Okay, we're back live here at the Strata Conference at Silicon Valley. I'm John Furrier, the founder of SiliconAngle.com and SiliconAngle.tv. And I'm joined with Mike Cohoes. I'm Dave Vellante at Wikibon.org. This is day three for us at Strata, Wikibon.org. Has all the research. SiliconAngle.com is the news and editorial. So go check out those sites. Check out SiliconAngle.tv, where we broadcast all the videos. We've got a huge day today on top of yesterday's. And we've got a company called Calpawn here today. We do massively parallel columnar databases. We're going to talk to Jim Tomaday, who is the CTO. Jim, welcome. Thank you. Good to be here. Yeah, so this is a hot space. And help us understand sort of where you fit into this new emerging database sector. Sure. So internally we offer the scalability expect from MapReduce offering. We're actually mapped to a columnar storage that allows for some tremendous IO efficiency. So we do things fast. And part of our package is just an overall ease of use component, because the combination of the two technologies allows access to the data to be very easy. So, you know, we're very familiar with the verticals and the aster datas and the green plums. How do you fit into that space? You know, obviously you've got the Teradata's and the traditional legacy Oracle stuff, IBM. But you had this emergence of these sort of new MPP type players. You fit in there or are you new new? So we fit in there two degrees. We certainly see those competitors. We have a columnar storage. In some cases we're going to map to the IO efficiency you would expect of Vertica. In some cases the IO is going to be similar. Internally though, we actually have a different distribution of work model, rather than being a sort of a database on top of a database with the most history, running SQL and then that data being stitched back together. We actually translate the SQL into MapReduce internal to our engine, and then that workload is distributed across all the cores in a single server, all those cores in a cluster of servers. So tell us a little bit about the company. When did you guys start? So we actually, this effort's been in place since 06. The product itself was in basically benchmark testing in 08, launch in 09. Infinity B. Infinity B product, yes. Launch in 09 in our open source and then the commercial in the following year in February. So your thrust is in Hadoop? No, so our thrust really is really around a new solution for traditional analytics. So it's not necessarily a solution that's for unstructured data because columnar by definition is applied structure to it. But certainly there's plenty of use cases for structured data and we just make that operation much simpler. So it translates for us into web analytics solutions. We talked yesterday with the company that's doing genomics for livestock. So different applications come into play here. So how is this concept of big data changing the traditional enterprise data warehouse world? So it's certainly, in my opinion, it's certainly moving them off of the traditional platforms. That's for certain. So the general purpose database has not solved problems at two dimensions. So either at any access at scale there are better solutions and then additionally at analytics at scale there are better solutions and I think there's those two dimensions are basically stretching the traditional technologies. Can we talk about column stores for a minute? Maybe just do a little columnar 101 for the audience. Sure, certainly there's some confusing terms out there. So there's column family that really is a flex and access to an entity with a flexible structure and that's not what we are. So we're actually, one definition is sort of a true common database. We actually will divide each of the fields, each of the columns into separate stores that can be accessed independently. And so what that does mean is a couple things. You can access any query. We can use just the columns required for the query. So the IO characters are far lower than traditional systems. And then you can also, it's more flexible in terms of any columns that aren't part of the query just don't pay any penalty for those. And so it's easier to use from that standpoint. Okay, so you're saying that basically columns are better than rows in terms of IO efficiency? It depends on the workload. So as long as the workload is a large query against millions or billions of rows against the data set that's billions or trillions, it's a clear winner. For any kind of entity access the other is true. Row basis is better or column family or one of these other structures. Okay, so that makes sense. So what's your open source play? Talk about that a little bit. So we have a community offering that's out there that allows people to try the product. There's some reserve features into our enterprise. But we want people to be able to try the product. It's part of what's ready to let people know about us, certainly. We are also a MySQL storage engine. So that means that existing tools that connect to MySQL will also connect to our engine. Now internally we're a non-standard MySQL storage engine. We actually will take over the execution of the query and do all the joins within our layer rather than have that happen in the MySQL kernel. Okay, and so are you a software only? So we're a software only solution, yes. Okay, what does that give you? So just flexibility around implementation. So we are looking at even solutions beyond Intel architectures in terms of where we're going to run. How do we get to significant parallelism? In my opinion, that doesn't appear to be a solution that we want to build a hardware out because that tends to become stale quickly. So you're implying that you can scale better, be more portable? Yeah, so that's been demonstrated for us. We're very happy with both the scalability and the affordability of the system, yes. Jim, you got CTO and we want to apologize to Nick, your VP of marketing who's out there. Nick, I wanted to come on theCUBE, but... Never apologize to marketing. Dave and I don't like to give up the microphone, so we want to talk to the tech guys. But there's been a big tech conversation around performance. Obviously performance is great. Predictive analytics and real-time analytics are the hot areas everyone's forging into ahead of. But the database architecture in hardware has changed. We've had some folks on veterans in Silicon Valley, Scott Tetsin at Peer Storage, talking about all flash, no more spindles. So David Floyd was doing some research around IO centric architecture and infrastructure. And with flash and virtualization, it's changing how people are using the hardware side for maximizing performance and then the software on top of it. So how do you guys vector into that trend technically from a product standpoint and for all the people out there that are implementing these new flash architectures, they now can use commodity hardware, create multiple different kind of databases. What's your view on that perspective? So my opinion actually differs slightly. So I think that those technologies are absolutely differential allowing more IOPS into the system. But IOPS themselves are actually a solution for enemy access rather than analytic access, in my opinion. And so it's truly differential in that use case. But for ours, we're not IOPS bound in any use case in our engine. So the differential performance from minimizing the cost of an IOPS is small. And the other part of that is that certainly we're disk is the obvious enemy for performance. There's a solution there, but there's also efficiencies within processing in the CPU memory cache that we solve and that aren't affected by things like those technologies. Okay, so Avi Mehta was on yesterday talking about analytics. Dave, I can't recall specifically the exact quote, but he was saying something around how analytics is paired up. What was his comment around that? Do you remember that? He was, analytics is tied to the data is tied to the analytics.  Do you remember what he said there? He was referring to the essentially the do best job versus the analytics activity and not making a choice between the two but actually trying to bring the two together is essentially what he was talking about. Is there a new analytics paradigm that people aren't seeing that you guys are, that you're seeing success in? I guess that's my question. There's a little different philosophies on how you do the analytics with multi-processing. I mean MPP, for example. Well, so I think this is the answer to your question, but I mean the end users are interested in the ease of use of the system, right? So it's a combination of features that allows for easy access to a high-performance system without understanding the complexity is easier to use for the market. And that's where we think we've got a nice edge because we map the SQL to our MapReduce layer transparently. So the end user isn't aware of all that processing, that it's being processed but underneath the covers it is. So one of the terms that we've found in our customers is that for Warner Music Group they talk about, we let them turn their SQL developers into big data developers. And so we think that transparency is interesting. What experience have you having when you go into these big, your clients? You got to go in, you got to do the whiteboard, you got to meet with the tech guys and they want to have that business conversation. Bill Schmarzo was recently on talking about the cultural change in these companies. And we've heard the theme earlier on, it's a talent game.  I mean they're in tech, they're viewing SQL and spin around. Structured data is important. What are you seeing as the key ah-has for the clients and what are the stumbling blocks that they have to get over? Well, to go back to one of Dave's points, it's part of our open source strategy. So in many cases the tech guys get a chance to get in and get their hands on the product, understand the capabilities of the system and for us that's half the battle. So the rest of it is certainly forming a bond, making sure they understand walking through the architecture and things like that. All the traditional tools. But a key resource for us really is our access to open source capabilities. So there was a run on new next-gen enterprise data warehouse companies there. It reminded me of an NFL draft, John, right? Bill Belichick takes a left tackle and all of a sudden all left tackle start going, right? So there was a spate, you know, and a teaser. Absolutely. And Vertica and Astrodata and now it's sort of gotten quiet again. Yes. What's your take on all that? You must have been excited watching it. Well, so certainly these are interesting times. I agree with that assessment. So things I think have gone past that little bubble, right? Where people were excited and concerned that the draft pick has gone. So I think that's true. I think there are people making more seasoned decisions about acquisitions. But I believe that there are still places for differential technology to come to market. Okay. And you guys, so where are you at in terms of the company, the funding, headcount, things like that? So we're headcount just 23, 24, customer count, three dozen and moving. Active pool is much larger that we're really excited about where we're going in the future. We're in the middle of ramping headcount internally to do more partner opportunities.  And how about funding? So we're well funded. We're actually private equity, privately funded. Great. My final question is we got on a time limit here on our next guest. We're talking about an application explosion. Obviously analytics is big. We had Mike Daberon and other VCs we've talked to here. Frank Artali once spoke on the phone yesterday with Ping Li from Excel. Analytics is really the hottest area. So what are you seeing in terms of the enablement of where analytics is going? Obviously it's the dashboards and all that good stuff. What are the key apps that you're seeing that you guys want to enable and what kind of benefits? Predictive, as you mentioned, is tremendous. That's going to allow for much more powerful use of data to deliver a difference for the business. So that's the leading trend that we've got. Some of the, you know, certainly visualization tools are critical. I'm not going to pretend to be the expert around those. I'm more of the backbone's guy. Yeah. What's your advice to your friends and colleagues and customers when they ask you? Hey, what's going on with big data? I don't know. What do I need to know about? How do I get started? What do I do? I have all this old tech. Well, so I have a 16-year-old daughter who's in the middle of career decisions. What to do? Well, I talked to her about there's certainly tremendous opportunities in data. So what's happening five years from now and 10 years from now with data is going to be just life-changing across a wide variety of industries. We've got a sense of it looking five or 10 years back, but I think the future is even more interesting. Okay. Well, thanks so much for coming on The Cube. Jim Tominy, thanks so much. You're in Frisco, Texas, not San Francisco, but it's in Texas. It's near Dallas. You guys love Texas. Mark Hopkins lives in Texas. We have a Silicon Angle office in Texas. Keon's there. Keon's there. And we love the Texas hospitality. Good old barbecue. You guys are great, friendly folks down there and good tech scene. So thanks for coming.


Tantra is willing to use the mind in a certain way. Tantric processes need elaborate visualization, elaborate. Very sophisticated levels of visualization to a point where it manifests. To make mental process just like a physical process is what Tantra is trying to do. To make imagination so strong that after some time it is like physical.  Both are possible, any kind of form you want. You can actually create it, manifest it and make it walk. You know like I was probably at that time, first standard, means I'm five, six, six, seven I think, second, third standard I must have been. And I have a cousin brother, who's a no good fellow. So at that time in my mind I'm you know like if I'm sitting like this I'm I can be engaged in my mind for hours on end without any communication with anybody because I'm setting up all kinds of things. So I loved Mysore Zoo so much, I want to have my own zoo. So I desired my own zoo, I didn't call it a zoo, it was a world by itself. Where animals were not locked as I wanted them, they were all over the place. Lots of animals and of course I had a rhinoceros and everything. So this fellow, I you know one day something he was with me and I told him, you want to see my pet animals? So I just kind of took him in. I just started talking to him and I sat with him and I told him, see this is it, can you see this, can you see this, can you see this one. Every day he wants to go. Couple of times I allowed because he had something that I wanted. After that he has nothing but he wants to go every day and every day I have to sit with him and take him in. He got addicted to it. I'm saying it can become so real. Whatever human beings are doing, use the same things to elevate yourself. This is the way of the tantra. When it comes to the guru, he exercises tantra on the disciple in different ways, in ways that they will not know. If you get connected like this energy wise with a powerful deity, capabilities that are enshrined into that deity becomes available to you. Someday you must do that with me or if you find me unattractive you can do it with Dhyanalinga. Tantric processes need elaborate visualization. You will see Parvati wanted to have a child. The epithelial cells from your body, the earth for the material, your own energy and your mind, you make a baby out of it and it stands up. Once we initiate people into tantric processes, we cannot leave them alone because technology is an evolving process. With tantric practices or tantric methods, the change is dramatic. Tantra is a series of initiations. More than the practice, it's a series of initiations. When you went into it, however you were, when you came out of it, you're not the same person anymore. The Tibetans, because they came from a culture which was steeped in Akal, they were looking for magic. They want things to happen. They added Rakinis and Dakinis and everybody to Buddha. In the tantric principle, everything fits in because there's no principle. The principle is of unity. So, Kailash is known as the tantric mountain. He found Padmasambhava as a eight-year-old boy. It might have been a completely different realm altogether and they did phenomenal things which would have never been done with simple meditative processes.


Hi everyone. Happy Monday. I'm Joe for JaspeysCaseBreaks.com coming at you with 2223 Bowman University Inception. Four box random letter break number 13. We're going first letter, first name. 19 spotter. Yeah, if you get V for Victor, C for Caitlyn, so on and so forth. Big thanks to this group for making it happen. Appreciate you. And there are the letters right there. And let's roll it and randomize names and letters one and a five six times each. One, two, three, four, five, and six. Nancy down to Michael. One and a five six times for the letters one, two, three, four, five, and six. After six times we got J down to GI. GI Joe. All right, Nancy with J, Eric with M, Fabian with P, Victor, T, XL, Tristan with H, Kevin with A, Jordan with C, Tristan BS, Mark with V, Tristan EF, Fabian WD, Jordan, K, Nancy, no, N plus O, Eric, R, R, Oliver with Z, and Michael with GI. So let's order these alphabetically by letter. And we're going to pause the video when we come back. We're going to see if there's any trades and then we'll have the break stick around. We'll be right back. We'll see on the other side of the trade window. All right, welcome back everybody. No deals were done. So the list right here for break number 13 remains the same. And as you can see right here, this break is from a fresh case. So fresh and so clean. Not this case open and then roll the guy and we'll see which stack of four we're going to do here. Four, four, four, and four. So on the dice we'll go one, two, three, four. If I roll a five or a six, we'll just roll again. Four, one, two, three, four. So we're going to do dice is on the top camera. Now we're going to do that stack right here and these we will save for next time. And the next four boxes are in the store right now ladies and gentlemen. So if you want to check that out, I'll leave that. I'll actually leave those right there because I'll bet we'll run probably run this entire case back. It's tonight now. There you go. Here we go. Good luck everybody. And obviously all cards will ship and there's Caleb Williams up there. There's this blank card right here. It's the numbered card. Nice. 83 out of 99. It goes to letter C for Jordan. Slim pickings for Z. I think so, but we pulled a Z yesterday, I think. And people in the chat were very excited about this collegiate player who was who's apparently going to be a star and sells well on the secondary market. We've got Kellal Ware. 11 out of 99. It goes to letter K. And that's going to be for Jordan and the letter K. The design and inception. It's got some consolations back there. Future stars. Then we've got Jermaine Burton. Letter J. That's going to be for Nancy. Last spot mojo. This looks like like a vinyl record. All right, next box. Maybe Z-Flowers? Yeah, that's a possibility. All right, your numbered card is is Mackay Ashton Langford. 64 out of 199. Letter M. That's going to be for Eric. And there's Aiden O'Connell. 94 out of 99. It goes with my Raiders. It's going to go to Kevin in the letter A. It's Kirsch on the bereavement list. Because one of his grandparents passed away. And then we've got Femi Odukale. Oh, mom. How old was his mom? Letter F. That's going to go to Tristan with the EF combo. Take your time, Clayton. 70s. That's right. I do remember seeing that. I think Kirschaw still made his start, right? And then he said he was going to do it afterwards. There's Seth Trimble. 5 out of 50. That's going to go to letter S. Tristan with letter S. And then we got Bryce Young. Nice. 24 out of 25. That's some solid penmanship there. That's going to be a good one. Good name to collect throughout the next football season. Letter B. Tristan with Bryce Young. Nice. Nice, Bryce. All right, and we've got another tell-all where for Jordan and the letter K. All right, one more box to go. The next four boxes in the shop. So check it out. jaspyscasebreaks.com. I'm sure we can run through the entire case tonight. It's a random letter break. It's only 19 spot break. So some pretty decent odds to get the nice letters. First letter, first name. And then Hooker, the letter H. It's going to go to Tristan. The numbered card is Julian Phillips 240 out of 299. It's for Nancy, the letter J. And your final two autos. We have Rakeem Jarrett from Maryland. Letter R, R. Eric Houston with R. It's a cool auto too. And the final auto is Oscar Tashi Wei. That's what I'm going to go with right here. Oscar going to go to O. Nancy with N plus O. No. There you go, gang. Nice fun little break of Bowman Inception. Bowman University Inception. Sheebway. It's Sheebway. Thank you, Oliver. I'll remember that. Is he going to get drafted this year? He's going to be on NBA team here. I'll commit that to memory. I hope he does. That was Break 13 in the books. I'm Joe for jaspyscasebreaks.com. I'll see you next time for the next break. Bye-bye.


Hello and welcome to the session. My name is Asha and I shall be helping you with the following question that says A cylindrical pillar is 50 cm in diameter and 3.5 m in height. Find the cost of painting the curved surface of the pillar at the rate of rupees 12 per meter square. So let's begin with the solution and here we are given First, the diameter of the pillar is equal to 50 cm. So this implies that radius of the pillar let us denoted by small r is equal to 50 cm divided by 2 that is equal to 25 cm. And since 1 cm is equal to 1 upon 100 meters therefore we have 25 upon 100 meters and this is equal to 1 upon 4 meters. So this is the radius of the pillar. Also we are given the height of the pillar is equal to 3.5 meters. Now we have to find the cost of painting the curved surface of the pillar at the rate of rupees 12.50 per meter square. So first let us find the curved surface area of the pillar and this is equal to 2 pi r h where r is the radius of the pillar and h is the height of the pillar. So let us denote the height of the pillar by h which is equal to 3.5 meters. So this is equal to 2 into 22 upon 7 into radius is 1 upon 4 into height is 3.5 meters square. Now 7 into 0.5 is 35, 22 is 4 and 2 into 11 is 22 so we have 5.5 meters square. So this is the curved surface area of the pillar. Now we are given the cost of painting 1 meter square is equal to rupees 12.50 therefore cost of painting 5.5 meters square is equal to rupees 12.50 into 5.5 and this gives rupees 68.75 thus the cost of painting the pillar at the rate of rupees 12.50 is rupees 68.75. This is our answer. Hope you have enjoyed it. Take care and bye.


In this video, we will talk about Linear Regression in Priority to Handex. So, let us start with the activity. Assume that your students' performance, attendance and engagement data, you would like to predict the students' performance in the upcoming exam in your class. You have historical data, assume that your historical data from last 3 years or 4 years of day collected data from students. And you want to predict the students' performance in the upcoming exam. What data do we need? Like, do you need any other data? And how will you predict the performance? Given all this data, which data is important for you and how will you predict the system? So, it can be regression or anything else. So, think about it. Write down your answers after writing it down, resume the video to continue. Predicting performance requires identifying patterns from the historical data. And you can use attendance versus performance or engagement versus performance. You can do those kind of data picking. So, you can also talk about questions asked, difficulty level of each question, topic covered, you cover 4 topics, how much time you take into cover the topics. Questions we used, all these things can be also considered for the predicting. This also other data can be used. So, the step is you have to develop the model from the given data. So, that will be the linear regression model, we will talk about it. So, after developing the model from the training data, you have to extend the model to predict the future events that is apply the learn model new data that is with the current attendance what will happen. Suppose you created a linear regression model using learners attendance versus performance from the historical data. And that is a trained model and you are going to test it or you are going to use the model to predict the performance of the students attendance in the current semester. So, apply the learn to model to predict the future event that is a new data. That is the linear regression or any other predicting algorithm. So, what is regression? We talk about regression in this video. It is a statistical model which investigates the relationship between dependent and independent variable. We already saw regression briefly that it is to identify the linear relationship between dependent and independent variable. The assumption is there is a linear relationship. It is a very, very important assumption. And it is suitable for working with the continuous data. For example, the marks, we saw that instead of classifying into bin A, bin B, you want to predict in a continuous data like 75, 76.5 something like that. The use of regression is predicting or forecasting that is what will happen in the future predicting what will the students performance or evaluate the strength of predictors whether which particular independent variable is like strongly associated with the dependent variable something like that also can be evaluated using linear regression model. It is used widely just because it is easy and easy to understand, very no complexity at all to understand to create it. So, it is used widely as a first step in predictive models. So, we saw this picture already the regression can be classified into simple and multiple. Again, simple can be classified into linear regression or non-linear regression. Let us talk about simple linear regression. Then we will see one example of multiple linear regression. So, there are four types of regression other than the diagram shown in the previous picture. Let us take the linear only. Let us take the linear regression in that the simple regression means it has one independent value on one independent value. Let us see. What it means is the y is 1. There is no multiple things you are predicting x is 1. And we have what is that? We have weight or interscar like an intercept. So, c plus weight x1. So, weight 1 x1 y equal to mx plus c. So, the weight is this. So, there is a only one independent and one dependent variable here. So, that is called very simple regression, simple regression. In multiple regression, there is a one dependent value see y1 and intercept plus w1 x1 plus w2 x2. So, this can be like some n variable. So, the number of independent variable can be many. That is the idea. So, univariate regression and simple regression. So, univariate regression and simple regression this and this are same. There is no difference. It is again one independent single independent. But let us think about the multivariate regression. It is involves multiple dependent and multiple independent variables. It is not y1. You might have yi. Like you might predict yi, y1, y2, y3 also possible. So, we will not talk about that. That is not needed here. Let us think about simple regression and multiple regression. Let us solve first three things. So, in a simple regression, one variable is dependent that is predicted. Other variable is dependent. One is independent, one is dependent and you know that x and y. x is the independent and y is the dependent variable. Assume that there is a linear relationship between x and y. So, this assumption is very, very important. There is a linear relationship between x and y. If there is no linear relationship, simple regression cannot be used. So, given a dataset x and y or attendance and performance or engagement versus performance, linear regression model assumes that there is a relationship between x and y. That is linear relationship. And regression model analyzes the relationship between dependent and independent variable and tries to create a model from the historical data. So, the simple linear regression is simple y equal to mx plus ci, y equal to wx plus c. So, you know the slope is the w and c is the intercept. And we have six students data. Suppose, if you have six students data and which means you are six pairs of input data, six pairs of xi and y, x1, y1, x2, y2, something like that. Hope you understand what I am talking. So, you will have a one set of variable x1, y1. That can be say, attendance of 80 percent, final mark of 75, something like that. Similarly, x2, y2. So, if we have six students data, six pairs of x and y is available. That is the data. In a linear regression, the goal is to find a linear relationship between these two data that best fits this x and y. That is it. So, the very basic approach is let us talk about the attendance. We have six students attendance data and the marks out of the 100 for the same six students. So, we are plotting attendance in a percentage versus marks out of 100. Six students attendance here, six students marks here. Let us see how this line fits. So, you before doing this, you might see by descriptive analytics students to attend the class regularly scored good in exams. It is good. So, there is a descriptive analytics says that there is a linear relationship. So, by looking at this data in the plot, you might tell that there is a relationship. What is the relationship can be identified by diagnostic analytics? How to use that relationship to predict the future event is the predictor analytics. That is very simple example we are using it in the in this slide. So, let us see I plotted, I draw a line here. This line is like this. So, it fits almost all three data. And can we say this linear model is correct? If this model is correct, how do you validate it? Can you say this model is correct? And if it is correct, how do you validate it? Please pass this video and write down your answers after writing it down, resume the video to continue. So, the linear regression, now we see two models are there. I can say one fitting all this line, one line in the blue color it is actually bit low, but it might be better. Which model is correct? How do you know which model is correct? There might be multiple not just two, like there are a lot of things possible. So, there might be something like that, there might be something like this. So, the different lines are possible, which line is good or something similarly something not at all related to it. So, which line is good? How do you say this line is not good compared to other lines? How do you say this? This is the question. In order to do that it is very simple, we have to look at the objective function, you have to pick the line which gives the very least objective function that is we saw the objective function in our clustering example. Now we can see objective function is mean square error. Let us see how to compute the mean square error. The mean square error is identified by as a error means the error of predicted marks minus actual marks, the square of that. Let us see this example here. So, for attendance equal to 20%, the actual mark is 30. But the line, if you think about the line which is from the model, linear equation model gives you this particular line, which gives the value which is something related to some 35, some consider it is 35, it is not 35, but consider it is 35. So, the difference between this predicted versus actual does not matter which one is first because we are going to square it up, whether it is first or minus. So, the difference between this value 35 minus 30 is so, 5, 5 square is 25, even if it is minus 5 it will be 25. So, you have to use the least mean square method. So, least mean square method is basically compute this difference between actual and predicted in each point, sum it up and divided it by 6 values because the 6 samples you are finding the mean error, mean squared error. If you use the sum value also it is fine because both have a same relationship. So, compute that least compute that value for multiple lines. So, not just one line. So, you can have multiple lines, you can have one line here, one line there. So, compute that least mean square for multiple lines and pick the one which is best. So, consider a system started with the say a line like this and it found some error and it want to reduce or it want to go for the better model. How it go? So, the system will change the weight, based on the error system will change the weight. The training happens using different algorithms like gradient descent algorithm or some other algorithm. We do not want to talk about that in this course. The reason is it is not to understand how this model is created. If you are interested, go ahead and watch the videos by Andrew Ng and YouTube on Introduction to Mission Learning. He explains it very clearly how the weight is learned to train this linear regression model. That particular model gives this is the better score. So, say 0.799x plus 12.402. This 12.402 is actually intercept if you extend the line something like this it comes like that 12.402 that is where it cuts the 0. So, in this video we discuss what is linear regression and how to pick the best weight model. Thank you.


Zero accounting software 2023 bank feeds matching bank feeds to cash-based sales form.  First a word from our sponsor. Well, actually these are just items that we picked from the YouTube shopping affiliate program, but that's actually good for you because these aren't things that were just given to us from some large corporation which we don't even use in exchange for us selling them to you. These are things that we actually researched, purchased, and used ourselves. Here we have a western digital WD elements 20 terabyte USB 3.0 desktop external hard drive we use as part of our backup system noting that if you lower the number of terabytes of storage the price will lower dramatically as well. When you're thinking about a backup system you're usually thinking about an online system or an external hard drive system like this or ideally some combination between the two giving you some redundancy. You can also work directly from an external hard drive like this but there are some drawbacks to doing that. One being if you use this as your primary drive you're working from it's no longer a backup drive and you're going to need a backup system possibly another external hard drive and or some kind of cloud backup system and if you're working on something that takes up a lot of short term memory, a lot of RAM as you're working on it such as video editing the external hard drive can slow up the system so you might want to come up with some kind of system where you download the project you're working on to your computer to your C drive or possibly to a solid state drive which is a much more expensive external hard drive as you do the work once the work is done then save the project to an external hard drive such as this. If you would like a commercial free experience consider subscribing to our website at accountinginstruction.com or accountinginstruction.thinkific.com where we have many different courses you can purchase one at a time or have a subscription model giving you access to all the courses courses which are well organized have other resources like Excel files and PDF files to download and no commercials. We are in our custom zero home page going into the company file set up in a prior presentation the bank feed file duplicating some tabs as we do every time we're going to be right clicking on the tab up top so we can duplicate it and then we'll right click on the tab up top again and duplicate it again back to the tab to the middle accounting drop down going into the balance sheet report tabbing to the right accounting drop down again going into the income statement of the profit and loss report well that got kind of intense slow it down we're going to go back to the balance sheet the date looks good we're on the range looks good here let's go to the income statement changing the range to 2022 so we're on drop down 2022 January to December of 2022 and updated alright let's go to the first tab where our bank feeds are at accounting drop down we're on the bank account information which we've uploaded in a prior presentation we're focused on our checking account the top one up top drop down we want the account transaction so that's how I usually get there at least and then I'm going to go on over to the reconciliations so we're looking at deposits again this time we want to think about deposits that come through like a cash based system possibly like a cash register type of situation so let me see if I can find my deposit here so this is the one that we will be using now we've been trying to come up with systems where we're constructing our books from the bank feeds so that we can automate things as much as possible but now looking at those items that throw a wrench into the system making things a little bit more complex let's go over to our flow chart on this one and this is a QuickBooks desktop flow chart but we're just looking at the flow of the forms which is basically the same for any kind of accounting system we're on the customer or sales cycle and we're in a system now remember that we're kind of driven by the industry that we are in so the easiest industry would be like if we had gig work or something we could just wait till the deposit clears the banks their electronic transfers we could just add them to our income statements as they come through but if we're at like a point of sales system like at a cash register situation then we're still on a cash based system we're not tracking accounts receivable however we would like to generally record the transactions at that point of sale because that gives us an internal control to allow us to basically tie out how much we have received and tie that out to our various forms of payments whether that be cash or credit cards or that kind of thing so normally we're going to want to enter the sales receipts and then you can imagine a system where we have the sales receipts here and we basically try to tie out the bank feeds directly to the sales receipts in a similar way as we did with the create an invoice where we have the bank feeds tie out to the invoice so if you could do that even if you were to do that even if that was possible that would mean that when you make the sales receipts you could just deposit the sales receipts at that point directly into the checking account and then basically you could match out your bank feeds to what cleared on the checking account however oftentimes it's going to be a bit more of a problem because if you're getting paid at a cash register with different forms of payment you're either getting cash or you're going to be getting credit card payments or something like that which means there's a financial institution in the middle of the transaction so if it were cash for example you might be getting multiple cash receipts from the sales receipts and then when you deposit the sales receipts into the bank you're going to be depositing them as one lump sum therefore what's going to be seen on the bank statement is just going to be one lump sum and if you deposit each sales transaction into our system one at a time it's going to be difficult to reconcile so what we typically have to do oftentimes is make that clearing account possibly so that when we make the sales we record them into a clearing account and then we take them out of the clearing account and put them into the checking account in the same format as we'll be seeing on the bank feeds and that's also a system as we talked about in a prior presentation that might help us to deal with fees and that kind of stuff as well so we can come up with a system that goes into the clearing account remember that if you're matching anything and you have to match multiple things to tie out to a deposit to tie out what's in your system to a deposit form then you probably don't have the most efficient system the reconciliation should be easy so let me show you what I mean, let's say that we have this 1,015 we're going to go backwards and imagine what happened first we enter sales at like a cash register multiple of them that will tie out to this number so I'm going to go up top and say okay and that happened on sometime in October or September let's say, all right, drop down let's say we're at a cash register, receive money for them we're getting paid now here's where the choice comes in do we want to put it directly into the checking account now if we do, if we put it directly into the checking account we basically are just entering a deposit form and we can connect the deposit form to the bank feeds using the matching system however, again, it will only work if every deposit that we make is going to show up on the bank feeds for that dollar amount which won't be the case if we're using cash or if we're using credit cards or something like that so we set up a clearing account in a prior presentation which is just a cash account that we didn't connect to the bank feeds as a clearing account and so what I'm going to do is I'm going to first put it into that account and I'll say next, let's say this is going to be for customer customer number five, let's say customer number five and we got to bring this back to September of 2023 so let's bring it on back bring it on back to September where the grass is green and the girls are I don't know, I got guns and roses in my some band some song, I don't know what just get back, focus we're going to say $500 and the account is going to be sales and there it is, so this is going to be a money form so it's going to be increasing, not the checking account but the clearing account by $500 and then the other side is going to go into the sales account I almost lost it there let's save and add another save and can I have another so save and another and let's say this is for customer number six and we'll bring it on back take me back to the Paradise City where the grass is green and the girls are pretty September, let's say this happened September 1st as well and I have multiple talents as you can tell, singing musical acuity $515.25 let's do this one and this is going to go into the sales account as well and so this one, the $500 and the $515.25 will add up to the $1,015.25 is that right? yeah, that should work so this is also going to be increasing the clearing account by the $515.25 and then the sales account so let's go ahead and save that and we'll check it out so if I go back on over to the balance sheet I can balance this sheet on my pinky finger I've balanced the sheet okay so we're going to say this is going to go into the clearing account there's the $1,015.25 so here we have the $1,015.25 so which are the two transactions that we made of the $500 and the $515.25 now if I made these two transactions directly into the checking account then you can imagine that we might be able to kind of tie it out because I could go over here and say okay let's actually do that just well no let me just show you if I go back on over here and I go into my checking account accounting drop down and bank accounts and if we go into the drop in down and we go into the account transactions again and we go into the reconcile so that we can and then we find that $1,015. There it is now if I do if I deposit those two directly into the checking account it probably wouldn't have found them even though they add up to $1,015.25 but I could go to the match and I would have been able to see those transactions here I can't see them now because I deposited them into the clearing account but if I deposited them directly into the checking account I could go in here and try to match them off so you can imagine a system where you can try to do that but usually that's not the system that you usually want usually you're going to think how can I come up with an accounting system so by the time I get into the reconcile process over here it's going to be as easy as possible and that's why the clearing account might come into play because you might have other things that are involved too like fees that can be involved so that means I'm going to put it into the clearing account here as we did and then I'm going to transfer it from the clearing account to the checking account in the same grouping as I expect to be seen on the bank feeds and you have to figure out how you're going to do that depending on your system and why and how you're getting paid right if you're getting cash payments then the amount that's going to be deposited into your bank will be dependent on at the end of the night you're going to physically deposit the money into the checking account and so you'll know what that deposit is and that's why then you can take it out of the clearing account in the same grouping as you put in the bank if you have some intermediary credit card company PayPal, Stripes then you've got to figure out how do those systems work and how are they grouping my payments together so that you can put them you can take them out of the clearing account and deposit them into your checking account and if they also have fees involved in some way then you can see how you can integrate the fees and possibly the clearing account can help you to make sure that you take into account the fees and transfer them from the clearing account possibly into the checking account so now that they will be in the checking account in the same format as we'll show on the bank statements so that you can reconcile now I'm going to do this with a transfer form transferring from here to here remember that the transfer form is a little confusing because I just want to point out that I could use three forms to do this I could say if I want to take money out of here and put it into here I could do that with a deposit form but a deposit form will look correct over here so you might actually use that to receive money form but the deposit form will also be showing here as a decrease to a checking account now this is just a clearing account so maybe it's not a big deal but that still looks a little funny you could use a check form or a spend money form which would take money out of the clearing account which would look correct here but then you would have a check form that would be increasing the account which would look funny and if you wanted to sort, use filtering options buy one in here and try to filter my accounts using my filtering tools then I couldn't really filter by the source as easily because now if I would have transactions going the wrong way because normally the received money forms should be increasing and the spend money forms should be decreasing not the other way around so that's why you have a transfer form because the transfer form could be an increase or a decrease type of transaction it doesn't look funny on either side alright that's the point, let's go to the first tab and let's do a transfer form we're going to go up top and say transformer form transfer form more than beats the eye I would say beats the eye which doesn't make it meets the eye more than beats the eye more than meets the eye accounting it's going into the checking account it's going from the cash clearing account into the checking account and this is happening back in 2022 this happened back in 2022 let's say 17 FEB and let's say the amount is 1015.25 and let's make the transfer let's make the transfer transformation has happened that's the transformer song by the way in case people don't know transformers so now you've got the clearing account is gone and we put that amount into the checking account clearing account cleared out that's what happens with clearing accounts they go back down once the purpose has been served as the service of this purpose has been so here's the 1015.25 there's our transfer so notice the transfer form doesn't look funny it doesn't look like it's a spend money form that's like an increase or anything right so then let's go back and see now if the bank feeds should recognize now because we have the same dollar amount so now the bank feeds should be easy that's the whole point here the bank feed, the recognition should be easy so I'm going to go to the banking account adding not an adding what are you doing you're going to confuse I'm going to get notices for that people are going to tell me that I confused them managed account and then account transactions okay and then we're going to go into the reconcile and let's find and let's see if that match happens it see if it recognizes that's not the one it recognize that one that's not what we're looking here it is boom recognized it because now we put the dollar amount in the same dollar amount and so the reconciliation should be easy so you want to have a system where the reconciliation is like this right the system could recognize it because the dollar amount is coming in in the proper dollar amount and this reconciliation system should be as easy as clicking off boom that didn't record anything new by the way because we had to record it all on our side so you might be thinking what is this reconciliation thing doing for me in this case well it's helping us to reconcile with the bank reconciliation it's helping us that's what it's doing it's not actually creating the financial statements in this case because we have to do more of a full service type of system which isn't a bad thing it just happens to be what we have to do be we can't rely on the bank all the time because we have this accrual component that took place and and that and through a wrench into the system so bank feeds don't solve all the problems all your problems won't be solved with the bank feeds just like you know I try you know you could try bank fees to solve all your problems liquor drugs or something right none of it none of it saw love what it doesn't solve all of your problems you still have you still have to do you know it could help out a little bit and let's look at the let's look at the trial balance now I'm going to go to the accounting drop-down let's look at our reports and see what the trial balance looks like try pin in trial balance and there and so there we have it so we've been just constructing this as we go look at this look at this thing look what we've made it's amazing so the balance sheet stops at the equity and then we've been making the income statement the debits and the credits are in balance that says the double entry accounting system is working it's the same thing from a double entry accounting standpoint as going to the balance sheet and saying hey my assets equal my liabilities and my equity how does the income statement fit into this whole picture on the balance sheet if the assets equal liabilities and equity is the whole thing because the income statement is squished into this one number current year earnings 8624.79 which is the bottom line of the income statement if you update it I have to update it now it's the bottom line of the 8624.79 here this being our timing statement telling us how we did over the year period which is not bad I must say we're not getting rich or anything but it's still respectable I feel like that's legitimate legitimate a job well done but over here you can see that on the equity side we only we don't have that current number in the equity it stops at retained earnings so the current year earnings are broken out and so that's why we can put the balance sheet on top of the income statement without all the subtotals and use the debits and credits which is actually more efficient than the double entry accounting system represented in the accounting equation which is on the balance sheet assets equal liabilities plus equity and then breaking out the income statement which is the current year portion in a separate report of the profit and loss or income statement


Hello everyone and happy National Physicians Day. Today, we celebrate all of you, Canada's doctors and health professionals who do so much to heal our communities and keep people healthy. I know this is an extremely challenging time for all of you. You've been on the front lines of our fight against COVID-19 for over a year now. I know you're exhausted and I know it's been an incredibly stressful time for you and for your families. But despite that, you step up every single day and your work has been nothing short of remarkable. That dedication and that commitment to Canadians has saved and continues to save countless lives. For that and for so much more, I want to say thank you. And I want you to know that as we continue to deal with COVID-19, our government will continue to have your backs. We will continue to make sure that you have the supplies and support you need to keep yourselves and others safe and healthy. And together, we will beat this virus once and for all. And when we do, we'll be a stronger, safer and more resilient country because of it. Once again, thank you for all that you do. We as Canadians owe you so much. Please stay safe and healthy everyone.