiwn y mae'n ei wneud


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yn ymwneud â'r cyngor.


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Ond,


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felly, i beth o'r ystod,


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a wnaeth Mary'r Ysgol yn ymwneud


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ar y Ddŵr?


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Mae'n un o'r pwyntiau


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a pha fydd Mary'r Ysgol yn ymwneud


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yn Lourdes?


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Mae'n un o'r cwestiwn y mae'n ei wneud.


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Felly mae'n un o'r pwyntiau


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a pha fydd yn ei wneud yn ystod y datgwyddiad.


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Ac yna hefyd,


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i beth ysgol French yw


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y statiw gwyddiadol


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o'r Mary'r Ysgol yn ymwneud


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yn ymwneud?


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Felly, gallwch weld yma


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mae'n ddim,


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i'r gyfresaeth cyffredinol,


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Felly,


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y ddŵr ymlaen.


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Gallwch weld y gallai


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dysgu i ddysgu


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yn rhywbeth i ddysgu


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gwestiwn ddwy


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cymdeithasol rydyn ni'n ei ddweud,


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os nad yw


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yn rhaid i'r


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ddŵr


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gwneud swb


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cymryd rhan o


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llawer o ddata


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ddata.


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gweithiau a'r ddysgwyr,


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fel arfer.


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mae hynny'n ei wneud


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gysylltu'r notebook IPython, ond mae'n


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ddim yn gysylltu'r model hwnnw'n ei


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beth rydw i'n ei wneud yw


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dydw i'n


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ddiddordeb


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model yma.


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dyma'n ddiwylliannol.


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Ym.


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Mae'r


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model arfer yn arfer


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Rwy'n rhoi unrhyw enw, tynnu model,


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ddisgrifiad, ac yna


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dweud,


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log arfer.


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A chyfnodwch


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yr arfer i unrhyw


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arfer.


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Yn amlwg.


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creu'r arfer.


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Mae 1DB yn yma,


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cyfrif.


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Arfer


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wedi'i gael. Nid yw'n


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dweud y model.


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Ond, lle yw


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y model?


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gallwn hefyd edrych yma


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gweithiau a chyfnodwyr. Mae'n rhaid i chi gael y logau yma.


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yna, aelodau yn


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test, rhainer,


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a yna rhywbeth o


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cekpoint.


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Yn


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amlwg,


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ac mae gennym hefyd y model.


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Ond,


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gallwn ni...


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Yn amlwg, mae gennym hefyd y model?


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Ne!


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Ychwanegu


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y model i adeiladur.


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Sut ydym yn ei wneud?


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Iawn, mae yna


Machine Learning Holidays Live Stream

lot of plumbing. Models, pre-trained model, push to hub. I don't want to push to hub,


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I want to save. I want to save it. Now model outputs is not the right thing. Save pre-trained.


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OK, dyna'n dda. Models, save pre-trained. A yw hynny'n arwain yma?


Machine Learning Holidays Live Stream

And then we're good. Exists? Nice. OK. So we'll say my model. All right, we did it.


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Configuration saved. Nice. And now we say 1db.artifact. Let's just peek at the documentation.


Machine Learning Holidays Live Stream

A name, a type, a description. OK, so the name is Quest Q Generator, the type is model, and the description is generates questions.


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Gweld hynny. Felly dyma fy modell gwestiwn generasio.


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OK, and now what we want to do is add a directory and then log the artifact. My model.


Machine Learning Holidays Live Stream

Oh, stupid me. And then log the artifact. Now how do we get the 1db.run?


Machine Learning Holidays Live Stream

Does this work? Yes, this works.


Machine Learning Holidays Live Stream

And now we should have it. We should have it. We should have it here.


Machine Learning Holidays Live Stream

As soon as it's synchronized. Code, code, code, code. Not yet. There we go. Look at that.


Machine Learning Holidays Live Stream

So here's our model. Yes. OK. We have a config. We have a model. This is pretty good. This is excellent.


Machine Learning Holidays Live Stream

So this is a variant of T5 small. We always know what tokenizer to use and we can use it as we want.


Machine Learning Holidays Live Stream

Load model for inference. We should just be able to, with the same project name, use the model, load it up for inference.


Machine Learning Holidays Live Stream

And then load it into Hugging Face. OK. Probably what we want to do is we want to explicitly like set the project name, right?


Machine Learning Holidays Live Stream

Especially, you know, since we need to be in the same project in order to load this model, it would make sense to have the same project name.


Machine Learning Holidays Live Stream

Therefore, or what was the project name?


Machine Learning Holidays Live Stream

Hello. What's weight and bias is used for? It's used to log all the outputs. So to log your graphs, but also here to log your models, which is pretty cool.


Machine Learning Holidays Live Stream

So, OK, the project name is called Hugging Face. I guess that's fair. That's fair enough. Where did it get this idea from? Did we set this anywhere?


Machine Learning Holidays Live Stream

Not explicitly, but, you know. OK. So what we can do now is we can generate the data set.


Machine Learning Holidays Live Stream

And now let's just do the noobiest thing there is and just copy. Just make it. Can we duplicate this?


Machine Learning Holidays Live Stream

Can we just, how do I duplicate the notebook? This is by far the noobiest thing to do. But we'll do it.


Machine Learning Holidays Live Stream

So save a copy. That's probably fine, right? That's probably OK.


Machine Learning Holidays Live Stream

All right. Now we have hfqa inference. OK, now we can set. So here, over here, we would probably want to set the run to 1db.


Machine Learning Holidays Live Stream

What was it? Init. Project. OK. And so that's fine. And then over here, we want to set it to job type inference.


Machine Learning Holidays Live Stream

OK. We want to quit this other session over here.


Machine Learning Holidays Live Stream

And soon as we're done. All right. So now, now comes the crucial part, right?


Machine Learning Holidays Live Stream

So we have logged in here. Now we need to load the model again over here.


Machine Learning Holidays Live Stream

So I'm doing this in two files deliberately because we could reuse this model pretty much anywhere.


Machine Learning Holidays Live Stream

Therefore, I want to show how this is done. Just downloading the model. And this should be fairly easy, right?


Machine Learning Holidays Live Stream

Here we get the artifact by saying use artifact, then we say download and that gives us the directory.


Machine Learning Holidays Live Stream

And then we simply instruct Hugging Face to load from that directory. Could not be easier. Let's try it.


Machine Learning Holidays Live Stream

So is it run.use artifact? Probably. So and that's my generator, my model. What was it? What was the name that we gave it?


Machine Learning Holidays Live Stream

The GQ generator. OK. So we use this artifact, model artifact, then we say download. That gives us the model directory.


Machine Learning Holidays Live Stream

And then we say, let's just print it. Let's see what this gives us. Nope, nope. OK. All right.


Machine Learning Holidays Live Stream

Without an alias. OK. Latest. That's it. This is stupidly easy. Still at it. Yes. This is going to take a while.


Machine Learning Holidays Live Stream

OK. So we have a model directory ready. If we execute that again, it's probably still cached. All right. Cool.


Machine Learning Holidays Live Stream

And now we should just be able to just load this here. I mean, right? Because that's what it's for.


Machine Learning Holidays Live Stream

So we just say from pre-trained. OK. It's deprecated. It's fine for now. It's fine.


Machine Learning Holidays Live Stream

Yes, the config, I guess we can suppress this output right here a little bit. And, and.


Machine Learning Holidays Live Stream

Hello, my dog is cute and. Hello, my dog is cute and cute. No. What was it? So now we, if this really overfit, we expect.


Machine Learning Holidays Live Stream

OK. We still have the same thing. I hope this actually, I hope this actually is the correct model.


Machine Learning Holidays Live Stream

So now, OK, we load the data set again and. We are, I guess we should still. The question is, how do we process it now?


Machine Learning Holidays Live Stream

OK. So we still have the squad data set, but now we need to we need to process it. Should we process it in the same way?


Machine Learning Holidays Live Stream

So now we should process it. Yes, we should process it probably in the same way. But now we should feed it to the model in inference mode.


Machine Learning Holidays Live Stream

So we have no more, we have no more labels necessarily.


Machine Learning Holidays Live Stream

It's just that down here we don't have training. We simply we don't need the trainer. We can run all of this. This is fine.


Machine Learning Holidays Live Stream

But we don't need the trainer. We don't need training arguments. We don't need train.


Machine Learning Holidays Live Stream

Now we need a loop over the data set and for every point in the data set we feed it to the model in inference mode like we do down here.


Machine Learning Holidays Live Stream

Down here.


Machine Learning Holidays Live Stream

So X2 here is a point from the training data set. We simply want to do this with every piece of the training data set.


Machine Learning Holidays Live Stream

Decoder IDs.


Machine Learning Holidays Live Stream

Yep. So we'll do that.


Machine Learning Holidays Live Stream

Yep.


Machine Learning Holidays Live Stream

And let's like, can we even remove the min length or set it to something really small like three?


Machine Learning Holidays Live Stream

Probably. And then do sample.


Machine Learning Holidays Live Stream

So we'll say for y vim.


Machine Learning Holidays Live Stream

For X in train data set.


Machine Learning Holidays Live Stream

And then here in the batch decode we'll have.


Machine Learning Holidays Live Stream

The predicted question is going to be this.


Machine Learning Holidays Live Stream

And we'll just add that to some data set. Right. So predicted questions.


Machine Learning Holidays Live Stream

And we'll append.


Machine Learning Holidays Live Stream

And we should probably. Do we have like a takadam? Do we have this in here?


Machine Learning Holidays Live Stream

We probably should.


Machine Learning Holidays Live Stream

So.


Machine Learning Holidays Live Stream

All right. So now we take every question. We don't want X2. We want X.


Machine Learning Holidays Live Stream

We take every question and we put it in.


Machine Learning Holidays Live Stream

And we'll see what comes out. And we'll just do this. Like we'll do it once.


Machine Learning Holidays Live Stream

And break just to see if this works.


Machine Learning Holidays Live Stream

OK. Well OK. I haven't actually installed it.


Machine Learning Holidays Live Stream

It's not defined.


Machine Learning Holidays Live Stream

Oh I need to import it. OK.


Machine Learning Holidays Live Stream

We also don't need this. We don't need to save this.


Machine Learning Holidays Live Stream

Or this or this. Just this.


Machine Learning Holidays Live Stream

Nice.


Machine Learning Holidays Live Stream

Yep.


Machine Learning Holidays Live Stream

This is fine. Right. So we actually have an output here.


Machine Learning Holidays Live Stream

Now if we just do this for every single question.


Machine Learning Holidays Live Stream

That should give us a data set.


Machine Learning Holidays Live Stream

Now the problem is that this.


Machine Learning Holidays Live Stream

This data set is just a bunch of questions right now.


Machine Learning Holidays Live Stream

So if I stop this. You know. Predicted questions.


Machine Learning Holidays Live Stream

Like let's get this one.


Machine Learning Holidays Live Stream

OK. We got.


Machine Learning Holidays Live Stream

First of all it's pretty small. You can see it's like cut after some some length.


Machine Learning Holidays Live Stream

And. But we'll worry about that later.


Machine Learning Holidays Live Stream

But for now what we want to do is we want to grab out the question.


Machine Learning Holidays Live Stream

So we want to do a bit of a bit of of a post processing with this.


Machine Learning Holidays Live Stream

So let's say you know after after we've predicted all the questions.


Machine Learning Holidays Live Stream

We'll zip them together in zip.


Machine Learning Holidays Live Stream

And we'll sort of make a fake data set.


Machine Learning Holidays Live Stream

Right. Now the question is how how do we make this data set.


Machine Learning Holidays Live Stream

Let's first make it as just a list of dictionaries and then we can worry about making an actual whatever hugging face data set out of this.


Machine Learning Holidays Live Stream

So what essentially what we need to do is we simply need to replace the question given the question in in this set.


Machine Learning Holidays Live Stream

Right. So we'll take out the.


Machine Learning Holidays Live Stream

Actually we can we can probably do this up here.


Machine Learning Holidays Live Stream

Fake data set.


Machine Learning Holidays Live Stream

OK. So the predict the question what we need to do is we need to split it at the first question instance.


Machine Learning Holidays Live Stream

So. Split queue.


Machine Learning Holidays Live Stream

Right. This right here. Let's say if it's not in.


Machine Learning Holidays Live Stream

If. Not in.


Machine Learning Holidays Live Stream

I mean it should be in right because it's here.


Machine Learning Holidays Live Stream

But you know who knows. Let's just warn.


Machine Learning Holidays Live Stream

I don't know. Let's let's warn. Let's.


Machine Learning Holidays Live Stream

Let's look over here.


Machine Learning Holidays Live Stream

Is look over nice for collapse.


Machine Learning Holidays Live Stream

I don't know. It's a cool logging library.


Machine Learning Holidays Live Stream

OK. Then.


Machine Learning Holidays Live Stream

In.


Machine Learning Holidays Live Stream

OK.


Machine Learning Holidays Live Stream

All right.


Machine Learning Holidays Live Stream

Logger.


Machine Learning Holidays Live Stream

Yeah it's defined. I know it's defined.


Machine Learning Holidays Live Stream

Now it's defined. Good.


Machine Learning Holidays Live Stream

Did you do a sanity check. Yes I did. Exactly. The sanity check worked and the model actually trained and now we are super duper producing data.


Machine Learning Holidays Live Stream

So we'll split one time which will give us something and the actual question.


Machine Learning Holidays Live Stream

Let's overwrite this variable.


Machine Learning Holidays Live Stream

We also break here. No continue.


Machine Learning Holidays Live Stream

And then we'll new X is just all of X.


Machine Learning Holidays Live Stream

And also.


Machine Learning Holidays Live Stream

If you see the structure of the data right here there is a entry called question.


Machine Learning Holidays Live Stream

There is also an ID. We should probably give it a new ID.


Machine Learning Holidays Live Stream

We probably we probably should.


Machine Learning Holidays Live Stream

There is also so import you ID right that exists in Python.


Machine Learning Holidays Live Stream

How does that work. So if I do.


Machine Learning Holidays Live Stream

This I think it should give me something it should give me a you you ID. What if I make this into a string.


Machine Learning Holidays Live Stream

Good enough. Good enough.


Machine Learning Holidays Live Stream

So.


Machine Learning Holidays Live Stream

Here we were.


Machine Learning Holidays Live Stream

ID. Let's say let's say that is.


Machine Learning Holidays Live Stream

Fake and then.


Machine Learning Holidays Live Stream

For and string of that.


Machine Learning Holidays Live Stream

And then question.


Machine Learning Holidays Live Stream

Should be this one right here and then we just append to the fake data set.


Machine Learning Holidays Live Stream

So now.


Machine Learning Holidays Live Stream

That didn't work fake data sets. What is it. Why do I.


Machine Learning Holidays Live Stream

Append an S everywhere.


Machine Learning Holidays Live Stream

Excellent. So we run inference on T5. We could probably batch this or something like this. But you know things can be improved later.


Machine Learning Holidays Live Stream

Do dict keys to see what's inside the object.


Machine Learning Holidays Live Stream

Thoughts on Julia language. I have no clue. I have never tried it. But I'm not.


Machine Learning Holidays Live Stream

I don't like language wars. It's just just use whatever languages is most easy.


Machine Learning Holidays Live Stream

Hope it's not based on log4j.


Machine Learning Holidays Live Stream

Redeeming your Monday bonanza. Yes. Yes.


Machine Learning Holidays Live Stream

So this. Yeah this week with the holidays is obviously a bit a bit weak like a bit slow.


Machine Learning Holidays Live Stream

I thought about just doing an ML news live stream since there's probably no editing going to happen this week.


Machine Learning Holidays Live Stream

So as you can see we are logging a fake data set in the meantime we'll look up how do we do data set versioning with weights and biases right because that's another thing.


Machine Learning Holidays Live Stream

So type raw data.


Machine Learning Holidays Live Stream

That seems easy enough. Type raw data. Do we have something else.


Machine Learning Holidays Live Stream

Not really.


Machine Learning Holidays Live Stream

Splits prepackager splits. Add directory.


Machine Learning Holidays Live Stream

You can have different sub directories. Nice.


Machine Learning Holidays Live Stream

But you know. I mean we can just we just log this out right. And.


Machine Learning Holidays Live Stream

Yeah why not.


Machine Learning Holidays Live Stream

The quote. The only question is how do we save a data set such that the hugging face data set understands it.


Machine Learning Holidays Live Stream

That's like my only question. How how how does one make a custom data set.


Machine Learning Holidays Live Stream

AWS or Google Cloud for training models on cloud GPU. I don't know. I don't. I've never worked. It depends also on what you want to do if you want if you just want the machines or if you want to do something like SageMaker.


Machine Learning Holidays Live Stream

I don't know. Stuff evolves so quickly in this in this in this area. It's crazy. I cannot keep up. So I have no no way of giving advice there. Let me switch power cables around here.


Machine Learning Holidays Live Stream

I have to say the the iPad Pro is like one of the best investments ever for me. This is insane. Like it's it's super good at at with the pen.


Machine Learning Holidays Live Stream

I used to have this Microsoft Surface and this was already it was OK but it would sort of get slow over time as it got older and especially in summer.


Machine Learning Holidays Live Stream

Because it also had to record the screen and now with the iPad because I also have the MacBook which I usually don't like this kind of vendor lock in things but they just make it so nice together.


Machine Learning Holidays Live Stream

So I just plug in the cable and I can record in OBS and now I can just have it as a second screen because in the studio thing here I don't have like a monitor.


Machine Learning Holidays Live Stream

But when I want to stream I have to have like OBS and chat somewhere. So this is just insane. It just acts like as a second screen and and even without a cable. Insane.


Machine Learning Holidays Live Stream

I'm amazed by technology. No, by just how well this this works together. Right. OK. So the data set objects. How to create a data set.


Machine Learning Holidays Live Stream

Share, process, load, process, how to, overview, the data set object. Let's start here.


Machine Learning Holidays Live Stream

Yeah, I don't I just want to create one. Train with data sets. Upload a data set to the hub. Maybe this will help us.


Machine Learning Holidays Live Stream

So how does it does it just take like JSON if I just do JSON line files.


Machine Learning Holidays Live Stream

Do you think that's that's going to work or wait let me grab this real quick and then


Machine Learning Holidays Live Stream

oh no we're here. Let me grab this over here because I need to investigate the file structure right here.


Machine Learning Holidays Live Stream

OK. My question is. Could I just save this as like a JSON line file.


Machine Learning Holidays Live Stream

This fake data set that I'm generating right now. How do I save it such that Hugging Face data sets is able to load it.


Machine Learning Holidays Live Stream

That's all we need to know. If we have this then we can we can like upload it immediately. So here they load a data set.


Machine Learning Holidays Live Stream

What's a data set dict? A dictionary of data sets. OK. So the data set here.


Machine Learning Holidays Live Stream

Load data set. OK. Download or run the loading script. CSV. There's CSV.


Machine Learning Holidays Live Stream

OK. We can probably export it as a CSV. No.


Machine Learning Holidays Live Stream

JSON. That should work. OK. I'm pretty confident we could just.


Machine Learning Holidays Live Stream

We could just probably get JSON. Like export it as a JSON.


Machine Learning Holidays Live Stream

And it's going to be fine. We'll do that.


Machine Learning Holidays Live Stream

JSON L. Yeah.


Machine Learning Holidays Live Stream

Ever try training on an external GPU via Thunderbolt. No.


Machine Learning Holidays Live Stream

Not yet.


Machine Learning Holidays Live Stream

OK. I mean let's try this. Let's stop this. So we have our fake data set.


Machine Learning Holidays Live Stream

It's going to be some list right of objects.


Machine Learning Holidays Live Stream

OK. The one to be back in process is shut down. That's I mean that's cool.


Machine Learning Holidays Live Stream

No. Why is it a list of strings.


Machine Learning Holidays Live Stream

OK. Because we didn't append new X obviously.


Machine Learning Holidays Live Stream

Because stupid.


Machine Learning Holidays Live Stream

The one to be back in process is shut down. I mean we're we're fine. No we're fine.


Machine Learning Holidays Live Stream

Can we re initialize abnormal program exit.


Machine Learning Holidays Live Stream

All right. We'll just restart. That's cool.


Machine Learning Holidays Live Stream

You didn't see this. No one saw this. One to be never crashes ever.


Machine Learning Holidays Live Stream

Anyone tells you otherwise lying.


Machine Learning Holidays Live Stream

So we'll go for X in fake data set. And we have to import JSON though.


Machine Learning Holidays Live Stream

We have to import JSON.


Machine Learning Holidays Live Stream

And we need to actually open a file. So.


Machine Learning Holidays Live Stream

Import pathlib2.


Machine Learning Holidays Live Stream

And then we'll say.


Machine Learning Holidays Live Stream

Dataset directory is path my fake data.


Machine Learning Holidays Live Stream

Make there.


Machine Learning Holidays Live Stream

No parents exist. OK is true.


Machine Learning Holidays Live Stream

Sure.


Machine Learning Holidays Live Stream

And then we'll.


Machine Learning Holidays Live Stream

With path.


Machine Learning Holidays Live Stream

Jason line file dot open. Let's call to this right.


Machine Learning Holidays Live Stream

And.


Machine Learning Holidays Live Stream

Right.


Machine Learning Holidays Live Stream

Dumb.


Machine Learning Holidays Live Stream

We can. No no no. So we have to.


Machine Learning Holidays Live Stream

We have to.


Machine Learning Holidays Live Stream

Dumb. S.


Machine Learning Holidays Live Stream

We could do this more pythonic no.


Machine Learning Holidays Live Stream

Pretty sure we could.


Machine Learning Holidays Live Stream

In any case we'll just do we'll do this for now.


Machine Learning Holidays Live Stream

This could be done with like a map and the right lines and the generator.


Machine Learning Holidays Live Stream

It'd be beautiful.


Machine Learning Holidays Live Stream

However.


Machine Learning Holidays Live Stream

This is what we do now.


Machine Learning Holidays Live Stream

OK. Let's.


Machine Learning Holidays Live Stream

Cancel this.


Machine Learning Holidays Live Stream

And then we can run this.


Machine Learning Holidays Live Stream

K.


Machine Learning Holidays Live Stream

Apparently.


Machine Learning Holidays Live Stream

One to be doesn't like.


Machine Learning Holidays Live Stream

It doesn't like what exactly.


Machine Learning Holidays Live Stream

Maybe it doesn't like me using logging.


Machine Learning Holidays Live Stream

Could this be it.


Machine Learning Holidays Live Stream

Now.


Machine Learning Holidays Live Stream

Or does it just not like.


Machine Learning Holidays Live Stream

Well let's search for this.


Machine Learning Holidays Live Stream

Try calling one to be dot finish after interruption.


Machine Learning Holidays Live Stream

That is some advice.


Machine Learning Holidays Live Stream

We'll try it.


Machine Learning Holidays Live Stream

Like here.


Machine Learning Holidays Live Stream

Make it sell.


Machine Learning Holidays Live Stream

One to be dot finish.


Machine Learning Holidays Live Stream

Yeah it has shut down. We know.


Machine Learning Holidays Live Stream

What if we do in it again.


Machine Learning Holidays Live Stream

No.


Machine Learning Holidays Live Stream

This is.


Machine Learning Holidays Live Stream

This is lost now.


Machine Learning Holidays Live Stream

Some. It doesn't like something about me interrupting that cell.


Machine Learning Holidays Live Stream

Right.


Machine Learning Holidays Live Stream

It does not like it.


Machine Learning Holidays Live Stream

Maybe we don't we don't necessarily.


Machine Learning Holidays Live Stream

It what's require.


Machine Learning Holidays Live Stream

I'm pretty sure.


Machine Learning Holidays Live Stream

Also use it incorrectly because I make a new experiment right which I don't I don't necessarily want to do.


Machine Learning Holidays Live Stream

I just want to to log an artifact.


Machine Learning Holidays Live Stream

But what's what's require.


Machine Learning Holidays Live Stream

Reference.


Machine Learning Holidays Live Stream

Python library.


Machine Learning Holidays Live Stream

How about.


Machine Learning Holidays Live Stream

Save.


Machine Learning Holidays Live Stream

Log.


Machine Learning Holidays Live Stream

So when we run in it.


Machine Learning Holidays Live Stream

Can we say some. We already said that this is just an inference run right.


Machine Learning Holidays Live Stream

Project.


Machine Learning Holidays Live Stream

And to tease a user name or a team no conflict no.


Machine Learning Holidays Live Stream

Safe code no group no.


Machine Learning Holidays Live Stream

Job type. There you go.


Machine Learning Holidays Live Stream

This is just a tag.


Machine Learning Holidays Live Stream

Re in it.


Machine Learning Holidays Live Stream

We could set re in it to true right.


Machine Learning Holidays Live Stream

I guess we could.


Machine Learning Holidays Live Stream

I don't know anything else.


Machine Learning Holidays Live Stream

Run time restart. Yes.


Machine Learning Holidays Live Stream

Let's do.


Machine Learning Holidays Live Stream

Maybe it is the logging.


Machine Learning Holidays Live Stream

Like.


Machine Learning Holidays Live Stream

Yeah let's let's interrupt.


Machine Learning Holidays Live Stream

Let's remove the logger.


Machine Learning Holidays Live Stream

Let's restart and run all.


Machine Learning Holidays Live Stream

And in the meantime.


Machine Learning Holidays Live Stream

Let's remove the logger here.


Machine Learning Holidays Live Stream

So we'll just say.


Machine Learning Holidays Live Stream

We'll just print here. That's fine.


Machine Learning Holidays Live Stream

OK.


Machine Learning Holidays Live Stream

Yes it still allocates Iran. I don't even know if I would have to necessarily do this.


Machine Learning Holidays Live Stream

If or if I could just.


Machine Learning Holidays Live Stream

Use artifact.


Machine Learning Holidays Live Stream

Without and just say which project.


Machine Learning Holidays Live Stream

I'm not entirely sure.


Machine Learning Holidays Live Stream

I don't know how anything works.


Machine Learning Holidays Live Stream

Model management maybe.


Machine Learning Holidays Live Stream

Let's go to use artifact.


Machine Learning Holidays Live Stream

Artifact or name.


Machine Learning Holidays Live Stream

Maybe prefixed with entity project.


Machine Learning Holidays Live Stream

Name version.


Machine Learning Holidays Live Stream

I mean this.


Machine Learning Holidays Live Stream

We may need and we may not even have to in it.


Machine Learning Holidays Live Stream

We may just be able to do use artifact.


Machine Learning Holidays Live Stream

Because that might that might make sense.


Machine Learning Holidays Live Stream

So now let's see if we if we break here.


Machine Learning Holidays Live Stream

Does this complain about one to be again.


Machine Learning Holidays Live Stream

No.


Machine Learning Holidays Live Stream

We don't do that.


Machine Learning Holidays Live Stream

We do this.


Machine Learning Holidays Live Stream

Maybe it was the logger after all.


Machine Learning Holidays Live Stream

So now let's cat fake data data dot.


Machine Learning Holidays Live Stream

Jason L.


Machine Learning Holidays Live Stream

Cool.


Machine Learning Holidays Live Stream

And we made the mistake of just removing all columns earlier.


Machine Learning Holidays Live Stream

Ah.


Machine Learning Holidays Live Stream

See plumbing is like machine learning isn't even machine learning anymore.


Machine Learning Holidays Live Stream

Just plumbing.


Machine Learning Holidays Live Stream

So we may we did we removed sort of all.


Machine Learning Holidays Live Stream

All of the extra columns earlier.


Machine Learning Holidays Live Stream

When we did.


Machine Learning Holidays Live Stream

This right here.


Machine Learning Holidays Live Stream

When we transformed the data set.


Machine Learning Holidays Live Stream

So the here right here.


Machine Learning Holidays Live Stream

I think.


Machine Learning Holidays Live Stream

Yes.


Machine Learning Holidays Live Stream

See.


Machine Learning Holidays Live Stream

So we said remove columns and we just removed all of the columns.


Machine Learning Holidays Live Stream

Like how about how about we don't how about we don't do that.


Machine Learning Holidays Live Stream

So we don't do that.


Machine Learning Holidays Live Stream

Then what we're going to do instead is.


Machine Learning Holidays Live Stream

Instead of putting into the model.


Machine Learning Holidays Live Stream

I know we put the input.


Machine Learning Holidays Live Stream

I.


Machine Learning Holidays Live Stream

These.


Machine Learning Holidays Live Stream

Mm hmm.


Machine Learning Holidays Live Stream

Yep.


Machine Learning Holidays Live Stream

Input ideas and the labels.


Machine Learning Holidays Live Stream

These are all this will already be tokenized so we may want to remove them.


Machine Learning Holidays Live Stream

So out of the.


Machine Learning Holidays Live Stream

New acts we don't want all the keys.


Machine Learning Holidays Live Stream

Want answers context I.D. question and title.


Machine Learning Holidays Live Stream

Answers.


Machine Learning Holidays Live Stream

I.D. question context and title.


Machine Learning Holidays Live Stream

That's going to be.


Machine Learning Holidays Live Stream

X is it X.


Machine Learning Holidays Live Stream

Title.


Machine Learning Holidays Live Stream

Context is.


Machine Learning Holidays Live Stream

X context.


Machine Learning Holidays Live Stream

It's called X No Yes.


Machine Learning Holidays Live Stream

Obviously.


Machine Learning Holidays Live Stream

OK.


Machine Learning Holidays Live Stream

Just for making things pretty.


Machine Learning Holidays Live Stream

Nope.


Machine Learning Holidays Live Stream

Screw you auto format.


Machine Learning Holidays Live Stream

How cold is your place is pretty cold.


Machine Learning Holidays Live Stream

This is this is a pretty cold place I'm sorry.


Machine Learning Holidays Live Stream

This is the attic it's not really heated.


Machine Learning Holidays Live Stream

But the studio is is nice here.


Machine Learning Holidays Live Stream

I can set up the green screen and the lamps and all so.


Machine Learning Holidays Live Stream

OK now we should be more ready.


Machine Learning Holidays Live Stream

As you can see.


Machine Learning Holidays Live Stream

This looks better and let's try let's try restarting the runtime one more time.


Machine Learning Holidays Live Stream

Not even not even in it so we don't even want to in it.


Machine Learning Holidays Live Stream

We simply want to say one D.B.


Machine Learning Holidays Live Stream

Use artifact and then.


Machine Learning Holidays Live Stream

Just prefix with the with the project.


Machine Learning Holidays Live Stream

Because otherwise it will just create a run every time which is which is kind of useless.


Machine Learning Holidays Live Stream

No.


Machine Learning Holidays Live Stream

You must call.


Machine Learning Holidays Live Stream

OK.


Machine Learning Holidays Live Stream

Well let's call.


Machine Learning Holidays Live Stream

Let's let's do a run then.


Machine Learning Holidays Live Stream

Let's make one.


Machine Learning Holidays Live Stream

Can I still then I don't have to do this.


Machine Learning Holidays Live Stream

It was worth it.


Machine Learning Holidays Live Stream

A try.


Machine Learning Holidays Live Stream

And yes I don't have to use the run right here.


Machine Learning Holidays Live Stream

So I might be able to just say here like don't create a run at all.


Machine Learning Holidays Live Stream

Because I just want the artifact.


Machine Learning Holidays Live Stream

So now we'll wait a little bit.


Machine Learning Holidays Live Stream

This is good.


Machine Learning Holidays Live Stream

This should be cached.


Machine Learning Holidays Live Stream

No because obviously we don't delete the columns now and.


Machine Learning Holidays Live Stream

Yep.


Machine Learning Holidays Live Stream

Let's wait for like two batches batch sizes eight.


Machine Learning Holidays Live Stream

How was the recording yesterday.


Machine Learning Holidays Live Stream

It was it was excellent.


Machine Learning Holidays Live Stream

We didn't actually record.


Machine Learning Holidays Live Stream

We just discussed about what to record.


Machine Learning Holidays Live Stream

But the vid.


Machine Learning Holidays Live Stream

Yeah I recorded some other stuff.


Machine Learning Holidays Live Stream

That was cool.


Machine Learning Holidays Live Stream

OK we got some data points together.


Machine Learning Holidays Live Stream

And now hopefully once the data set is written.


Machine Learning Holidays Live Stream

Yes we got answers.


Machine Learning Holidays Live Stream

We got context.


Machine Learning Holidays Live Stream

You can see this here.


Machine Learning Holidays Live Stream

Context is long.


Machine Learning Holidays Live Stream

We got question.


Machine Learning Holidays Live Stream

Question still has the end tag.


Machine Learning Holidays Live Stream

We obviously don't want that.


Machine Learning Holidays Live Stream

So we have to also remove this right here.


Machine Learning Holidays Live Stream

So as here.


Machine Learning Holidays Live Stream

If.


Machine Learning Holidays Live Stream

Let's copy this.


Machine Learning Holidays Live Stream

Predq if not predq ends with.


Machine Learning Holidays Live Stream

This S.


Machine Learning Holidays Live Stream

Is it.


Machine Learning Holidays Live Stream

No.


Machine Learning Holidays Live Stream

Yeah.


Machine Learning Holidays Live Stream

Then we'll.


Machine Learning Holidays Live Stream

Question does not end with end tag.


Machine Learning Holidays Live Stream

And this is probably more.


Machine Learning Holidays Live Stream

This is more usual because whenever.


Machine Learning Holidays Live Stream

Yeah you can see right here.


Machine Learning Holidays Live Stream

Whenever it's too long it'll probably stop.


Machine Learning Holidays Live Stream

Before it reached this.


Machine Learning Holidays Live Stream

So this is this is a way for us to filter questions that are too long.


Machine Learning Holidays Live Stream

We might also want to change that in the future.


Machine Learning Holidays Live Stream

But for now this is sort of a good heuristic.


Machine Learning Holidays Live Stream

All right.


Machine Learning Holidays Live Stream

And if that's the case.


Machine Learning Holidays Live Stream

Will.


Machine Learning Holidays Live Stream

Split.


Machine Learning Holidays Live Stream

Will split from the right on this.


Machine Learning Holidays Live Stream

And will take the first.


Machine Learning Holidays Live Stream

Like that.


Machine Learning Holidays Live Stream

Let's see if this works.


Machine Learning Holidays Live Stream

Yep.


Machine Learning Holidays Live Stream

This works well.


Machine Learning Holidays Live Stream

Let's wait for 10 seconds.


Machine Learning Holidays Live Stream

And then we're going to try to log it to weights and biases.


Machine Learning Holidays Live Stream

OK.


Machine Learning Holidays Live Stream

So.


Machine Learning Holidays Live Stream

Again.


Machine Learning Holidays Live Stream

This looks good.


Machine Learning Holidays Live Stream

And now.


Machine Learning Holidays Live Stream

Will do another artifact.


Machine Learning Holidays Live Stream

This time will do simply do instead of a model artifact will make a.


Machine Learning Holidays Live Stream

A data set artifact.


Machine Learning Holidays Live Stream

What was it.


Machine Learning Holidays Live Stream

Raw data.


Machine Learning Holidays Live Stream

Yeah.


Machine Learning Holidays Live Stream

Type raw data.


Machine Learning Holidays Live Stream

Let's call it fake data.


Machine Learning Holidays Live Stream

Description fake questions.


Machine Learning Holidays Live Stream

I know very very descriptive.


Machine Learning Holidays Live Stream

Oh no.


Machine Learning Holidays Live Stream

This is.


Machine Learning Holidays Live Stream

This is the wrong one.


Machine Learning Holidays Live Stream

I wanted to change it here.


Machine Learning Holidays Live Stream

OK.


Machine Learning Holidays Live Stream

Add directory and the directory is.


Machine Learning Holidays Live Stream

Fake data.