Hello, and welcome to the Virtual AI Summit in New York. My name is Marius Bogovici. I'm a Chief Solutions Architect of Financial Services team at Red Hat. And in this session, I will share Red Hat's experience of helping financial institutions build applications, and in particular, discuss several strategies to help you maximize the return on your investment in the artificial intelligence and machine learning. It is important to understand the driver for these changes because financial institutions have been early adopters of AI, and it's not a new topic for them. Existing data has been used for a long time to improve existing products and to devise algorithms that power specific bank functions, especially on the back end, especially in the capital markets area or risk assessment or even fraud. What is really changing is that banks in financial institutions find new ways to increase the efficiency of their operations. Aspects like just-in-time landing, automated compliance and reporting, improved loan default prediction, just make the bank operations faster, quicker, better, and they help their customers with the same products but working so much better. At the other end of the spectrum, there's experimentation. Financial institutions create new business models based on intelligent decision-making and insights derived from data, personalized incentives and rewards, client advisories, personalized insights for investment. So this way, they can create new value on a new business and serve their customers better through new products. As a combination of the two, AI becomes gradually interwoven in all the areas of operation and becomes an integral part of financial institutions function in areas like payments or omni-channel. So as it becomes a core functionality, functions related to AI start being shared across or outside the organization. Thinking about prediction as a service, thinking about sharing the expertise of building AI systems as a center of excellence. So this UB security of AI is also something that AI leaders have to take into account. All this is supported by a number of emerging technological trends that help support implementing these AI solutions. Commodity hardware provides simpler and more cost-efficient ways to get the compute power that you need.  Specialized hardware for AI ML also helps accelerate innovation. And usually the solutions that we see built are a combination of the two commodity and specialized. Cloud platform, on the other hand, provide the means for elastically scaling and allocating resources on demand for resource-intensive workloads without the need for the massive investment, for example, for building a larger data center that is largely un-utilized. And open source offers companies freedom to innovate and to tap into the power of communities to create new and better tools to implement an analytics and machine learning more close. And in fact, the majority of these new tools, the majority of the emerging software in the machine learning space that's powering AI initiatives comes from the open source environment and allows a great degree of flexibility to pick and choose what they need to implement what they need. So armed with these new capabilities, they can implement solutions that meet certain business viability objectives like agility, solutions but must allow for easy and rapid change to deal with changes in market conditions or consumer preferences or just try out new things. They must be deployed to scale. As I mentioned earlier, these solutions tend to become ubiquitous. So once they're deployed in environments that, for example, are part of interaction channels like web and mobile and deal with an increased number of users, they also need to be able to scale with increased usage. And when these functions, for example, become supporting functions for the entire enterprise, again, they're facing this increase in usage which demands this scalability. And finally, companies must balance the value that they create with the cost of implementing and operating these new solutions. So cost effectiveness is a big part of the story. Now, financial institutions also must meet specific standards and obligations which are kind of the fourth driving factor when we discuss these solutions. They must have the, they must be solutions must be secure. And it is important to notice that machine learning models and training data become part of the attack surface of the solution, compromising a fraud detection model, for example, can create a great loss at the bank, especially if it goes undetected. Reliability is important. Being able to run these large scale workloads is a critical part of actually building machine learning models, but also once they're deployed, these models must be highly available as they become critical business functions. And deploying your versions to production must be performed with minimal downtime, ideally no downtime. Finally, AIML provides solutions to complex regulatory requirements and are subject to regulatory scrutiny. So they must comply with, they must meet certain security criteria as we've seen, but also comply with data access and privacy regulations and meet the transparency criteria set by regulatory. What we'll talk about next is how Red Hat sees and how Red Hat experience of helping financial institutions deal with these four driving forces and implement platforms that allow them to take advantage of the business opportunities while leveraging technical advances in an environment that means the viability criteria and also is compatible with the regulatory requirements in which they have to function. So in order to move forward, it is often noticed that implementing AI solutions in financial institutions faces a number of challenges which create and always create a, which make put into question the return of investment of such solutions, because they're typically seen as requiring a lot of resources, requiring a lot of investment and very often not delivering on the promise. And in implementing such solutions, the AI leaders and their teams must clear a number of hurdles. The first one is data access. Finding and preparing quality data is difficult, especially when regulatory constraints restrict data access. So this is something that companies have to consider a worker app, but also finding talent and using efficiently that talent, especially when teams do not collaborate well with each other is another problem. So solutions are very often hard to implement due to slow manual and side load operations. And this leads to models, for example, becoming outdated by the time they reach production. So they do not deliver on their business value, accelerating that process is a critical part here. This happens very often due to either the unavailability of infrastructure of software for data scientists, for example, to conduct their experiments or to find the resources that developers need to incorporate these models into production. And ultimately making sure that all these processes are transparent and easy to assess by regulators and address ethical concerns such as bias. The observation here, and this is a diagram from a well-known paper on machine learning and its impact in building applications is that the building the model and training the model is just a small part of building an intelligent applications. Usually there is a much larger, a model is part of a much larger distributed solution that incorporates a number of supporting activities and tasks. And this can be easily seen in this diagram that illustrates a solution for building an anti-money laundering architecture. It is RedHats architecture for building for anti-money laundering that illustrates how artificial intelligence and machine learning can be combined with other functionalities in a bank in a modular event-driven fashion that leverages data from a variety of data sources to produce, to build a cohesive holistic solution as opposed to, let's say, a monolithic Antoine one-size-fits-all type of approach. But what's important here is that while the anti-money laundering that the machine learning capabilities are a critical part of supporting functionality such as know your customer or transaction monitoring, it needs to be integrated in a larger landscape where for other aspects like data integration, case management or analytics exist. So that is essentially a requirement, translates into a requirement for collaboration between different teams that are part of this process. Data engineers, data scientists, and application developers, data engineers essentially are more focused on collecting the data, transforming and preparing it. For data scientists who analyze, extract the features, build models and monitor them. And then application developers are using these models to build intelligent applications, to build the business solution around them, interfaces, APIs, management tools. So all these teams must work with each other seamlessly. And what we see in typical machine learning workflows, especially when it comes down to getting the work of data scientists up to production, this is a classic workflow for preferring machine learning tasks. We typically see two big bottlenecks in this process. One deals with building discovery environments. Simply giving data scientists access to tools, to the tools that they need, to the libraries that they need, is without the need to file IT tickets is usually a problem. So moving from the mindset of everyone does their work on their own laptop, and then somehow shares their results to a mindset where they have a platform which they collaborate is a big change here. And the other one, of course, me is concerned with moving these models into production. Just kind of throwing notebooks over the wall is not a recipe for success. So integrated build pipelines that take the results of the earlier group and translate it into seamlessly working services, I think is an important part. And again, these kind of bottlenecks, the solutions here share a number of common traits. There is a need to have reproducible environments. What worked on my machine needs to work in the cloud, needs to work in the public cloud as well, or in my data center, so a different environment. All of them need access to specialized hardware. In the case, for example, of production deployments, they're also a requirement to monitor against degradation and drift. So what's the solution for addressing these pain points? How do we see this happening and how financial institutions have been successfully implementing such workflows? Essentially building a single workflow that unites the multiple personas, recognizes the needs, their different needs, and provides self-service access to tools and data, and minimizes the burden of the IT operations, people that have to support it. And the way this workflow takes place is through this conceptual architecture for operationalizing AI that we are proposing. And we have used for successfully helping customers build and accelerate the AI initiatives, as you will see later. At the top, essentially at the top of this architecture, you see the project lifecycle with a business goals, like going from the setting of the business goals to developing the machine learning model, to implementing applications and inference, and monitoring and retraining models as needed. In order to execute this process, in order to support it, you need a machine learning software tool chain that consists of specialized libraries, tools like TensorFlow and Spark, and Jupyter Notebooks, Python, Selden, a few other. And you can recognize that all of them are essentially open source tools. So this is kind of coming back to the conversation on the power of open source as a catalyst for these types of environments. So how do you get access to them and use it in a seamless fashion is the key question. Now, also there is a need to have these access to a variety of data sources, databases, SQL, NoSQL, data lakes, data pipelines that provide the data for the data scientists to transform and later on, provide the access to data for inference. So all of them, there is important, like having a single platform on which these two, these components can run side by side is critical. So the platform here is essentially the key component that enables the seamless operation of these data pipelines and tools is the hybrid cloud platform, is a hybrid cloud platform with social service capabilities based on containers, Kubernetes, and with embedded DevOps practices. I should also add security here, which is like baking in security as part of the platform is a key way to address the regulatory constraints that financial institutions require. Now, this hybrid cloud platform also provides an abstraction layer for simplifying access to hardware accelerators, right? Like GPUs that help speed up model development and inferencing tasks. And the role of the platform is to make this access to these resources transparent, just as it is to make access to infrastructure as transparent. So offering the consistent experience across on premises, public clouds, edge locations, and can be efficiently managed by the operations provides a seamless experience and provides this flexibility goal of being able to deploy your models, being able to deploy your workflows whenever you need it in the way that you need it. As I mentioned earlier, one of the foundational blocks of this hybrid cloud platforms is containers that provide the agility, flexibility, portability, and scalability for data scientists to build and develop models and for developers to code software applications powered by these models. So of course, deploying and creating, deploying new applications and getting access for resources requires them to be agile. And not waiting, for example, for a ticket to IT in order to deploy the task. Also containers means, containerization means that applications with different profiles of a different nature, like your job application that access is a model and for example, the Python application that embeds the model, run the same way on the platform. Those will provide portability. So the ability to share and deploy models consistently across in the different target environments, the ability to provision environments as needed and when needed. And finally, being able to aspects like scalability and self-healing and high availability means that this Kubernetes provides this natively for containerized applications. So, and we can easily scale your applications in a declarative fashion as managed by Kubernetes. So this is essentially captured in the, in Red Hat's Kubernetes powered Red Hat OpenShift container platform that is the foundational tool that helps accelerating the ML lifecycle. So OpenShift, of course, provides, simplifies the deployment, scaling and lifecycle management of containerized ML tools. And that, like HDOAI, Starburst Presto, Seldin and ensures high availability and faster time to value and also provides access, as I mentioned earlier, to hardware accelerators like NVIDIA GPUs. So it means that it acts as the glue between this specialized software and the underlying capabilities. It also provides consistency and portability, offering consistency of day one and day two operations across the data center, edge and public clouds. So machine learning and application development workflows are portable across different environments. And also it extends the value of DevOps to the entire machine learning lifecycle, enabling collaboration between different teams. Finally, it is a hybrid, it's a fully integrated hybrid cloud platform that includes key capabilities like monitoring, automation, DevOps dual chain, completely built on open source projects. This helps drive innovation without vendor walk-in. Now, to illustrate this and to illustrate how this happens in practice, I'm using a case study based on a successful implementation of any platform at the Royal Bank of Canada. The Royal Bank of Canada is a top global bank. It handles massive amounts of data, easily 10 billion clients in directions per month. It has identified artificial intelligence and using machine learning as a critical business capability, going so far as to create its own research center for AIML called Borealis AI. The challenge that the Royal Bank of Canada had was that AIML projects to up to two months to get off the ground. So they couldn't really see the result of the investment because the getting them into production took too long. So, there were a number of symptoms of that, workflow orchestration and social service access to GPU was slow. It was hard to build platforms, the actual applications and this of course security and compliance requirements were hard to meet. The goal in the end was to build an AI platform that could serve its more than 100 AI developers and engineers helping complete projects faster, but most importantly, adopted DevOps culture into Leverage Cloud to improve efficiency and application through these cycles. And the solution essentially was to use Red Hat OpenShift to build this platform in collaboration with NVIDIA. So this is another great story of how Red Hat in collaboration with its partners, helps deliver value for customers and help them accelerate their processes. It relied on a few things that we kind of spoke earlier, continuation of machine learning applications and services, building a private platform for banks, AIML center, a unified platform to which the different teams that are part of the bank can collaborate. And of course, it simplifies access to hardware for the NVIDIA through the NVIDIA operator. The results speak for themselves, more than a thousand models have been deployed, experiments run 10 times as fast, models take days to build instead of months and the performance of the system is great. It can process 13 million records in less than 20 minutes. So this not only kind of helped RBC to meet its goals, but it also set RBC on a path that where it's able to, when it's thinking to leverage AI in customer facing applications as well. So this story has not only helped RBC meet its goals, but actually it sets the path for future success. In summary, what we talked about here today is about how financial institutions that have been have a long-standing experience of using data-driven processes are launching new initiatives. And in order to be successful with these initiatives, in order to leverage, like to meet the business opportunities to take advantage of the technological advantage and to meet the business objectives and regulatory obligations, they need to build a platform that allows self-service access to tools and data and reduces the operational IP burden. Such a platform is provided by Red Hat OpenShift that provides container and hybrid cloud-based capabilities that provide the flexibility, reliability and security required by financial services environments. And as an example, you've seen how top financial institutions like RBC have accelerated the adoption of AI and can easily launch new initiatives, creating value for the business. I will end with a number of resources, a number of hybrid cloud success stories from Red Hat, a bunch of reference architectures for the use of artificial intelligence in a financial institution setting, like the architecture for money laundering, which I've spoke about earlier, and also an e-book for an example of operationalizing AI into production. With that, I would like to thank you for attending this presentation and to wish you a, to enjoy the conference.


Hello and welcome to the session in which we would look at a treasury stock exercise or this could be a simulation on the CPA exam, simulating a treasury stock transaction effect on the classes of accounts and on certain accounts. So we're going to review three transactions that deals with treasury stock and determine whether these accounts increase, decrease or has no effect on the following accounts and you might see something like this on the CPA exam or you might see something like this in your intermediate accounting course or in your financial accounting course. So let's go ahead and get started. Adam Company has outstanding 80,000 shares of $10 power value stock which has been issued at $60. That's fine. It was issued at $60. Review the following transaction and determine their effect. Whether these transactions increase, decrease or they have no effect on the classes of accounts below. So first I want to make sure you understand when we issue stocks what entry do we make. When we issue stocks, we debit cash for the amount of cash that we received. We credit common stock for the number of shares times the power value which is in our situation it's going to be 800,000 and anything left will be placed and paid in capital. Whatever's left will be paid in capital. Now since those stocks were sold at $60, we know that we received $4.8 million and as a result our paid in capital is a plug-in of $4 million. So this is the original entry that we made when we issued the stocks. It's very important that you understand what was the original entry because if you understand the original entry purchasing treasury stock is simply the opposite of that. So if you understand the initial entry, the opposite is easy. Remember the paid in capital is a plug, whatever's left. So we know we received cash for $4.8 million. Common stock is the number of shares. I want to make sure you know the formula times the power value, remember. You want to memorize this because this is easy. Common stock always the number of shares times the power value. Anything left is paid in capital pick. So if we look at this transaction, we notice that assets went up and equity went up. This is when we issued those stocks but that's not the question. The question is about those three transactions here. Before we proceed any further, I have a public announcement about my company, farhatlectures.com. Farhat Accounting Lectures is a supplemental educational tool that's going to help you with your CPA exam preparation as well as your accounting courses. My CPA material is aligned with your CPA review course such as Becker, Roger, Wiley, Gleam, Miles. My accounting courses are aligned with your accounting courses broken down by chapter and topics. My resources consist of lectures, multiple choice questions, true-false questions, as well as exercises. Go ahead, start your free trial today. No obligation, no credit card required. So let's start with the first transaction. We purchased 5,000 shares of Treasury stock at $90. Well, let's think about this. What journal entry do we make? We are going to credit cash for the amount that we paid, which is 5,000 times 90 shares, which is, I believe, 45,000 or was it? It is four zeroes. It's 450,000, 450,000, and we're going to debit Treasury stock. So let's take a look at these, at the effect of the accounts. Assets, assets will decrease. So assets will go down. While liability is not affected, stockholders' equity, well, what would happen to stockholders' equity if we increased Treasury stock by 450? Stockholders' equity is a contra-acquiry. Contra-acquiry, therefore, stockholders' equity will go down. Well, did we touch paid in capital? Because the question is about paid in capital. So when we bought the Treasury stock, did we touch paid in capital? And the answer is no effect on paid in capital. Did we touch retained earnings, none that obviously nothing in net income and nothing in retained earnings? Now, transaction two. We resold 2,000 shares of those Treasury stock, of those 5,000 Treasury stock at $98. Hold on a second. We bought them at nine, sold them at 98, not a bad deal. Okay, let's see what we do. We're gonna debit cash for the number of shares times what we sold them for. So 2,000 times 98, that's gonna be 196,000. And we are going to credit. I'm gonna be assuming we're gonna be using the cost method. We're gonna credit Treasury stock, Treasury stock for the amount of what? Treasury stock, the cost was 90. So if we take 90 times 2,000 shares, that's gonna be 180,000. And we have left something is hanging. We have an extra, which is technically the, in quote, the profit, in quote, the profit of 16,000. So let's start with what we know. We know that cash went up. So assets went up. We don't see any effect on liability so far. Obviously, we're not gonna credit a liability. So now the question is, what do we do with the 16,000? For that 16,000, we cannot increase profit. What we're gonna have to do, we're gonna have to credit paid in capital and account called paid in capital Treasury stock. Therefore, stockholders equity overall would go up. Notice it's the opposite of what we did earlier. Paid in capital also would go up because this is paid in capital. We have different type of paid in capital. We have paid in capital, Treasury stock. We have paid in capital, common stock. We have paid in capital, preferred stock. But paid in capital would also go up. With retained earnings goes up. I don't see anything because it did not affect income. If it doesn't affect income, it doesn't affect retained earnings. Okay? So we don't, there's no effect on income, no effect on retained earnings. Now, we resold 500 shares of Treasury stock at $80. Again, what do we do first? Let's account for the cash. Sorry, let's account for the cash. Okay, we sold cash. And that's gonna be 80 times 500 shares. That's $4,000 in cash. We sold them at, we credit Treasury stock for the amount of 500 times 90 at cost. Again, I'm using the cost method, which will give us 4,500. And now we have 500 hanging here. 500 hanging a debit to make the entry balance. So let's start with what we know. We know that assets will increase. We know that. We know there is no effect on liabilities. Stockholders' equity, what's gonna happen to stockholders' equity here? Well, let's think about it for a moment. What we're doing here is we are reselling the stock. Okay, reselling the stock. So we debited cash, increasing cash. We credited Treasury stock. When we credit Treasury stock, we reduced stockholders' equity. We reduced counter equity, which means we increase stockholders' equity. Cyber, by reducing Treasury stock, I failed to mention this here. Since I failed to mention it, let me mention it again. Since we are reducing Treasury stock, we are increasing equity. So we increased equity. Now, we are missing a debit here. What do we debit? Well, here's what's gonna happen. Technically, this 500 is a loss. Notice we sold it at 80. We purchased the stock at 90. We have a loss. Again, we don't book a loss or a gain from our own transaction, but listen to me carefully here. Listen to what I'm gonna have to say because there's gonna be an exception. I already have, not exceptions, it's gonna be a different, it could be a different scenario. I already have paid in capital Treasury stock and in that account, I have 16,000 if you notice. So if I incurred a loss in quote from selling my own stock and I do have paid in capital Treasury stock, I'm gonna book that loss here. So this $500 will be booked to, sorry, let me, it closed on me. So just let me, okay. So this $500 will be, will be, will be, will be debited to paid in capital Treasury stock. So I'm gonna debit, paid in capital Treasury stock. And what is that going to do if I debit paid in capital Treasury stock? Well, if I debit paid in capital Treasury stock, paid in capital overall will go down. There's no effect on retained earnings. There's no effect on income, okay? Now, here's the exception. If my losses from selling Treasury stock did not have, if my losses exceeded for this example, 16,000, because I have 16,000 paid in capital Treasury stock, any excess above 16,000, then I will reduce retained earnings. So I will have to debit retained earnings if I did not have paid in capital Treasury stock. And if I did not have paid in capital Treasury stock altogether from the beginning, let's assume that's the case, then I will debit retained earnings, then retained earnings will be affected. So the only way retained earnings can be affected from Treasury stock is downward. No way retained earnings would go up here in any scenario. Okay, retained earnings could go down because you don't, you have losses, you created in quote losses from buying and selling your own stock and you don't have paid in capital Treasury stock to absorb those losses. Net income, no way will be affected, liabilities no way will be affected in any way shape or form, in any way you do it. So net income and liabilities are out. So what's affected are assets specifically cash and equity accounts and you need to know how these equity accounts are affected. Treasury stock is an important transaction. Treasury stock transaction is an important concept in your accounting course as a student and on the CPA exam. Invest in yourself, don't take it lightly. The CPA exam is worth it if you're studying for your accounting courses. Now you need to go to Farhat lectures and start to work MCQs through false. Look at additional resources to help you understand this topic. Invest in yourself, your certification, your accounting career is worth it.


hi guys welcome back to my channel today i'm going to be vlogging my day at the sydney vegan markets so this is the second g that they're having it don't laugh at me my sister's coming along too she's laughing at me vlogging for the first time wow i'm so bright oh my voice came out really high when i was like hi i was going to be like hi guys welcome back to my channel today i thought i'd take you along with us on our sort of first experience of going to the vegan sydney markets in the city we've been looking at their facebook page on all the posts that they've been making unlike all the vegan products that they have and like i saw this amazing like the ultimate vegan burger it was like huge so i'm really keen to try that today try some of their different foods there they try some of their beauty products maybe get a few t-shirts really excited just to see so we're going to take you along with us and our experience of going to these markets so i'll see you there as you can see i'm so excited to try everything out and get tasting as much as i can today so here is my little tour of the vegan markets today and what we get on the way as much as we could and it was honestly the best food ever there was so much to choose from we had a burger like you saw hotdog we had loaded fries we had these amazing donuts which they call hot dogs which was honestly the best donuts i've ever had that's that i bought so i'll show you here one of them was this cool t-shirt it has a hilarious slogan um and what i really liked about it was the packaging on it i thought was really interesting it's actually compostable and it's biodegradable and they actually source it from bali it's made with cassava plants so i'll show you what the shirt looks like on so voila this is what the shirt looks like i really like the color it's like an olive green it's really like and as you can see well it's very funny and yes it's just a really cool shirt that i thought was really awesome yeah so this is the other product that i've got this candle right here i'll take it out of packaging but it's from it's made by angels secret i'll link all the like all the websites and instagrams of all the um products that i got the places where i've got them all like them all down below in the description so you guys can check them out yourself oh my gosh this packaging is not easy to get into what was the lemongrass and it smells smells amazing it's so good and what's interesting about this is because it's the gemstone like gemstone one it's really interesting the gemstone helps with healing she talks so the creator of these candles she talks about on the website and it's just so fascinating how about this one i've seen it on their facebook page it's the sydney vegan market facebook page before we went um and this one was actually the hemp milk one and it's actually by this company here i like them and i'll just i'll link them in the description below this is really interesting they actually pre-make the like pulp so like the hemp they just whizzed it up in the blender with some water already so it becomes like a pulp and all you need to do is add some of this to water in a blender stir it up for a while on high as high as it goes in the blender and it all makes it together and creates this amazing hemp milk it was the most refreshing thing we had when we were at the market it was so good i've never had hemp milk before so this looked really really good so we bought some of this to have ourselves one jar of this actually makes 10 litres of milk it's fantastic another product that i found that i was really excited about when i saw this i was like oh my gosh this is going to change the whole way like i wash my hair so you know how you used to using shampoo conditioner or even if you might use apple cider vinegar and barcub soda to wash your hair i used to do that too but i found using apple cider vinegar as a rinse it would make my hair go oily i don't know if that happens with you guys come below this is something that happens with you but i would find and make my hair go oily like quicker or just make my head look oily didn't really look like i'd washed it so i'd gone back to using natural shampoo and natural conditioner but when i was here the lady who creates these soap um bars and soap like shampoos which i thought was so cool she says she just uses it like this so far she rubs it through her hair make sure it gets all clean leaves it in there for a while to sort of like get all the gunk all the build up in your hair and let you wash it out at the end so this then that's all she does just in these conditions afterwards and she says it works so well how crazy is that all you need to do is use this soap bar to wash your hair in the in the evening or morning so the company this is product is made from it's nana nana can't cook i'll link it in the description below but this is basically what it looks like so it's what this one is the tea tree lavender and apple cider vinegar one so don't mind me i got actually a little bit burnt today that was my bad i didn't put sunscreen on listen learn next time puts on screen and last but not least we got this porta healing natural deodorant as you can see now when i saw these guys at the vegan markets i was like oh my gosh i already buy their natural deodorants and natural face moisturizers and things we actually look we actually found out about them at the blue mountains markets in near where we live so we live quite near the blue mountains so we were up there one time then the markets one we actually discovered these guys while we were there so that's when i started using their natural deodorants rather than using spray ones because they have chemicals and nasty additives i actually prefer using the natural deodorants now just because using spray deodorants for me they didn't last as long as this one does for me i found they would wear off halfway through the day enough to re-apply it anyway and two they had nasty chemicals and additives that would really harm like they can be really harmful for your body so i decided to use this one as it's made with natural ingredients so when i saw these guys i was like oh my gosh i have to get more of the more of their deodorant because i was almost out of it and two because i was like i want to support these guys they're really cool so that is basically my sydney vegan markets haul that i got so that's all the products that i did i got today and then i had a heaps of food as you saw in the video which was so good the top two things that i really liked from the vegan markets like food wise today had to be the honuts because they're the best thing i've ever had and the vegan burger so good so if you guys get a chance do check out sydney vegan markets next year i highly recommend them so good i hope you enjoyed the video guys if you did like it please give it a thumbs up and please comment below what you thought of the video what you liked about it maybe if you've been to the sydney vegan markets too tell me what you got when you were there what you loved the most about it i'd love to know otherwise i'll see you in my next video guys bye


Hey everyone, welcome to today's day trading recap today's Wednesday, December 2nd I had a positive green day today plus 578, but it was a royal battle Let's break this down.  So let's break this down Talk about what we did here starting with Apple. So this would this one was actually kind of lucky But hey, sometimes better be lucky than good. I actually put it in order when price is trading around in this area here I had left went to the bathroom. I totally forgot I had the order in I was anticipating price coming down to unchanged And that's what happened and I got filled there But didn't even realize that I was in the trade until after this thing kind of took off So did exactly what I wanted, but interesting. I kind of forgot about the order I don't like to let resting orders sit, but it worked out for me this time Amazon plus 320. So this is one that we we took in the live stream room right before we went off the air price had pushed back up right under the The high of the day and was kind of chopping around and so we were looking for Amazon to go positive on the day. So we got long right in here and right away. Just pushed up Only only ended up doing one contract. So just took a quick profit out of that Wasn't able to scale out. So decent profit though of 320 Boeing plus 182 now This was a this was one of our battles that we had today. So Boeing had this big push up and then and then we and so we got short here Pulled back almost got filled on half of our exit But just missed it by a tick and it ripped higher and then we added here took this little pullback here So we ended up taking a loss on that mighty 90 trade But then on the runner it started to continue to consolidate Above yesterday's high. We added a little here and then added a little bit more right here and then Took some off here and then held the rest until we got out. I think it was just somewhere in here Kind of at the high of day. So ended up booking a positive P&L in Boeing overall Donkey Kong this was a painful one as well. So this was a big push up. We got short here looking for a pullback Never did kind of had a little bit of a pullback here But then just ripped higher. We added some up in this area I finally got our little pullback and got out but got us for 848. I would you know I'm not disappointed in the the way that I traded at all today I mean I did use a little bit bigger size and so that's gonna obviously show a little bit bigger losses on some of these trades That we lost on it happened to Use bigger size today, but overall, I mean, this is a trade that just lost, you know 100% of your trades aren't gonna be winners. So Donkey Kong just got us.  I'm coming for you. You owe me some money Netflix was a nice winner plus 530. Let's take a look at Netflix here So Netflix we got let's see we got long here. Yeah on this initial push lower Nice flush lower. We got long in here and held this up there for a nice bounce of plus 530 on Netflix In video was another one that got us We did get some of it back with our with our runner, but on this big push up We got short here and then we didn't you know, we had two red bars Which we should have gotten out of our position here, but I held it I was waiting and it just popped up and then I was kind of in this to battle it and and it ripped higher again Added again and got this little pullback, but by that time we were we were down on the trade We were able to re-enter on a runner in this area right here and Chopped around for a little bit and then popped up and gave us a nice profit on that runner So net net still loser in Nvidia But got some of it back on that on that last piece there and then Peloton was our big winner plus 1290 Jumped in this a couple different times At this initial push-up started consolidating above this pivot We got long right here caught this nice pop got out of most of it it pulled back We re-entered with some more contracts here and caught another pop and we're out of most of it It started to pull back I think I had one last contract ended up just cutting it loose in this area here But plus 1290 and then uba uber was our last trade uber Had this let's go back here. You had this nice nice big push-up We got we got short here and it just started grinding sideways And then and then just pushed up on us so we ended up just cutting our losses and getting out of uber So plus 578 on the day not the kind of green day that I really like But positive nonetheless so can't complain. I had to battle some early early action where we were trying to get short and But we were able to battle back it with some of our runners later on to to go positive for the day So hope that helps you guys have any questions. Let me know


So, Soma means it's a day to get intoxicated. Temples were consecrated, keeping this in mind. Wherever there is a powerful linger, there are specific processes for that so that our body and the physical things around us don't become impediments in our life. Namaskar, I'm Sarkar. Why is one day treated special by Shiva devotees? See the lunar calendar is worked in a certain way. Let's say twenty-eight days approximately. In these twenty-eight days, four segments or four weeks, today this Monday, Tuesday, Soma-vara, you know, it's Soma-vara. So, Soma means it's an intoxication day. It's a day to get intoxicated, not at the Tasmak shop, at the Shiva temple. But that was relevant if you were only following that lunar calendar, then the months would be different, you know, the dates would be different. Then Mondays always came on that day that is relevant in terms of the lunar cycle. But today we have taken the same nomenclature and put it into another system which is more like metric system one, two, three, four, five. I don't know why they didn't make hundred days in a month or ten days in a month or something like that. They made thirty and four because it didn't go well. They made one thirty, one thirty-one, another twenty-eight, twenty-nine. This is a Hodgepodge calendar. But that's become the world's waste because it is imposed by the western societies and it's taken as a standard calendar today. It is absolute Hodgepodge because it has no relevance to the planetary system. The Lunar calendar or the Lunisolar calendar that we follow has relevance and the months change accordingly and the weeks and the days change accordingly. In that context it is relevant that Mondays are significant for certain processes. So temples were built keeping this in context that when a temple is built, it also waxes and wanes in a certain way. In all the Shiva temples, wherever there is a powerful linga, there are specific processes for that. All this was related to the position of the planets and the satellite of moon around us and relating to that. Because we understood that our lives are related to these things, because our physical structure, our existence here is... This is a potter's wheel which makes us... So to stay in tune with that so that our body and the physical things around us don't become impediments in our life. So the way the Lunar calendar flows is, there are twelve and a half months in a year, sometimes thirteen months, sometimes twelve and a half months, it goes like this, which is perfectly in alignment with the planetary system the way it's functioning. So it is based on that all these things were done, temples were consecrated keeping this in mind.


Well, we have a little winter time riding here, and I have a nice, nice mare that I get to work with for a little bit. And I started riding her without her bridal. Just give her a break and out of her mouth for a little bit. And it's interesting because they feel good having freedom to move their tongue and their jaw and they can stretch and come forward and arc into the hand again, sometimes with snaffle bits, horses get tense and they roll in behind the vertical. They try to get away from the bit. Their neck gets short and they have a hard time functioning.


Now, let us look at this even further, let us go a little bit more into depth here. Now, what is this particular expression here on the left? What is this particular expression? The infimum of this linear function evaluated over T. Now, if you recall, I had discussed this a little bit earlier. If you evaluate a linear function over G and when lambda is greater than equal to 0, and when this quantity here is 1, then that linear function was actually nothing but the minimizing that linear function was actually the same, the minimizing that linear function was actually the same as minimizing the Lagrangian over the entire space. So, its value was actually the value of your dual function. So, now I want to try and relate this in that way. So, what I have on my, if you just take this here, so I have infimum of, let us write this out here once again. So, infimum of lambda tilde transpose U plus theta tilde transpose V plus mu tilde into T over U V T in G, this is greater than equal to for us mu tilde into P star. This is where we are doing. Now, suppose, or you can say, let us take two cases. So, case 1 is when, so actually, so now, in fact, before I take these two cases, let us come, let us look at this once more. So, what we have done is we have said that well T and G, they are convex sets and we applied the separating hyperplane theorem and that has got us to this stage, that has got us to this particular inequality here. So, now, we have so far, what have we assumed about the optimization problem? We have assumed that well, you have this optimization problem where A is full row rank, G is a convex and this is attained. And from there, we said we just, we only showed that well G must be a convex set, T must be a convex set, G and T are and we said that there must be a separating hyperplane that separates G and T. I have not yet made any assumption about constraint qualifications.  So, what I will do is, so we have reached this stage, so now, before we move any further, let me raise this before I move any further. Now, suppose, so this is going to be my theorem, consider the convex optimization above, convex optimization problem. So, A is p cross n rank of A is p and suppose there exists an x hat such that, so we want to slate a point. So, there exists an x hat such that all these g i of x hat is strictly less than 0 and A x hat is equal to b. So, this sort of point is what is called a Slater point. So, consider the convex optimization this and suppose there exists an x hat such that g i of x hat is strictly less than 0 for all i from 1 to m and A x hat is equal to b. So, this is what is called, suppose there exists a Slater point and this condition is what is called a Slater condition, then p star is equal to d star and what is d star? d star is simply the max of d of lambda comma theta over lambda greater than equal to 0. So, at p star, so the optimal value of the primal is equal to the optimal value of the dual. So, that is what we are now, we are setting up to show. So, we came up till this stage, we wrote this set, we wrote this set g, we wrote this set t and we found that these two sets are disjoint and then therefore, there is a separating hyperplane and the separating hyperplane gave us that there exists this sort of quantities lambda, lambda tilde, mu tilde etc. such that lambda tilde is greater than equal to 0, mu tilde is greater than equal to 0 and this inequality holds. Now, from here onwards, I will need, I will have to invoke my Slater, I will have to invoke that we have a Slater point. So, now suppose here, suppose out here mu tilde is positive. Now, suppose mu tilde is positive or let us say we are taking the case where mu tilde is positive. So, if mu tilde is positive, then what can I do with this expression? This expression, let me just write this expression once again there on the next slide.  This is greater than equal to mu tilde times p star that is what we have so far. Now, suppose mu tilde is positive and I have that lambda tilde is greater than equal to 0 already. So, this is actually nothing but the infimum, this is nothing but the infimum of the Lagrangian evaluated at, so what I can do is the following. So, if mu tilde is greater than 0, I can just divide both all sides by mu tilde and then I would get that this is nothing but the infimum of the Lagrangian evaluated at lambda tilde divided by mu tilde and theta tilde divided by mu tilde and that would be greater than equal to p star. And what is this infimum? How did I get this? This is because of what we just wrote here that we just we wrote that the dual function is actually equal to the infimum of a linear when lambda is greater than equal to 0 and this coefficient here is 1. The dual function is actually equal to the minimum of this linear function over this entire over the set g and if you see that is exactly what this was. This was the minimum of this linear function over the set g, lambda tilde was greater than equal to 0. We did not have a coefficient 1 here but that coefficient can be made 1 by just dividing throughout by mu tilde and that you can do because mu tilde is actually, we assume mu tilde is greater than 0. So, when mu tilde is greater than 0, this becomes the infimum of the Lagrangian which is simply d of lambda tilde divided by mu tilde, theta tilde divided by mu tilde and that is greater than equal to p star. And what does that mean? This means that we have got the inequality which is opposite of that of weak duality. So, which means this implies strong duality holds equivalently p star equals p star. So, when mu tilde is positive effectively what we have done is we have automatically found a non-vertical supporting hyperplane and that has given us this particular direction. Now, suppose mu tilde is equal to 0, see mu tilde is greater than equal to 0, we took care of one case where mu tilde was strictly positive and there we got that strong duality holds. Now, when mu tilde is equal to 0, there should be able to rule out that we should be able to still show that either still show that strong duality holds or rule out that this case is not possible. Still, until this point we have not yet made use of this later condition. We have only used convexity. So, now we are going to actually going to need this, we are actually going to need slater condition. So, let us look at it this way. So, now suppose mu tilde is equal to 0, then what happens to my right hand side? My right hand side here becomes equal to 0 when mu tilde is equal to 0. So, if my right hand side is equal to 0, then I am left with and on the left hand side my mu tilde times t that term is also equal to 0. So, when mu tilde is equal to 0, what I have left with is something like this lambda tilde transpose u plus theta tilde transpose v infimum of this over u v t belonging to G. Now, this here I can again since lambda tilde is greater than equal to 0 and v should be equal to Ax minus B because of u v t belonging to G. This in fact can be written in this way infimum of lambda tilde times this itself. So, the least value of u will be get attained when u that u is actually equal to G of x. So, it is infimum of this into lambda tilde transpose G of x plus theta tilde transpose Ax minus B and over all the infimum is over all x. Now, let us take x to be a slater point. So, suppose when this is equal to this and my right hand side on the other hand is that my right hand side here remember this quantity evaluates to 0. So, this whole quantity is what I get is that this whole quantity is greater than equal to 0. In other words, I am getting that lambda tilde transpose G of x plus theta tilde transpose Ax minus B is greater than equal to 0 for all x. Now, take x equal to x hat which was my slater point. So, I had this x hat here which was my slater point. So, let us take x equal to x hat. So, what would we get from there? We would get that G of x hat remember would be strictly negative and Ax hat would be equal to B. So, if G of x hat is strictly negative and Ax hat would be equal to B and Ax hat is equal to B. So, if I put x as the slater point that would imply that the only way possible is that would give me. So, lambda tilde G of x hat greater than equal to 0 and the only way that is possible is that lambda tilde is equal to 0. So, not only is mu tilde equal to 0, I have now also got lambda tilde is equal to 0. We are now almost at the end. So, if mu tilde is equal to 0, lambda tilde is equal to 0. The only way remember the separating hyperplane theorem told us that there is a non-zero. What did the separating hyperplane theorem tell us that we told us this should be non-zero. So, the only way this slope should be non-zero is that now is that theta tilde is not equal to 0 this more neatly. So, the only way that is possible is that theta tilde is not 0. Now, theta tilde is not 0. So, what does this mean? But anyway, we have still we have got that lambda tilde is equal to 0. So, this term is so this term is now gone. It means now that with theta tilde not equal to 0, we have we must have that this expression is greater than equal to 0 for all x. So, let us go back there. So, which means that theta tilde transpose Ax minus B is greater than equal to 0 for all x. Now, if you take, so let us think about it this way. So, here is Ax minus B. At x equal to x hat, at x equal to x hat, you have Ax equals B. Now, that means that is the point that is exactly on the intersection of all of these hyperplanes. So, in the neighborhood of this point, you should be able to find other, there exist other points where you should be able to find other points for which for whatever slope you take, you should be able to find points for which the inequality actually gets reversed. See, it is this quantity is great in a neighborhood of this later point that they would exist points like this, points where this inequality gets reversed unless, see because this is after all a linear function. This is a linear function. So, at and at x equal to x hat, this the left hand side is becoming exactly 0. So, you should be able to find other points where in the neighborhood of x hat where it also becomes negative, because this is a linear scalar function. So, since it is the only way it is not that a linear that this function will not become negative is that its slope is actually 0. At x equal to x hat, you have Ax hat equals B and LHS equals 0. In the neighborhood, in the neighborhood of x hat, we have that there exist x such that theta tilde transpose Ax minus B is less than 0 unless, unless it is the slope itself is 0, unless theta tilde transpose A is equal to 0. Now, so in that case, then you will not be able to change x and get to you know, you will not be able to vary x and get to a negative value. So, unless it is a constant function, the linear function around that point would take values that are both positive and negative. So, now, so and it will be a constant function only if the coefficient itself is 0. But then can this coefficient actually be 0? What is this, what is this actually saying? Well, for this coefficient to become equal to 0, what we are saying is that theta tilde transpose A is equal to 0, which means that if you take the rows, the rows of A and you sum them up using some linear combination which is with these nonzero weights with a bunch of weights that are not all 0. So, you take a linear, so this is actually the left hand side is a linear combination of rows of A and that linear combination we are saying is equal to 0. Well, that means that what does this mean? That means that the rows of A are linearly dependent. Now, when can, so but then we if you recall here, what did we just assume? We assume that the rank of A is equal to P, that means it has full row rank, which means which is a contradiction to that, contradiction since A has full row rank. So, the only way this is possible is that that A has linearly dependent rows, but we assume that A has is full row rank. So, the problem we are starting with is one where A has full row rank, so which means that this is also not possible. So, what does this mean? What this has got us to is that this case, this case here where we said suppose mu is equal to 0, this case is not possible. So, which means mu tilde is not equal to 0, which means the only case that is mu tilde has to be greater than 0 and is the only possible case and then which means that strong duality holds. So, the only possible case is this case where mu tilde is positive and in that case we concluded that strong duality holds and that is the proof. So, to summarize, we have a convex optimization problem as written here with the following requirements that the constraints, the linear constraints are full row rank and there exists a slater point for the constraints. You put these together, then it has to be that the optimal value of the primal is equal to the optimal value of the dual. That is what this theorem has shown and in proving this basically up to the point we got to the point where we wrote this set G, we wrote this set T we showed that sets T and G are both convex and then we showed that there must be a separating hyperplane. Now, the problem from there onwards was that we wanted to show that the existence of a non-vertical separating hyperplane, which means that we wanted that this mu tilde is not the mu tilde here has to be basically has to be positive and that this slope here is greater than equal to 0. The greater than equal to 0 came quite easily but in showing that the separating hyperplane is non-vertical that means this mu tilde has to be positive that is where we needed a constraint qualification. So, until that point everything works without constraint qualifications. The constraint qualification ensures that your slope is that your separating hyperplane is actually a non-vertical one and that is what actually that makes your strong duality work. So, with this then we have we have now shown strong duality also for convex optimization. You will recall that the slater condition which is the constraint qualification for ensuring strong duality that condition is was also what gave us was also used in KKT conditions. So, all these things come together when you actually solve an optimal when we when strong duality holds the optimal values of these Lagrange multipliers are also optimal values of your also what will the optimal values of the dual variables are also what will solve your KKT conditions as Lagrange multipliers. So, with this I will end here we will next move on to a new topic.