Python on hardware news. This is all related. A lot of this stuff is Python. So Cobalt's community, once again, congratulations to Ann and everyone who contributes to the newsletter. We're up to 250 of them. We're going to get to the large LCD displays with CircuitPython in just a second. Please, please, please check out all of the news, stories, and more. We even go over some of the open source certifications we recently did. ARM's IPO, that's going to have probably an impact on some of the chips and some of the things that happen to come out later on, which might just run Python. Some handy cheat sheets, not really cheating. It's just figuring out what Python you need. More highlights from things that are going on. Tons and tons of projects. If you just want ideas for how to get the weather displayed on something. We have events that are going on in the world of Python. Events are coming back that's kind of cool projects. Lots of keyboards. But what we're going to show now is a quick video. This is the screen that is playing at CircuitPython that we got. Coming soon. The PR is green. It's happening. Greetings, ESP friends. Lady Aida, what's this? This is the ESP32-S3. And this is a large 720 by 720 square display. And the rappel is appearing on the display.  Huge display. And you can see it's got the Wi-Fi connected as an IP address. And one cool thing is that once you have displays working in CircuitPython, they all show up as DisplayIO, which means that we have example code for displaying animated GIFs. This is an animated GIF that's stored on the flash. Can you have a little friend? And of course, we can use the fonts and the graphics and the touch screen and all that stuff. Because it just looks like the same kind of display whether it's an OLED or an SPI TFT. This just knows it's a gigantic 720 by 720 color display. So kind of neat. You just gave me a GIF and I dragged it on. And I used GIFIO to play it. And it worked great. All right. It's part of Aida for Daily. You can go to AidaforDaily.com. It's not connected to your store account. It's a completely separate account that way. You can be sure that you'll never get spam. We don't spam, we promise, but everyone lies. So you should just protect yourself. You should. Everyone lies. OK. Shucks.


My name is Sergio and I study in New York College at this on the Greenwich program for biomedical sciences It's a pretty interesting program a pretty interesting course. I really like it. I love the experience Greenwich has been like pretty amazing even better than I thought that I've always been interested in health and biomedical sciences Has enabled me to pursue my career in the health care field I would really recommend it to other people the teachers and the staff there which are really really helpful I always feel free to ask questions. They always come up with interesting answers the Library the e-library is very helpful. You have access to hundreds of thousands of books So you can basically learn whatever you want in your free time or everything like that You have access to that's one of the things I like the most the the reason why I decided to study in Athens It's first of all because it's closer to my country. It has very Important historical values. It's a really beautiful city and the school there was also very nice. The campuses are really Up to date the teaching experience the learning experience over there is very amazing and getting to know the Greek culture is one of the best things over there. I believe this course is one of the Biggest steps towards my future career the quality of learning is as good as in the UK while being over there in Greece


Welcome to MOOC course on Introduction to Proteogenomics. Today's lecture by Dr. David Fenu will talk about predictive analysis. He will provide a detailed information in supervised machine learning how it is linked with predictive analysis. Dr. Fenu will briefly discuss various parameters which are important for how to train a model, how to test a predictive model. He will also talk to us about how predictive analysis can be used in treatment of cancer, especially in taking decision of treatment strategies. Dr. Fenu will also talk about image classification and how the image classification could be used for the skin cancer diagnosis. So let us welcome Dr. David Fenu for today's lecture. Now we are going to talk about machine learning and specifically about predictive analysis. So you heard Mani's lecture where he talked about unsupervised and supervised machine learning. So what we are going to talk about today is purely supervised. So that means that you need a set of labeled data. So I mean Mani gave a very short introduction and I will plan to go a little bit deeper and give you more details about this. So what I would like to learn this morning is first of all how does one train a model. And there we are going to talk about gradient descent which is a way to a method, quite general method to find the parameters of the model. Then we are going to talk about regularization which is a method to protect us from overfitting and we will talk about all these terms in detail. The other thing we will talk about is feature selection. So one thing that in proteogenomics is that we measure a lot of things. We measure let us say tens of thousands of transcripts, maybe 10,000 proteins, maybe 30,000 phosphorylation sites so it is a lot of measurements on different molecules. But most of these will not be relevant to let us say predicting what happens to a tumor. And so what we want to do is focusing in on the important ones. And that is what why we do feature selection. So we select out the genes that are important to and closely related to what we want to predict. And so then we will just briefly touch upon that, but people have developed a lot of different methods to machine learning and a lot of different approaches on how to do this. So we will talk a little bit about how to choose the right method for the problem that you want to solve. So that is another thing that is quite important. Then the very important thing is that then after we have trained our model we need to test it. We need to evaluate how good it is and how well it generalizes. So that is and there we are going to talk about overfitting and underfitting. So I showed this slide in two days ago and so this is one example of predict the modeling. So when this for example the surgeon cuts out the primary tumor, we can we analyze the primary tumor that is a measure we do RNA-seq and proteomics. And then we want to from that measurement build the predictive model that can tell the oncologist which combination of drugs to give to cure the cancer. And this will of course depend on the both individual and the type of tumor that they have. So this just shows in one example in top the treatment A is what we want and but for the individual in the lower panel we want the treatment B works much better. And this and because currently as you probably know very well that is not the case I mean there is some standard of care that is given to everyone and it is only in a few specific cases where we can make this decision. But of course the hope is that by doing research and proteomics in the future we will be able to make these kinds of decisions in the more general way. So you probably read in the newspapers that machine learning has improved a lot. So people have been working on machine learning for several decades but the last few years it is really exploded and things work much better than it has in the past. So one thing that has been for example very successful is image classification. So the like Google and Facebook they have a lot of images so they have they put large efforts into automatically annotating these images and classifying them. And it is actually amazing how well it works. So and as you see these are just this is one big data set that is often used for this training. It is a very very large variety of images but also how easy it is to see what is in the images. So that is but this has really become so for several years they had competitions of who could develop the best algorithm to look at these images but now they have actually given up on image classification competitions because it works too well there is not worth doing much more. So they look at more complicated problems. But this is the general and of course this we can apply in our field and for example so there was a nature publication last year on the skin cancer diagnosis. So what the authors did was they had cell phone images of moles on people's skin and then they built had pathologies I mean dermatologists look at these images and classify them if they were benign or they were cancerous. And then they built so they and they collected a lot of images. So it was I think close to 130,000 and then built a model with that and then they could show that it actually their machine learning model worked better than at least average pathologist. So that is and that is quite incredible I mean you can imagine the implications if you can just you worried about some mole and you take a picture of it on your cell phone and upload it to some web service and then you get an answer back right away what with high accuracy. So another thing that has been very successful is teaching algorithms to play games. So quite a while ago chess game machines were became very good at playing chess and then for became better than any human but still for a while there if you had a collaboration between an algorithm and a person that was better than any algorithm on its own. But that is not the case anymore now the human does not add anything extra in chess. So and then more complicated games like go has also become and jeopardy. The advantage in games which we do not have in our case is that if you can have slightly different algorithms play against each other you can in general you can generate as much training data as you want because in our case we have a certain number of tumors that we analyze and if we analyze more tumors that is more expensive. So that is limited but in the game case it is only if you have large computers you can have the algorithms play against each other and learn from these playing. So it is in principle generating any amount of training data. So that is something that people are trying to do in produce genomics also but it is dangerous because there we have to then if we want to generate more data we have to model we have to build some kind of model how our data behaves and so that of course then the algorithm will probably mainly learn what we think the data looks like and not really not anything real. Another example that from the general thing is language translation. So this was in the New York Times two years ago. So this is a passage from Hemingways, Snows of Kilimanjaro and so one of them is the Hemingways original. The other one was a Japanese translation by an author from English to Japanese and then taking the Japanese translation and translating it back using Google translator. So which is which? Yeah, yeah so it's mainly the dead body of Leopard. That's the main and then there are maybe some other nuanced things but there's only one small grammatical error. So it's I mean this is quite amazing. So I'm just showing these general examples because as an inspiration that we should do the same for proteogenomics to be able to do these kinds of things. But of course as I said before the advantage in all of these cases both with image analysis translation and with games is that there is a very large data set that's been labeled. So that's really what we need. And unfortunately our data sets are usually limited and we would always want them to be larger to be able to achieve things like this. So let's look a little bit more at the details of supervised learning. So as Mani already mentioned we have two main things. One is regression, other one is classification. And so what supervised learning is is we build a very, we have a very general model with lots of parameters. We into this model there is no biological knowledge. It's just a very generic and we're going to look a little bit at what what is. So just generic model that can pretty much approximate any type of function. And then we want to learn the parameters that are that best fit that. So we're going to look first at regression and then what regression is is that we have some variables that we measure. We call them x usually. So in this case for illustration purposes we only show one x axis and then we want to predict what the value y is. So x would be for example the level of transcript that we measure with RNA-seq or the level of a phosphorylation site that we measure with my spectrometry. But with all data we have actually many measurements. So even though I only show one measurement you should always imagine that there are 10,000 axis or 100,000 axis. It's very difficult to imagine what that happens and also it methods that work on low dimensions become it things behave very different when you go to high dimensions. So what regression is is that we pretty much try to in this case when we have one x and one y we try to find the function that describes the relationship. It's quite straightforward that way and in classification we try to find the boundary between two classes. And so in this case the there are two measurements so x1 would be let's say the level of one protein, x2 the level of another protein and then we have for example the yellow circles could be that patients that have long survival and the black ones the patients that have short survival. And we want to find the boundary so we can classify when we've done the measurements will this we can answer the question will this patient survive for a short or a long time. And if we go back to the regression case there we would the y could be instead how many months will this patient survive. So we're going to start with linear regression and so here the axis so we can have many axis those are different measurements that we do quantitative measurements and then for each of them we have a different weight. And so what the output the y is is we take each measurement and multiply its weight and sum that up and add a constant. And then so that's you recognize that if we have just one x as the linear regression case. So now one thing to point out that we can also have an arbitrary function of x we don't just need we can we don't have to limit ourselves to just using x. But we can for example have a polynomial as a that has an input as x it's still linear regression because it's what linear regression it's linear in the in the w in the parameters that we are learning. So one thing is that we have limited data we can build these models arbitrary complex. So what we we can but we have to choose how complex to make them. So in this case if we have these data points it could be pretty reasonable to draw just have a linear regression a very not a very complex function and that could work well. But one could also have a much more complex function and then we would have the lower case. Can you please give a example of some particular data for the linear regression. Yeah so maybe the the output could be we want to predict how many months a person will survive. And then we the input would be the several different proteins the levels of them and then we can use that as the to predict and then what we want to learn are the weights. So the analysis is the rank will get us for how long that person will survive. Yes. So what will be x1 x2 x3 and so on. Oh so x1 yeah so x1 would be one protein x2 would be another protein and so on. So we would and then we are going to talk about some let us say we measure the levels of 10,000 proteins but that is a lot of parameters. So we can we probably do not have enough samples to support such a complex model. So we are going to talk about how to select which proteins are important a little bit later. So we have these two cases. So which one is right? Correct. Who wants to please who thinks the top one is correct? Please raise your hands. The answer is that there is no way to tell because you if you only have your training data there is no way to tell. I mean yes I agree the top one is more likely that is what we more would guess but it is just a guess. It is really we do not know. We need to collect more data and so we need to always train on one data set and then test our model on an independent data set. So let us say that we measure more data and these are now so the black ones are the same as before. So we have the grey ones or is our new independent test set. That is what we now we can say that we trust the linear regression. But for example if for some reason this would happen when we do our then we would choose the more complex model. But the main thing is that the it is really when you only have your training data set there is no way to tell how good it is and so that is probably the most important thing from my lecture here today. And another way to show this is here now we were on the this is the same data set that we those 12 points I think it was if we and we see that when we increase the degree of the polynomials so we increase the complexity we can make the error go down in this case to zero when we have the same degree polynomial as we have number of data points. So this is so one thing about the error that I am and you are probably familiar with this that we have to of course both for training and for testing choose a function that we in training minimize and then we in testing we use to evaluate. And for linear regression anyone remembers what we use as the this loss function. It is the sum you take for each data point the distance to the the line and then you square the error and then you take the sum of the square of the errors you remember that from high school maybe no no no sum of square errors. Not I mean usually not when you can take the average that does not matter but you can just it is really usually just the sum of the you take each error for each data point and then you sum them it's very it's simpler than that it's just taking each error taking the square and then adding them up that's the most common right it's that's the the sd so square deviation yeah you can do the mean but you don't even have to do the mean just the square deviation yeah so the sum of the square yeah you have to sum them but you don't have to take the mean yeah so it's very simple and you will you all know this I'm sure and so oh yeah so going back to here so we know that in a training set if we make our function complex we can minimize have the error go down to zero but of course this is meaningless because we just have made an overly complex fit to all the sort of noise that's in our training data and so because of this long time ago Johnathan Neumann said that if you give him four parameters he can fit an elephant to any data and and with five he can wiggle his trunk so meaning so what he meant was just this that if you train on if you evaluate your model with your training data that's not meaningful and of course this was a long time ago so he had much less data so he was worried about four parameters nowadays when people build deep learning model they have hundreds of thousands of parameters and worry much less than Johnathan Neumann I hope today you learned how supervised machine learning and regression and classification plays a role in predictive analysis. Dr. Fenni also showed how predictive analysis could help in skin cancer diagnosis and found to be superior when compared to pathology based diagnosis we also learned that how overfitting or underfitting plays an important role in model capacity finally we understood that the capacity of a model describes how complex a relationship it can model you could expect a model with higher capacity to be able to model more relationships between more variables then a model with a lower capacity in the next lecture Dr. David Fenni will talk more about predictive analysis giving more emphasis on training a model and testing a model thank you.


for a long time now reporters have had unconscious biases about indigenous peoples and other minority communities in this country. We all have unconscious bias and we have to start identifying that and recognizing that we're replicating tropes in our news coverage that have existed for a really long time in books and movies and our history texts in this country. We can talk about systems and the media industry but most of my colleagues care deeply about their stories. They're under pressure, they're under resource, they have deadlines, but they care deeply about their stories and they'll do anything to make their stories better. We just haven't equipped them with the kind of training that they need to understand indigenous communities and other minority communities that they might be unfamiliar with. I've been at this long enough that I remember the bleak and dark days when the few number of indigenous reporters that there were in this country had to fight long and hard just to get our stories on the air. So the underreporting has long been an issue and we're finally starting to see data that shows that news organizations are getting the message that audiences want more news about indigenous people, both indigenous audiences and non-indigenous audiences.


So, I am a playwright and I use spoken word poetry in my plays. And so this poem came from a play that I wrote about the police shootings between, the police shootings with the youth. And I couldn't really understand how a mother must feel when she loses her child to this incident. So I felt like a poem was the best way to capture that emotion. So this poem is called, Mother's Lament, Police Shootings. There's a dead corpse inside of me, like the baby bird fallen from his nest, fleeing for his life. Like the sea turtle, helpless on his back, plucked like the flower, innocent reaching toward life. Stripped from between my loins, they smashed my existence there. There you swam in a red pool and drowned in your stench.  My mouth is silenced by the screams inside my mind. Come, come unto me, all ye that labor and are heavy laden, and I will give you rest. Blessed, blessed are they that mourn. They shall be comforted of all the shattered pieces of my soul. They ripped the one sewn tightly to my heart, and spewed his life on the concrete like a freak show, grasping for life the way you emerged from my womb. After I kept you safe inside me, I bore you into death. Thank you.


We sold that one, man. You're too late.  Because your sign's still off that. How you doing? Tricky. I'm doing fine, thank you. Good, good. Yeah, I was just checking back with you to see, make sure I wasn't leaving you guys hanging in any kind of way. Nope, not at the present moment. Okay, okay, cool, cool. Yeah, I just want to make sure what nothing I could do for you or any of that good stuff. Cool. Well, that's all I was calling about. Make sure y'all are doing all right and see if there's anything I could do for you. Well, we're doing just great. Thank you for calling. Yeah. And we'll give you a call when we're ready to move forward. For sure. Just let me know. I'm here. Oh, hold on. Has a question. Okay. That if we listed it before the end of August that it closing, there would be a thousand dollar Yes. That you would give us towards closing. I, I did, yeah. Would you do that in September? Maybe. Maybe we'll list with you then. Yeah. Yeah, if you guys did list in September, I would honor it. He said yes. Okay, sounds good. Just don't tell anybody. Okay, we won't tell anybody. Okay. You know, we know so many people down there for 25 years, so he knows how to. He knows what's going on. Yes, he does. Okay, we'll be talking soon. Okay, thanks. Hey, buddy. Oh, not much. Just checking back with you to see if your people might want to make a run at that 1106. Like I told you before, the wife liked it. Her husband hated like how high up it was. He got kind of scared of it. So I don't know if he can get over that or not. I got you. Fluoride. But they would like it if they can get over that. And I haven't touched basically with this point. They were going to talk about it on the way back and we were going to get into it today. But really, your guy was going to be more understanding. My guy? Well, we haven't seen an offer at all. I'm sure he would. Yeah. Yeah, I told you yesterday he's motivated. Yeah. So that's not going to be the issue. The issue is are they, is your people going to, you know, move forward or not? Okay. And I'll get back with you. Okay. All right, buddy. Thanks, man. Hey, man. Pretty good. How about you? Good, man? Yeah, I just want to touch base and just tell you how mind blown I am that we hadn't sold your condo. We've shown it 28, 28 times. Wow. Yeah, 28 people have seen that condo. We've had good feedback and bad some, some kind of liked it and never did anything.  Some like Gulf Shores, you know, but there were some that said they thought it was price right and, you know, was a good unit. So I'm, I'm mind blown, but that's kind of how our market is. You know, it's kind of a, it's a weird market, you know, things that should have sold or some of them were kind of sitting there and, you know, things you think won't sell, sell. So anyway, it is, it is. I'm doing everything I can do though for you to try to make it happen. Yeah, they will. We'll sell it. I have no doubt about that. So all right. We'll just check it in with you, man. All right, buddy. Thanks, brother. How about a good one? Yes, sir. Okay. Yeah, a little Lagoon Cottage, the one on the, the one out there on the Lagoon. You want to buy that? It's got a good, got a good price on it.  We sold that one, man. You're too late. Oh, it's still right there at the top of the pylon. Yeah, that's what I was saying, at the top of the pylon. Yeah, it's still there, both of them. Yeah, both of them will sell that. All right. I'll go down there and get them. Let's see. No. Sure haven't. You're the first call, man. Where'd y'all come from? West Georgia. Yeah, yeah. Y'all ever been here? That's our first time. We used to go to, like, Destin or, hey, you're looking us here from all the YouTube videos. I have one of you coming. Yeah. How old is she? Eight months today. Eight months today, yeah. First baby. Hey, hey. How are you doing?  Give it a hand. Give it a hand. Give it to me. Give it a hand. Give it a hand. Give it a hand. Give it a hand. See all these guys are making calls right now. Oh, are they? I just sit here and watch them. They're all using my scripts and stuff. I don't think I've seen you do that before. What's that?  Yeah, it's nuts. Well, is there anything I can help you with, bro? I don't think so. We're good. Okay. So. Okay, script on time. Oh, yeah, yeah. Yep. They try to make calls every day, but time blocking. How long have you been selling? I had my license since 14, but really just full time into the past year. Yeah. So. It's going good? Yes, it's going really good. And your script and your programs really change a lot. Yeah. So it has. Yeah, for the people that really dive in and give it a chance. Yes. Yeah.  And I don't even look at it, you know, for a lot of people. But if I was charging them, you know, even more people keep going, at least I had more people being free. So they're going to lose summer. People think it's. Yeah. Oh, that's not a board video. That's the big thing in board. Yes. This is definitely what it's all about. Oh, really? The biggest one I ever did. That one there? Five sides of it. Yeah. Wow. Hey, there we go. It's okay. Do you recognize it? Gotcha. It's fun. Okay guys. So that's going to be it for the call session today. So you guys got to see me follow up with view buyers and mostly we're sellers I have listings with. So I have this sheet of my listings and I just kind of go down the list of my listings. I also have a system in MLS that shows me how many times it's been shown and feedback and stuff. So that gives me some data to kind of look at. And then I pull up the actual subdivision or condo complex that the listing is in to see if there's any recent activity I've missed so that I can be on top of the market there when I talk to the owners. And going through this process does so much for me because it keeps me on top of the market. It keeps me on top of the data of the listings. It tells me who's looked at it. If someone is thinking about doing something, if another agent has a client that's really interested, I can follow up with that agent and see where we are. It just keeps me on top of my inventory, the market, so on and so forth. So I try to do this every two weeks as often as I can, touch base with my sellers, see what's going on, see what's up with the market and just keep everybody informed. And you know, so it keeps me informed. It keeps them informed and it shows them that I'm working hard for them. And it just builds that relationship even deeper and deeper and deeper and that's what it's all about. It's not about the listing selling. It's about maintaining the relationship with the owner because regardless of the property sales or not, you're going to get more deals out of this relationship. So I'm not worried about the listing selling. I'm more so worried about maintaining the relationship. The listing will sell when it sells. I have no control over that. All I can do is all I can do and then let the process take care of the process. My job is to keep them informed and try to get more listings and help more people buy and sell properties. So thank you guys for tuning in. I hope you got a lot out of this real life experience following up with sellers and a couple other mixed calls in there. So thanks for stopping by tuning in. Leave me a comment. Hit the like button. That helps me get my message out to more agents who need to hear what I'm saying. And until the next video, let's go.


For accommodation, Booking has recorded bookings by fans from near 2,000 nationalities on the platform at www.kata 2022.qa, Saudi Arabia topped in the GCC countries and the U.S. in all over the world, in terms of making the highest number of bookings, said Marl Aljaba, executive director of housing department at the Supreme Committee for Delivery and Legacy. Speaking to Qatar TV recently, he said other top countries on the accommodation booking platform are Mexico, Argentina, the U.K., India and Brazil respectively. The official platform offers exciting range of accommodations, options including apartments, villas, close ship, hotel, fun villages and hotels. Speaking about booking accommodations out of official platform, he said fans holding much tickets can book accommodations through any platform.