We had a sleeper cell active in Pakistan at that point of raw agents and our raw agents were asked to go and locate this facility because it was a really top-secret mission that the Pakistanis were doing. It was absolutely illegal. This upcoming movie is of course Mission Majnu, starting Siddharth Malhotra. It's got a very interesting story. Amar ji, welcome to Candid Conversations. First thing really which I wanted to ask you was what made you choose this story? So first please call me Amar. I'm too young and despite the gray hair to be called any G or anything after that please. Thanks. So I mean I guess for all producers we're always looking for a good story. That's our starting point. All the films that I've worked on in the past that I've been associated with whether it's a K3, whether it's a Love Sonia, whether it's a Bajrangi Baijaan. It's always been the story which has been like that's like the thing that really gets us ticking and thinking. And that's I think also the USP of Mission Majnu along with of course this fabulous starrkast that we have. It's the story that really got us. It's set in the 1970s. It's about a great mission that our raw agents did in the heart of Pakistan. And I think it was important to tell the story because I think their bravery can be actually compared to our soldiers. The soldiers do it in uniform. Our raw agents do it without uniforms. And still this was slow enough. I think there are very few films that have told great stories about our raw agents. Razi was one of them. And I think Mission Majnu is another one. It tells a story about this mission that happened and that really changed the destiny of our two countries, India and Pakistan both. And just for the viewers, the ones who may not have seen the trailer or may not be convinced regarding the theme, if you could just give a sort of a short synopsis regarding what the movie is about. Obviously, I've seen the trailer. I've seen the teaser.  So the film is set between 1974 and 1977. We were just out of the Indopark 1971 war, which India won. And Pakistan was determined to make a nuclear bomb. And that was their only way in their thought process to kind of get any sort of control over India. And they started what was an illegal nuclear program at that time. And we had a sleeper cell active in Pakistan at that point of raw agents. And our raw agents were asked to go and locate this facility because it was a really top secret mission that the Pakistanis were doing. It was absolutely illegal. It was a mess. And I think for India it was really important at that time to stop this mission because otherwise the power in that in the sub region would have forever changed. And that's what this film is about, how they found this facility and what it meant for India and the agents of course. And how did you come across this story? Like how did probably the script writer or somebody else who ever came up with the script or the idea, how did they really pitch it to you? How did they come across it? How did you discover the story really? So actually all kudos to our writers. We have three of them. That is Sumit Bhattija, this Parveh Sheik and Asim Arora. And it was Asim and Parveh who actually came to us with just the story. It was because it was based on a real life incident. But they were the ones who brought it to us. And when I just heard the premise, I was like, you know, this is a great plot. And it really deserves to be told and deserves to be made. And I remember taking just the story to Siddharth Malhotra at that time. And he also loved it. And it was written jointly by Sumit, Asim and Parveh. Yeah. And I was seeing the trailer. Interesting. It was good to see Parveen Sethi after a long time. Yeah. Actually, yeah. Yeah, no, actually, it's, it's, there's a, there's a really good ensemble around sit in Rashmika. You know, whether it's Parveen Sethi, it's Sharya Bhashmi, there's Komod Mishra, there's Ashwad Bhatt. There's lots of really, really good actors, you know, around and all playing really important critical parts. Parveen Sethi obviously pays, you know, the head of raw at that time. So it's, yeah, we have a great ensemble around, around sit in Rashmika. And you know, I mean, I myself, when I was reading up about the story, I myself was not really aware of this. So, I mean, was it, was it really because it was such a secret mission? But since it was like over 50 years ago, still, you know, so little is known about it, or rather, maybe there is, of course, information about it. But how come the awareness about this issue is not there? People don't know much about it. So I think largely for two reasons. One is that it is a covert mission. You know, and unlike, I guess, missions of the army are, you know, they're documented a lot more, they've covered a lot more, but raw missions by the nature of who's doing the mission, which is an undercover agency, they don't really get documented that much. So which also means that, you know, we have to fictionalize parts of the film, because there are obviously lots of gaps in information. But the fact that this mission happened, and when you watch the film, you'll know that it was it was really genius what our what our raw agents did at that point, you know. So it's, it's, I mean, it's, it's just, it's just the nature of the beast. Yeah, that these missions are all very covert, they're quiet. They're under the radar. So they're not, we don't know enough about them. And actually, I think, but that's also joy, right? Because you discover all those years ago, how are agents, you know, they serve for this country. True, true. I think that's what makes it very interesting when you hear about a story for the first time, even in the internet age, where everybody seems to know, when you hear about something new, I never knew what mission was. And so there are like dozens of so many people out there who didn't know about it. One thing which I wanted to ask you, you know, because I've seen your, I've seen the, the kind of movies which you've done before, whether it's tubelight, or there's bhajrangi bhai jaan, there's mission majnu now, there are so many others really. I have always had this one particular view about producers. And I think I'm going to ask this one question to every producer who I interview. How do people face the stress?  I mean, that's like a very normal thing.  How do you guys deal with the stresses? I have to ask you, did you meditate?  And I think we're actually, I mean, I would consider myself really blessed that there are so many people wanting to make movies, there are so many stories that need to be told, and it's not easy to make all of this happen, right? And it's really, it takes, it takes really for, you know, for things to line up to actually get a movie made.  There were multiple delays because of that. And once you lose a location, you know, this being a period film, we had to do a lot of set work in Bombay, it never ends. It continues till the movie's out. But I guess it's also the joy of what you do.  I'll gladly pick up the joy that comes along the way, along with all the stress here. Okay, so it's not like you're going off to some Vipassana retreat every six months? No, no, no, no, not at all. Not at all. I'm gonna, I think it'll, like I said, I'm still young, I can still take a few years of that, of this beating that comes with it. Okay, okay.  Okay. Of course, you're, as far as mission majnu is going, you're going with Netflix. How do you weigh the pros and cons, the decision of should I release it in the theater, should I go for OTT, this film would be meant for theater, this would be meant for OTT, because also because of the pandemic, it has really, really affected the portfolio theaters. Right. I mean, okay, I have come back, but still it's that effect is there. No, sure. I don't think any of us can tell what movie will work in a movie theater or what movie will not work in the movie theater. The only honest call that you can take is whether the movie that you've made will find whether it's in a movie theater or on a platform. I think the greed for us honestly was that, you know, you're going to be in close to, you know, almost 200 countries on day in day. The day you go out on Netflix, it reaches 190 countries. I think that's a great opportunity for a film like mission majnu, you know, it can really reach to a much, much way wider audience. And I think that honestly was the opportunity for us. Netflix has been great to work with. They kind of, you know, they've really put their strength and might behind the film. And I think that I really do feel that mission majnu will find more love and a lot more love is the platform. True, true, because I think Netflix really exposes any film or series to a completely global audience. We have seen that in, I think, what was the name of that Korean series, I keep forgetting the name, but Squid Games, Squid Games is a very famous series which really make it global. But thanks for talking to us, Amar. And see, I've taken out the key like this interview. And best of luck for Mission Majnu and of course, all the other projects which you have lined up. Take care and all the best. Thank you. Thank you so much for talking to you. Thank you.


EuroPython as a conference would not exist if it wasn't for our gracious sponsors, right? And like EuroPython as a conference is built on the shoulders of the community, right? And one of the reasons why we have sessions like these is to, one, highlight our sponsors, and two, is to give opportunities to the community to effectively play a bigger role in these firms or just know a bit more about these sponsors and what kind of work they do, right? So what we're going to do now is for each and every sponsor, we're going to give them like five minutes each and they're going to effectively sort of present about what they are doing, what they do, and if they have any roles that they're actively hiring for, then they'll talk about that. We'll start off with Arm. So give it up for Leandro. Okay, well, thank you very much for showing up for the recruitment session. As I said, so my name is Leandro and I've been at Arm since 2016, so seven years and a half for us. We, well, we are the company behind Arm architecture, so it was in computer architecture and pretty much, well, all of us will have some device at home in our pockets that uses Arm technology. As a company, we are, well, about 6,300 people, more or less, so a lot of software development going on, a lot of Python going on, so we, well, we have an internal Python community, just, I mean, among employees and everything, we have about 900 people internally. So as a company, we are present in, well, many countries, so, I mean, we are here, you can visit us on the booth, on the Cambridge office or in the UK, but there are many offices in the EU, in the US, Asia, and lots of places. Again, well, as a company, I would say, so, I mean, from my own experience, so the company has a great culture, so it's a really kind of a nice collaborative atmosphere, a lot of, well, DEI conversations, so diversity, equity and inclusion is kind of a day-to-day on the company, on recruitment and all processes, so it's a very, I mean, again, from my own experience, it's a very welcoming environment and a great community to be part of. Well, there is a lot of content on that. I would, I mean, in practical terms, I would recommend you to, well, if you want to learn about day-to-day routines and everything, visit us at the booth. We are four people there, people that are stuffing the booth, are in the company for, well, more or less this time, so say, eight, ten years, more or less, so it's kind of a, we can give you an e-mail answer you might be looking for. So we, as a priority, the priority sort of the products for the company is IP development, so it means that we develop computer architecture specifications. But this is not successful if there is not a big kind of a software ecosystem running on top of it, so that's the key, it's key for the company, and that's, I mean, one of the reasons we are here sponsoring EuroPython. Just before coming here, we had a look on the careers website that I recommend you to visit. We looked and there are, well, 344 positions open in the company, and from those, 173, they mentioned Python in the job spec, so Python is a really big thing for us, and we contribute to it in, well, many levels from, you know, fostering the ecosystem and making it run better for the whole community, as well as internally, we use a lot of Python in our day-to-day routines. So yeah, so main message, if you are looking for a job, I would highly recommend you to go to our careers website. If you have any question, go and stop by, have a chat with us, grab some delicious jelly beans and stickers, and talk to us. We are very happy to talk to you there. Thank you very much. Okay, cool. Thank you. Thank you so much, Neandro, and I can indeed confirm that the jelly beans are quite nice. Had an entire box of it. All right, next up, we're going to have Alexis from Numbly to talk about Numbly, or not. So hello, everyone. Thanks for stopping by here. First of all, I want to thank all the other sponsors for being here. There are some faces that I recognize over the years, so thank you. Obviously, the organizers as well, because European needs sponsors, of course, but your time is what fuels this event and the community, so thank you for this. So I'm Alexis, the CTO of Numbly. That's a slide that represents what it is that we are supposed to use by our clients and useful to our clients. Basically, we are a data marketing company, so we help brands connect with their customers using data and all the digital channels available today. The reason for our presence and actually long-time presence, because this is the 10th year in a row that we sponsor and support EuroPyton. So I see, thank you. So yeah, we have a long-standing relationship with this event. That's a really special occasion for us that we are really happy to attend every year. For this 10th edition, we decided to come by and be the 10 of us, actually. So we have a pretty big number of people on our booth. So if you didn't, I encourage you to come by. We have stuff, anyway. So yeah, the idea is to have fun coming here and share our enthusiasm about the language and its community. If I go a bit technical, what defines us technically apart from our love for communities is that I like to say that tech is made by people. That's why I'm standing here right now. And we have a strong independent mindset and we are very proud of it. We value very much the skill set that people have in their minds and how they can apply it together. And yeah, we have special things related to independence and valuing European tech and the open internet that are things that are very dear to us as well. So we don't belong to only the Python community, we belong to many communities, I guess. So yeah, that's typical photos that we like to share internally. We have a strong commitment to open source, both as a company and also a lot of individuals that are working at Numberly have a strong relationship and strong involvement with various open source projects, they can be in Python or not, actually. So this is our webpage. We publish ourselves some open source projects. We sponsor open source communities and individuals for the projects that we use in production. So we try also to give back to individuals financially if they allow it. But what is mandatory when working at Numberly is to contribute back to what we use. So being an actor of the tech ecosystem is really, really important and is actually part of your job. So that's not something that we expect you to just do on your free time, right? Beyond this presentation, we have a tech blog that is there. Me being here is just words from someone you don't know. So everyone of us will try to put great lights on their company and bring out what they believe and they live through, hopefully. But I think what talks best is what other people say of you. And that's tangible evidence in my opinion. So that's why I allowed myself to copy past this from the Internet. And this involvement has also been rewarded multiple times for some tech achievements. So we are very proud of it and share also a lot internally about it. So yeah, we are obviously hiring a lot of different kind of positions because we apply Python and a lot of other technologies to a wide range of use cases. That's the luck that you have when you work with data and a lot of various data. So come by and at least say hello. Thank you. You want me? No. Super. Okay. Thank you so much, Alexi. All right. Next up, we're going to have JetBrains and we're going to have Jody and Helen do some cool stuff. Yeah. Getting everything set up. Thank you. So hi. I'm Jody. Hi. I'm Helen. And obviously we're from JetBrains. Got a nice double team today. So initially we were actually going to use this slot for recruitment. I'm very happy to say we actually had a candidate sign the offer for our open position yesterday. So we are very happy. We're very sad to not still be recruiting because many great candidates. So what we're actually going to do is use this slot to demo one of our features that's upcoming that we're most excited about. First thing I just want to say for JetBrains also the Python community is super, super important. PyCharm is one of our most important products. We're really proud to be here and be part of the Python community. And yeah, it's always sort of great to be here and we're really proud to be sponsoring this conference. So the feature I want to show you today, I'm going to be brave and do a live demo, is our new AI Assistant. So if you want to hear all the details and hear me go into tedious depth about large language models, please come for a demo at the booth. And I'm going to show you a few of my favorite features. So basically the AI Assistant is our attempt to integrate the, I want to say intelligent, sorry I'm a data scientist, the utility of large language models into our IDE in a useful way. So this is coming up in our next release in the next couple of weeks. You can access the beta now as part of our EAP. Again come and see me at the booth if you want to know how to do this. But I want to show you a couple of the cool things it can do. So here's some code and Helen wrote this code and what has she done? It's written a function without a doc string. I could write the doc string for her, but instead I could get AI Assistant to do it. Like magic. But it's not magic. It's a statistical machine. She's also, she's called it my funk. What is with you today? I don't know. What is with you and your code? Rename? Naming so hard, but with AI Assistant, it's easy. Let's do something even more fun. Let's say, I don't know, we want to ask the large language model to do something a little more complex. What do you want me to ask chat GPT to do with this function? Do you want tests? Do you want me to break it down into something else? Shout out. What was the suggestion? Okay. Please. Let's be nice. Optimize. This function. And it will go ahead and do that for us. Of course, we're using a large language model. Sometimes the code may not be optimal. This is an assistant. This is not to replace you as a programmer. This is also just the beginning for us. This is a priority for us going forward. Please come along, have a chat about how you can get access to this. This is a limited beta. Because we will be paying the bills to open AI. But if you want to get access to this, please come and see us at the booth. And you may have also noticed this doesn't look like the pie charm you might remember. I shall have this lovely microphone. Yes. I just wanted to give you a very speedy tour of the new UI. It is here. It is landed. If you are a new user, when you open pie charm, you will see it by default. If you are an existing user, you need to see it. If you are an existing user, you need to switch it on. Because we didn't want to switch it on for you. Everything has changed. To switch it on, you can just head over to your settings. In Mac, you can go here or command and comma. And look, it's already there. But just search your settings for new UI. You can enable it here. I really like compact mode. It takes away some of the white space. Just a little bit more code aware, more code space. And just have a play around with it. Again, because Jodi has already said this is the EAP that we are using. So there are some new features coming for this in a couple of weeks, including my favorite. You can now customize your toolbar. You can add your frequently used actions to the toolbar. You can change the toolbar color. And you might think that's not cool. But if you have multiple projects running and you are quickly flicking between them, a nice visual indicator is super helpful. So the new UI, it's here. Go check it out. Have a play. Back in your settings, you can submit feedback as well. Let us know what you like. Let us know what you don't like. Because all feedback is super, super important for us. If you do want to, I'm going to take Jodi's thunder now. If you do want to have a play with this, you do need the EAP to get some of the latest features so that the new UI will be in. It's been in PyCharm for a while now. Just not switched on. But if you do want to know how to download the EAP and get it all working, then come and see Jodi at the booth tomorrow.  All day. And should be doing AI assistant demos. All day. We don't give her any breaks. Except the one break I take to do my quiz. Oh, yes. So your quiz. I'm not going to trust myself to get the times right, because if I get them wrong, she'll shout at me about that as well as my code. So Jodi, what time of the quiz? Yeah, so we're doing quizzes at 10.30 and 1.15 every day. If you don't have a PyCharm professional license, top three scores in every quiz, win a PyCharm professional license and a book. I love the quizzes. I just thought I'd use this spotlight to advertise them. They're a lot of fun. There's nothing to do with PyCharm. And that's it from us. Thank you. Thanks. Thank you very much. Now, next up, we'll have Optiver. Hello, everyone. My name is Joost Lek. And first of all, I'd like to thank the European organization for having us here and for giving us the honor of being a sponsor of the event. Today, I'd like to tell you a little bit about our company and the roles we have available. So we're Optiver. We're a market-making firm specifically focusing on options trading. That's a lot to unpack, so I'll try and explain a little bit of what's going on in that sentence. We're trying to make markets. That means that we provide both a bid and an offer for specific financial instruments. And by doing that and doing it better than everybody else, we hope that people trade with us as they're doing that. We're not trying to build a big position, but instead we're trying to be there for market participants, trade with them, and at the end of the day, keep basically the difference between that bid and offer as a spread, as it's called. And that's what we use to make our money. We do that on electronic markets throughout Europe from our Amsterdam office. We also have an office in Chicago, and we also have one in Sydney where we do the American and Asia-Pacific markets. So from our Amsterdam office with about 700 people, we service a lot of the major European options markets. A lot of those are in London, but also Frankfurt, Stockholm. All in all, we manage equipment at about 12 exchange locations throughout Europe. So we use our own equipment to connect to exchanges. We have our own network department that manages our internal network between all of these locations throughout Europe and connections to our partner locations in America and Australia. I'm part of the infrastructure team at Optiver, so that means that I focus a lot on hardware deployments, on specifically ensuring that we have low-latency trading systems available. We have other departments at Optiver as well, and a lot of those use Python. Out of the three main technologies we use, C++, C-Sharp, and Python, Python is the one that I'm most familiar with as most of the engineers on site here, as you'll know that understand. The main use cases for Python within our company are data analysis and data processing. We gather a lot of data, what's going on in electronic markets, and we process that data to gather analytics. But also infrastructure management, both monitoring and deployment, are mostly in-house built, mostly Python-based, and there is a lot of trader automation nowadays. The new generation of traders that are coming out of university are actually used to using Python to process data, and so they want to automate their own workflow, they want to provide their own analytics, and we're doing that by providing them a Python platform to do that safely. So that's a little bit about us, and some of the details about the roles we have available. I'm not going to dive into each and every role here, but what I think is good to focus on is we have a lot of roles in infrastructure where we're focusing on automation, so those are roles where people focus nearly full-time on writing Python code that manages our deployment and our own infrastructure, and it's really an area where people can add value to the business with their Python knowledge and experience. You can scan the QR code and figure out more if you'd like to. Also, we have a booth here, so please come and say hi at our booth. I recognize Nicolas de Marchi on the picture who presented earlier today about infrastructure as code as part of our program, and the other picture is from me. Our recruiters are sitting over there, Theo and Bruna, so you might see them in the booth as well, and that's it from me. I'd like to thank you very much. Thank you very much. Next up, we're going to have Kraken. Let's give them a big hand. Just so you know who we are. Just in case. Nice. I haven't prepared too much, but hopefully most of you know a bit about what we do already. If not, we are a software as a service platform in the energy space, specifically promoting green energy as much as possible. We license our software out all across the world, so we've really grown a lot over the last few years. We're now actually in 14 different countries, and we have over 25 million energy accounts on our software now. Our aim is actually to reach 100 million by 2027, which sounds very ambitious, I know, but the trajectory we've had over the last couple of years, it's definitely on the cards, fingers crossed. We have our main hub in London in the UK, but we also have a number of different offices now across Europe, which is why we're here. Our platform is built primarily using Python and Django. Our tech team across the world is 500 engineers, of which probably about half of them are Python developers using Python and Django. We have open roles in all of the countries and cities you can see here. We also hire data engineers, data scientists, and machine learning engineers, all of whom use Python in various different ways. If anyone interested in large-language models that you guys are speaking about, we also work with them. We also have... It's slightly confusing because we're quite a big company, but we also have a Krakenflex software platform, which is a little bit different. It's a software that essentially helps us to control and manage energy assets, like solar panels, wind turbines, wind farms, and also electric vehicles and large-scale battery storage. So that's our team up here in Manchester in the UK, but we also have, again, offices in Turkey, Switzerland, Japan, and the US. That team uses slightly different tech, but they do still use Python. So, again, we're hiring for quite a lot of roles. I think we've got some very ambitious growth plans, so I'm not sure exactly how many open roles we have at the moment, but it's definitely... Yeah, it's more than 100, I think, so there's lots. And, yeah, I'm sure you guys can come over and ask us any more specific questions that you have about the company or about our hiring or anything like that. Please do come and say hi. And I brought some of these because we've been running out of them every day, and everyone seems to ask for them, so if anyone would like one, yeah? Here we go. It's always fun, isn't it? Ooh, that was going to the person behind you, but it's fine. I've got one more. I'm sorry, I'm seeing a hand. But come to our booth, we have got more. Yeah, this is probably what attracts people the most, but, yeah, we do have open roles, so check out our careers page as well. And I did want to say we are not the cryptocurrency company that's also called Kraken, so if you Google Kraken and you see that one, just keep scrolling with the tech company in the energy space, so, yeah, that's us. Yeah, thank you, and also thanks, obviously, to the organizers and to everyone for coming. Yeah, it's been a really great event. I think it's our second time sponsoring, so it definitely won't be the last. So, yeah, we'll hopefully see you all next year. Thanks, guys. Thank you very much. I was just telling Jodi that I have, like, six of these octopus at home. Every time I go to a god-business grappler. But, yeah, so last but not the least, we'll have Temporal joining us and tell us about themselves. Hi. So I did not understand the assignment and I did not have slides, so thank you so much for someone putting up the logo. Woo! So, hi. My name is Jessica West. I head up developer relations and community at Temporal. So, kind of in my title, community is incredibly important to me and also with the company that I work for. We actually, I heard of your Python I attended in 2018 and was just amazed by the community there, and I was like, this is amazing. I don't know what company I go to. We will be here. We will be sponsoring. So I'm so pleased to be at a different company and back here, and we're the first, this is Temporal's first conference here, and we've just had an amazing time. I can't, I cannot say enough about the community that you brought here together and continue to build. Everybody's so curious and so kind and lovely, and we've just had an enormous amount of conversations. So, one, I just wanted to give a shout out to the organizers of volunteers for bringing this all together. Yeah, cannot say enough good things. So, that being said, so Temporal, we are on the other side of the hall. I haven't had to history leave the booth, unfortunately I didn't even know this was hall, it was down here, but Temporal is kind of like a workflow as code. There's a very big discussion point, so if you want to hear more, come talk to me at the booth. Unfortunately, we aren't hiring in Europe right now for any engineering positions, but we have some engineering positions open in Seattle and I believe in New York, so anybody that's interested in US based or wants to go to the US for whatever reason, you can be over there. But we are a distributed company, so we're working on expanding that engineering base there. I personally split my time between London and Seattle because I like the rain. And the rain likes me, so it works out that way. So, a few other things about Temporal. So, again, community is incredibly important to us. Our founders actually have been working on our open source product for the last 10 years or so, and then made it a real company, is what I like to call it, in 2019. And then we've been working on, we've got our cloud offering now as of last year, which allows us to be here and spending more money on conferences and going out and reaching out to the language communities. So, with that, we just released our Python SDK. Yay! And it went into public preview in January and February this year. And we've seen some great adoption and some really good engagement, but we can be better. So, I'd always want to hear from the Python community about what we can be doing better. Is there anything else you want to see? And is there some cool ways or are you using it that maybe we didn't think about so that we can bring that together? So, it is all open source, so that means you can use it for free, MIT license. And we really encourage everybody to go and check it out and let us know what you think. My team's job is to make sure that this is really easy to use out of the gate, like there's no gotchas. And so, if you find that when you have a gotcha, you know, let us know on Slack, Twitter, Discord, really, like, anywhere on the Internet, send smoke signals, we'll probably find them. But really, for our sponsorship here, we just wanted to engage the community a bit more and show you our investment into Python community and specifically EuroPython. So, with that, we're really looking forward to coming back next year. I've already been talking through all the details and working through Event Recap. We'll be here all day tomorrow. And with that, we do have a raffle we're doing. We've been doing one each day. We have one item left, which is a Baby Yoda Lego set. It's going to be the best one. So, for anybody that wants to enter, come over to scan and enter this form or I've got my phone that I can scan your badge if you don't feel like typing anything in. And we are out of t-shirts, unfortunately.   But we'll be sending out an email next week with any links to those as well. So, again, our goal was just to get to know you all and say hi and let you know that we are excited to be a part of that. So, let me know if you have any questions. Thank you. Brilliant. This brings us to the end of this session. I would like to take a minute and thank all of our sponsors. Thank you so much for taking out the time. Just like talking about your companies, the available roles and everything. We love you and we know that you love us. Thank you very much.


Hello friends, I am Sanjay Bhokta. I welcome you on Sanjay Bhokta Tech School. In this video, I am going to tell you how you can correct algorithm for the empty transpose of a matrix. So, I am going to start writing the algorithm. So, the name of the algorithm will be Transpose. It will be receiving three parameters. So, first is A that will be a 2D array. Second is number of rows that are applicable in that 2D array and C means number of columns that are applicable in that 2D array. Now, step number one. So, start. Then, step number two. So, from step number two, we will start to do. So, I am writing the P, I, so it will be for I equals to 1 to row. Then, one more loop will be required so that we can iterate for the columns. So, the P for J equals to 1 to column. And here we can write print A of J. So, normally, whenever we print a matrix for the use A of IJ, but in case of transpose, we need to print rows in column heights or you can say column in row heights. So, for that purpose, we need a J I location. So, first we need to use column index and then we need to do it. Then here you can write end of loop. So, this is for this loop. Then again you need to write end of loop so that you can mark this is for this loop. So, here both the loops are ended and then you can write step number three matrix N. Now, let's take the matrix. These are values. Name of matrix is A and number of columns are three and number of columns are three. So, here you can see I is starting from one. So, initially I is one and controls will be transferred to J. So, J is also one. R and C both are 3D. So, first time we will be printing A of 1, 1 position. So, A of 1, 1 position is this. So, remember one thing, we are discussing algorithm. So, in case of algorithm, we consider indexes are starting from one, not from zero. Because whenever you will be implementing this algorithm with any programming address, then you need to consider that indexes are starting from two. But in case of algorithm, indexes are also starting from one only. So, A of 1, 1 position means one will be printed as output. So, it will be printed. Then J will be implemented. So, value of J will get two. So, here I implemented algorithm to all. So, here you can see J is two. So, next time two, one position will be printed. So, two, one position is this, four. So, it will be printed. Then, value of J will be one, three. So, it will print J, three, one. So, three, one is this, seven. So, this way you can see a column is printed. So, first column is printed. Then, second column will be printed. So, this way, through changing this Aij, Aji, you can print your matrix transpose. So, this is a simple algorithm to which you can print transpose or matrix or 2Di. So, this is an algorithm. If you want to watch its program like how it will be implemented in three languages. So, for that, you need to go to description of this video. Then you will find a link of playlist that has data structure you can see. So, you can follow that playlist. So, here I have uploaded all the videos. Those will be implemented algorithm with the help of C, language. So, who was that? And I hope you understood how we can print transpose or matrix and how we can write their algorithm with the help of this video. Thank you for watching this video.


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where do you see private training going in the U.S. in the next two to five years? Yeah, that's a good question. I could definitely see it growing and becoming more solidified, like not just with goalkeeping, because goalkeeper training has always been there because, especially in America, clubs, even club soccer, they don't have dedicated goalkeeper coaches. You know, some of them do, some of them don't, some of them have goalkeeper coaches that are there once every two weeks or whatever, and it's just not enough. Goalkeepers need the extra training, but I can totally see how personal training for all positions and other sports is growing because I think people are starting to realize they need individual attention, especially just with the way our society is nowadays. With social media and everything, everyone needs their attention. They need on them, and I think that's going to translate to sports, and they're going to want the individualized attention to help them grow their skills and maximize their potential, and I think they'll see the benefits of that. So, yeah, I definitely think it's going to grow and become much more common.


Regration analysis basically draws a line through the data, and the line is defined by regression coefficients or the betas in the model. Our task now is to figure out how we estimate those betas. So we give regression analysis some data of the dependent variable and one or more independent variables, and the regression analysis tells us where the best line goes and the line is defined by the betas. So how does the analysis know which line is the best? To answer that question, we'll be looking at some example data. This same data set is used in one of the assignments and it comes from the census of Canada from the early 70s. The data set is called prestige and we have the observations here are occupations, there are 102 of those. We have data for the education, which is the mean number of years that occupants of that occupation hold, what's the average income for that occupation, how many women there are from 0 to 100% in that occupation. What is the prestige score that is defined in some way that we don't really care about? We have census code, which is an identifier that we don't need, and we have type, which is a categorical variable, that can be the white color, blue color or professional. Then there's some information about where the data comes from and this is a print out from the R-package, C-A-R documentation that contains this data set. So we will be doing a regression estimation and our task is to explain prestige with education. So how much the prestige of an occupation depends on the amount of education in years that is required for that occupation. So our regression model, we said that the prestige is a weighted sum of beta 0, the intercept or the base level for people with no education or professionals with no education, beta 1, which is the effect of education plus some variation that the model doesn't explain, the error term U. Our task is to estimate beta 0 and beta 1, which define the regression line. Estimets in statistical analysis are usually denoted by growing this kind of carrot or hat symbol over the beta. So this is beta hat 0, this is beta hat 1, so they are estimates of these population regression models. Hat denotes that we don't know a value, but we have calculated the value from a sample and that serves as an estimate for what is the relationship in the population. Now we need to have a rule to set the line. So we have drawn the regression line here and the regression line should go through the middle of the observations, so that they are about the same amount of observations about the line and below the line, and the observations also are assumed to be normally distributed around the line, so that most observations are clustered closer around the line and some are further from the line. Telling a person to draw a line in the middle is easy and a person will probably draw a line like that, but you can't tell a computer to draw a line in the middle because in the middle is not well defined. You have to have a specific rule on how to draw the line and that specific rule of estimation is called an estimator. So estimator is any rule or strategy or principle that we can apply to calculate values for the quantities of interest from a sample data. So let's take a look at what are some properties of good estimators. We covered this in another video before, but let's revise. So we need to have estimates that are consistent. Consistency means that when we have the full population data, our estimates beta hat zero and beta hat one are equal to beta zero and beta one. So these estimates will be the population values. In other words, if we have the full data, a consistent estimator gives us the correct result for that population. Then we have another important, which is unbiasedness, another important property. Unbiasedness means that even if we don't have the full data set or large sample, our estimates will be correct on average if we repeat the study over and over. So the estimates are correct on average is unbiasedness. Then we have efficiency, which means that the estimates are more precise or more accurate than any possible alternative estimator. So efficiency is a property that we can use to compare to estimators that are unbiased. Finally, the repeated estimates from repeated samples should be normally distributed or at least follow a known distribution. That is important for statistical inference or calculating the p-values. One really good rule for estimating the regression model, actually the best rule is to use the residuals. When we have a regression line here, we can see that the observations are not exactly on the line.  So this is the amount of income that would be predicted based on your education level. Then the difference between the actual income and the predicted income by the model is called residual. So that is the part that the model doesn't explain of the dependent variable. So we can calculate this regression line by plugging in our estimate for beta 0 and beta 1 multiplied by education. So that gives the line and then whatever remains, what's the difference between the line and the observation is the residual. So the great or best rule for estimating this regression model is to set the line so that the sum of these residuals raised to the second power is as small as possible. So how we do it in practice, we set the line somewhere. We calculate residuals for each observation. We raise the residual to the second power, we take a sum and then we try different values for the betas to make the sum of squared residuals as small as possible. This is called the ordinary least squares estimator and it has been proven to be consistent, unbiased and efficient. And it produces normally distributed estimates under some assumptions that we will cover later.