What's going on and welcome to the NBA DFS Q&A. My name is Tom Vecchio I'm here for the next 30 minutes to take your questions about tonight's NBA slates You can follow me on Twitter at DFS underscore Tom You can find the daily ISO the daily NBA podcast right here on number fire comm You can find my articles. I do the three value plays. You can find that here as well Should be a good slate. We have we have a loaded at 10 games Of course, there's you know a ton of injury news that we have to pay attention to we already had LeBron James ruled out on tonight's slate. We are waiting on the status of Anthony Davis He was a bit banged up at the end of the game last night against the Spurs with this knee injury So we're waiting on whether or not he will be playing You know all of this could elevate Russell Westbrook as simply an amazing play considering he's only $8400. We'll get to some injury news I can take your questions about different players We can go through some some betting lines or whatever it might be Sports media on youtube is asking Westbrook ad stack tonight. I think that's Certainly reasonable and I think it should be very very popular It's just a matter of whether or not anthony gave his place. We have to wait on that wait on that information You know his list has questionable now I assume we won't be getting that in total very very close to tip Of course, this is an eight o'clock game. Hopefully we get it before line-up slot At seven I actually have to be able to pivot off of him If need be I I know I'm certainly looking to roster him just look what he did last night Going up against the Spurs with LeBron now just massive Fantasy points, you know a double double machine looking like he did when he was with the pelicans for so many years So player, I know I want to roster it's going to be Davis It's going to be a lot of Westbrook considering Westbrook's only $8400 He's looking like one of the absolute plays of the slate Just giving that usage giving his ability to push towards a triple double specifically with LeBron now And if anything, Davis is out it's going to be even greater tonight When it comes to some betting lines on tonight's slate I think there's some really interesting spots to attack some player props some game lines I like Memphis on the road I like them on the road with the points. That's a spot. I already took I took It was a Gary Trent player prop over two and a half threes for Toronto. I took CJ McCullum over 31 and a half points rebounds assist combined I think it's going to be a very high scoring game between Portland and Memphis. I think that this is a game that you want to have, you know, certainly some exposure to in DFS What else did I take? I took a bam out of bio to have a double double tonight going up against the brooklyn nats. We just saw Or we've seen over the past, you know a few games to start the season They're clear like lack of rebounding is very very apparent for them. The nets are Where the nets right here their lack of rebounding is very apparent in the early portion of the season You know opening night. It doesn't really matter Who they're going up against they're just giving up too many rebounds. They don't have a ton of size I know the mark solidridge and paul milsap can rotate in and out even nick clackston I just see bam out of bio going out there for like a 20 and 15 type of game an 18 and 13 type of game So bam out of bio to have a double double. That's where I also went tonight Also looking at brandening room over 34 and a half points rebounds assist combined A role player or I should not say a player that's elevated because there's no real Solid role player as a secondary option for the pelicans. I've spoken about this many times The solo on the daily iso to start this season I just think that he's going to be in a spot for plenty of usage with no zion this season Loveless match up overall Close ish spread with five and a half points of 219 over under we like to see that hit the over so that's a spot that I'm Looking to tonight Clayton tonight, which Clayton are you talking about? Which Clayton are you talking about who's on the slate? Andrew Carlton on facebook view could just clarify that uh, hopefully give you an answer on this slate Uh, I think that there's a lot of value plays that we could be looking at obviously there's a ton of injuries We also need to be focusing on uh, not so little could be in for uh, Norm Powell norm Powell is ruled out again. We saw not so little get the start for the past two games Claxton is You know Claxton is a player that I'm personally high on. I think that he has certainly some upside Um, you know his rotational minutes. We know are going to be fluctuating on a game by game basis where you know He's up over 20. He's below 20. He's producing under one fan to point per times He's producing over one fan to point per times. He's a gbp play at best for me He's not going to be in the starting lineup We know that lamarcus oligarch will be the first big off the bench usually for the net So I like him as a fine gbp play you're obviously hoping for this over one fan to point per minute upside But again, that's not something you can count on on a game by game basis So, uh as a gbp dart throw if you're looking to like expand your player pool, uh, that is fine Austin is asking tom we need to find a dorky mid-range type of head head fan to a point bet for tonight's slate Who do you like in the $6,000 range? uh Do you mean point guard or Austin is asking this of course filled in for me on the daily iso the other day does our Uh, does the nascar does the ufc podcast writes about nascar here if you're looking for a point guard in tonight's uh mid-range Uh, you know ricky rubio should just be a plug-and-play. I think that's fine Uh, hallow burton, uh, I love that scoring upside for him. Obviously not the easiest matchup going up against The suns but I like the the volume of shots that he can certainly put up Especially when deer and fox is off the court and starting to fill up the rest of the stat sheet as we saw Uh, sports is asking again. Can we trust zubats again tonight? Uh, I think you can I'm really fond of the clippers tonight I already took them minus eight or eight and a half whatever was Uh, I got them at uh, I think that's a good spot for them I think that were I think the clippers are a team that's going to be a little bit slept on tonight Where everyone's going to be looking to spend up, you know at the small forward range getting to a tenth cumpo With drew holiday out with low pez out. It's really just going to be a tenth cumpo millton running the show from the walkie Of course, plenty of people will play durant. I think tatum's in a great spot I think paul george is going to be slept on a little bit along with the other Uh, the other clippers players just because this matchup overall Uh, where is it doesn't shape up to be like the best fantasy environment? And obviously there are other games that are certainly a bit more enticing. There's certainly some blowout potential for the clippers You know two eighteen and a half over under isn't the highest on the slate So I think some of the clippers players could go a little bit overlooked and yeah, I do like zubat I do like paul george, uh, tonight Uh, does mccullum also go off tonight or is his cost too high? I personally like mccullum He's been playing really really well to start the season. Uh, he was on fire last year to start the season When he gets in these shooting streaks like he is a player that can knock down, you know four five six threes in the game As I said, I already took mccullum over 31 and a half or 32 points rebounds assist combined Right, uh, I think it's 32 and a half Yeah, I'm pretty sure 31 or 32 and a half for mccullum. It's 31 and a half here Seeing a minus 113. I I like mccullum over tonight I think he's going to continue to fill up those statutes, especially with littler like lacking in production recently Of course, I spoke about lillard to an extent on the daily iso on the podcast today because his salary is obviously super Super affordable and this is the type of matchup where he could really really go off Uh, you know for that 60 point ceiling. He uh, he's been known to have what do you think about valentunas tonight? Uh, you know, it's a it's a good matchup for him I think he's another player that probably will get slept on overall at center I know a lot of people will be looking to pay up. I think sabonis and adabio are awesome Options you could certainly spend down a little bit I'm still big on robert williams even with al whoreford returning to the lineup And I think a lot of people look to be a little bit creative with their roster construction Obviously mo bamba has looked really solid for elandos. Yes. I like valentunas tonight He's not my top option overall at center But I do think that the consistency should be there for him And again, I'm expecting the over in that game sitting at 2 19 and a half Uh would go along with hopefully my branded ingram bet Uh dj on facebook is asking which former bull is a better play gafford or window carter and stack the uh Stack the raptors wizards nets or clippers okay, uh So I would go with uh gafford for that one gafford and carter I would go with gafford and then stack the raptors wizards nets or clippers Well clippers could easily put up I don't know 125 points tonight. It could be a bit of a blowout against cleveland That's obviously within the ramble possibility. I I have a lot of interest in this Indiana and The raptors game, you know, it's got a very close spread only one point the overunder isn't amazing I'd be more interested in some of the raptors players than I am the pacers players If we look to, you know, offensive pace Uh possessions this year indianas all the way up at three, right? They've been playing a lot of high scoring a lot of close games You know kind of where they were at the end of last year toronto's all the way down to 28 in the league right now Uh, you know playing a bit slow. You obviously don't have as much offensive fire powers They did last year, but if toronto's gonna be playing up in pace against indiana I would side more with the raptors players tonight compared to the pacers players. So clippers raptors And then wizards, I think are all in play. You could say the nets have a tougher matchup Going up against The heat tonight That's certainly, you know, not the easiest matchup in the world So i'm a little bit hesitant on some of The nets players tonight, of course harden and gerand can always go off. I do like bruce brown As a value options tonight, but not trying to overexpose myself to the nets tonight austin saying it was a trick question the $6,000 range makes you upset Uh Lillard sellers lillard salary is $8,200 I am looking to have plenty of exposure to lillard You know, it's kind of you know, one of my I guess you call it like flag plants of the day where 8200 is simply too cheap for lillard Obviously not given the production he's shown this year, but given the production that he's shown in years past I expect some you know positive regression for his fandom points per minute his usage Obviously, he's been very very cold uh from downtown to start the year. I think he's only hit two three corners If I remember correctly, like he's o of is o of 9 207 and o of 8 from from downtown He's obviously a significantly better three-point shooter than those, you know, this three game sample size So i'm going to be looking to you know buy back in on lillard at a decreased salary hoping that he really goes for a ceiling gig DJ is asking of those four stacks. Which players would you start with? That's a good question You know zubach i'm totally fine with there paul george Reggie jackson, I think he's been you know sneaky consistent for the clipper So those would be the main three then I said the raptors I'm still big on scotty barns, you know scotty barns and pressures are Jewish should still be popular Vin fleet will basically always get there. His usage is going to be super high He's going to probably be one of the most consistent players in mba dfs But my kind of you know dart throw pick is gary trend Love the three-point volume from him The pacers are allowing 13 made threes from their opponents per game to start the season I took the over on gary trend Three-point prop tonight, which I think is one and a half two and a half Uh, two and a half. I took the over on gary trend's three-point prop. So I would go uh barns Vin fleet trend and then achua in that order, you know, I think achua is still going to be a solid option But I don't want to be factoring in everything just given the you know, his position I want to be Uh prioritizing other players at the center or power forward position Andrews asking need a value center right now I am rolling with plumlee but have my doubt time. So I think plumlee is a solid option overall The issue I have is it's more about this game environment where we're looking at a Uh, where is it? Matt, you know 220 over under and it's a five and a half point spread Um Would you be shocked if this game hit the under and the hornets control this game from start to finish? And it's a bit of a blowout just because it happens to be Orlando and we know Orlando is a super weak team You know, no, I don't think anyone would be super shocked in that scenario If the hornets control that game. So I understand plumlee being a safe option or a consistent option But his upside I think is a little bit lacking tonight just given the The context of the game. So if you have the salary and you can get up to john collins I think that would be great If you want to drop down and take the value with a chua who I do think is fine I think that's also an option and then dropping down to zubak could also be there. So I think zeller is fine tonight. He's just not moving the needle in a big way. I think some of the other The other options around him are great If a sports is asking and if ad is announced out tonight, who do you like to pair with westbrook? Now that's the real interesting question if anthony davis is going to be out tonight Westbrook is going to be an awesome play.  I assume it's going to be carmelo anthony That's my assumption. It'd be westbrook monk basemore anthony and jordan now of that lineup Deandre jordan and ken basemore aren't big like volume shooters, right? Where we're going to be looking at monk who certainly played well last night To certainly fill up some of the missing shots and then alongside carmel anthony westbrook is going to be there getting his volume You know getting his offensive usage But I do like carmelo anthony to step in and kind of fill some of that offensive void and get over 10 field goal attempts again So it would be westbrook carmelo anthony then malik monk for me in that order Neely on twitch is asking looking at these four guys for points rebounds and assist parlays Uh pg sabonis tray on the maculam. How do you feel about that? So I already took the over on maculam I already took the over on 31 or 32 and a half whatever was I forget Sabonis is like one of my absolute go-tos Just because he has like a double double or close to a triple double on a nightly basis. So I absolutely love that I assume you mean pg 13 instead of pg 3 pg 13 for paul george I I think that's it's a super easy matchup for him against cleveland I'm worried about the blowout potential because we're dealing with a Larger spread and I wouldn't be surprised if the clippers just kind of run them out of the gym So I'm a little bit lukewarm on that and then tray young of course the match up for tray young against the pelicans is Super easy like we're not going to be worried about their defense at all They're giving up, you know, 46 fan two points per game to opposing point guards tray young is going to be out there Dishing the ball is going to be out there You know putting up some three is like not worried about that at all again Kind of my the theme I'm going with I think this game is going to hit the over of 219 That's what I'm kind of buying into with brandon ingram. So of those four guys. I would say that mccullum is my favorite That's what I've already taken. I'll probably take sabonis Uh then tray young and then I assume that's pg 13 instead of pg 3 Grimm on youtube is asking nerkage or steven adams. Uh, that's a good question in this Uh, you know mid tier or so at center Uh, nerkage, I think is is a solid really solid option. You could say that the match up against steven adams isn't super easy But if the this game hits the over, uh, which I'm expecting it to and we're going to end if by chance Lillard continues a Lack of production the scoring from portland has to come from somewhere and it's I would say mccullum number one And then nerkage two, uh, I will say I did already take nerkage to have a double double at It's only plus 110, but I did take nerkage to already to post a double double So I am on board with that if you want to go to steven adams. I think that's also fine I'm not worried about the portland defense Uh, really at all. I don't think anyone should be worried about the portland defense He's obviously been super consistent to start the year. So if you want steven adams and nerkage to have a double double I'm not going to be surprised if you're asking me head to head. I would have to have adams odds Let's just see if his odds are posted for Uh, to post a double double to record a double double. Here we go I mean plus 200 is obviously better than than nerkage. So I if you're asking me head to head about those I would of course take adams at the at the plus money instead of nerkage at minus 105 CP three triple double tonight, um, you know that that's Let's see what his odds are because it's obviously, uh an option for For chris paul to do that the issue I have is the potential for a blowout Right where fenus gets up by a lot The kings can't keep up with them and then he gets pulled early plus 9500 It's obviously fantastic odds I would just prefer to to search for triple doubles in games with really high over unders and close spreads Just because we know that players will be out there for you know, the full game if chris paul only played 30 minutes night and the the sun's win this game Easily i'm not going to be surprised because it's a match up against sacramento So if it were a little bit closer spread a little bit higher higher of an over under I would have a bit more interest Uh, andrew on facebook is saying I was at naquilla. I was at a walker solid plate tonight Or does a potential blowout hurt him? I'm a big fan of na w Player that I wrote about at I would say many many times last year I think that you can be looking to him basically on a nightly basis. I don't think he's a threat for A blowouts just because we know that the pelicans are lacking offense And if he's going to continue to see this volume of shots, which is absolutely awesome Like I want to say really being a secondary score behind ingram because Ingram's volume of shots to start the season is awesome 20 or more in four straight games And we're seeing na w at 16 or more in three or four. I think that's fantastic He's obviously shooting a ton of three. So I am fully on board with na w basically Every single night In total something like dramatic changes Within their rotation Zion comes back and he starts to lose field with temps We will worry about that when we have to get there, but I am on board with na w each and every night $6100 I think is a super fine salary and again This is another game that I could expect to hit the over Whereas we're on 16th right now in pace. Atlanta is 22nd We should see Atlanta playing up in pace, you know The pelicans want to keep the scoring going to keep up with lantha who ultimately is the better team Austin is saying I'm worried about nurtures minutes like some I look at more than I do Definitely think a double double can happen 18 minutes though Yeah, I'm the minutes aren't super amazing But this game just trending the way that it is with a 233 and a half over under 234 and a half over under jumped up a little bit of two-point spread You know, it should be super close. All the starters should be out there for plenty of minutes Uh, you know, I want to buy into You know, I don't know if it's you want to think of it a little bit of it's hedging But I as I said before I said on the podcast. I said it now. I want to have a lot of lillard in Dfs tonight at $8200. I really think it's a good match up. He's just He's going to bounce back at some point Right. He's just too good of a player to continue to produce, you know, this uh, this little to start the season so i'm buying into Uh, dame big time in dfs and then i'm also betting on nerkage to hit a double double because a dame You know busts in dfs and he doesn't work out at least hopefully nerkage picks that back up And does get the double double So I don't know if you want to call that hedging or whatever it might be but that's something that I like to do so i'm on both sides of it grim is asking bruce brown or patty mills I wrote up bruce brown today as a value playing On number fire.com. I do three values every day He is grading out as one of the better Or is it one of the better guards? I think tonight he was projected obviously it's going to change with the lebron james You know, so it's obviously not going to be the same but i'm big on bruce brown Just love his minutes are going to be consistent If I can find him here brown Oh, he's still at the top So he's projected highly. I like his ability to you know, fill up a stat sheet. You can knock down a couple threes Grab some blocks and steals. I obviously provide tremendous fantasy value But the upside the upside lies with patty mills. That's where the upside comes from The volume of threes he's taking off the bench is really encouraging to start to start the season 679 in these three games over 10 field attempts off the bench in a crowded offense In three of the four games he's going to be playing a ton of minutes So I do like bruce brown, but I like patty mills a little bit more based on this potential upside Uh, obviously bruce brown doesn't have the same scoring ceiling that patty mills does So I would go with patty mills over bruce brown Uh, why is fangal begging us to play russ and dame tonight? Well, dame's been struggling. That's uh, that's absolutely a fact. Uh, his Production is obviously just not where we expected it to be to start the season But he's and that's and that's you know, it just that's where salary should be He's been lacking production now Obviously the salaries came out for tonight's slate last night as the slate locked Obviously they came out, you know, it's seven to one right after the slate locks and Westbrook didn't go for 63 points before the salaries came out and now we have the news that LeBron James is out and anthony davis may not play who's listed as questionable. Hopefully we can get an update on this Uh, it's just a russel westbrook kind of night And I don't know if you want to factor in that he's going up against okloma city his former team Take that for what you will but With no lebron with the potential for no anthony davis If anthony davis does play how healthy is he is going to be one of those situations where davis plays and he starts and then 16 minutes into the game he goes to locker room and then doesn't return and then it's just the russel westbrook show I'm in on westbrook tonight. I am fully in on him. He's going to be one of the players. I have the most exposure to tonight sports is asking Who's your top fantasy player tonight in the $4,000 range? At a specific position or just overall because if you give me like a specific position a little more detail That will make things a little bit easier on the $4,000 range Well, bruce brown is there. So I certainly like him. You could molly bunk. We know is going to be super chalky Especially if davis gets rolled out. I expect him to be popular to begin with I'd like some of the production that we've been seeing from anthony simons off the bench for the trailblazers You know, he's picking up some of the offense of slack if dame is struggling You know his minutes should be around 20 per game I can certainly get that three-point shot going if that game hits the over. Hopefully he will be producing as well Shooting guard. All right shooting guard in the $4,000 range. So I'm going to lean again to Bruce brown. I think that's fine patty mills is 4500 also a fine option Malik monk is here lukinard, you know, I don't know Where his shooting comes from sometimes, but if he can get this hot He makes a nice a gvp option again a player that shouldn't be super popular Ken baysmore, I assume will be somewhat chalky just because The increased offense of it availability for the lakers But there's not too much down here gary trend is how much is gary 20s 40? Yeah, 4600. So I would take gary trend. I would take patty mills bruce brown These sorts of players in here. We're looking for some, you know, some scoring upside I will say joe harris not to add a third nets player in here, but I believe he's only two or three three pointers away from Setting the record for the nets for the most all-time Three-pointers and nets history. I think he's about to tie or pass jason kid So if you want to buy into that that's certainly an option, but I would stick with monk simons patty mills those sorts of players Oh, you meant small forward. Okay small for we're going to small forward But those are some shooting guard options at the $4,000 ring. So the $4,000 range at small forward Obviously, we're gonna have some of the same players Uh crossing over with the dual position eligibility Uh But tomb could be a sneaky option Kevin herder gary trend again not so little. I'm pretty big on not so little especially if Norm Powell is out again Really solid minutes for him. Obviously, don't worry about this first game. He obviously didn't start But in the games that he did start Really solid production from not so little a great game environment going up against The grizzlies 234 and a half over under it's a close spread. So if pal was out I like little if you need to go a little bit cheaper I could look to kevin herder gary trend Again, maybe joe harris if you want to buy into that Uh, no one like is jumping off the page at me tonight You know Yeah, bobby portis doesn't have dual position eligibility. It's it's yeah, it's power forward center So I do like bobby portis. I was one of my favorite value plays, but he's not a small forward Do you plan on being overweight more to yannis or carl if any talents tonight from diffuse on youtube? Uh, I plan on being overweight on yannis tonight 11,200. I think that there's obviously a ton of value on a 10 game slate and just given the Potential production that he brings on a on a nightly basis when they're fully healthy is obviously unbelievable Removing brook low pez removing Drew holiday from their lineup I think grants him another 60 plus points tonight, you know 55 65 points or so tonight That's really where he should be a few extra rebounds with no Brooke low pez. Yes, I know bobby portis should be in there. He's taken a few more threes this year, which is good to see You know, he's more confident pulling up in shots, which is good to see. So my answer is yannis A little bit overweight on him compared to carl until he towns Was curious to hear tom's answer on that monk is the obviously obvious chalk dork 4,800 was much better last year, but he's getting He's getting men's tarant span that shouldn't see a bump without morris senior I agree with all that more. Malik bunk is clearly the chalk at Shooting guard slash small forward whatever you want to call it with Lebron james out central for anthony gave us to be on malik monk Really should be out there for again 30 to 35 minutes Plenty of field goal attempts from him The 10 field goal attempts obviously is a bit of an outlier that game was super close Went to overtime. So we can't be trusting on 10 but 5 6 maybe 7 3 point attempts from malik monk Certainly in play hopefully going for 30 plus standard points. I know I will have plenty of shares of malik monk Just because it's such a great spot for him and Really anyone on the lakers. We're not worried about the match against the thunder. Yes, the 216 point over under isn't amazing With You know a lower over under considering some of the other options we have on the sleep But the usage and volume of shots malik monk could be seeing would be awesome Uh franz Wagner for the magic. Yeah, I think that's a fine option. Uh magic are here Uh, I think it's you know, he's fine, you know, he Ultimately, he doesn't move the needle for me a whole lot just because if we look at the offense overall for the magic We know that you know, a lot of the shots are going to be going to call anthony jail and sucks And they're certainly getting mo bamba involved involved a lot Again, not overly worried about the the horton's defense or anything I just know that you know a certain point he's going to have a limited ceiling just because he's not the primary Option on offense for for them. So that's what it really comes down. So I think he's fine Totally fine option. Maybe I'll mix in a few shares of him I think his salary is obviously very affordable tonight. I think you can drop him in some lineups, but You know, yeah a good option at like a good option. Just not Amazing if that's the best way to put it. Uh deandre bambry any value that's a pass for me Uh on the nets just the minutes are just inconsistent We can't be trusting that just because he happened to play 18 in the most recent game against washington You know dnp in one game and seeing, you know Five total minutes in other two games is not something you really want to be trusting They're they're better options at that low end. Uh, you know shooting guards small forward We're off to a slow start. I he's going to score 50 soon. Derek on fendal. Uh, excuse me, Derek on Uh facebook is saying going to score 50 soon. Maybe tonight. Yeah, I am a big proponent of that I spoke about lilorid on the podcast I spoke about lilorid here again I'm buying back into lilorid at $8200 as a player that we know can outperform his salary Hopefully a lot of points in that game. Hopefully, uh, a lot of fantasy value for all involved All right, so that does it for today's mba Q&A I'll be back next week next wednesday for another edition As always you can follow me on twitter at dfs underscore tom until next time. Good luck in your contest


In this session, we will build a deep learning model for regression. In the last session, we build a deep learning model for classifying fashion accessories into one of the 10 categories. So, in this particular session, we will build a regression model for predicting fuel efficiency of vehicles. In regression problem, the output is a real number which is like a price or fuel efficiency measured by miles per gallon. Contrasting this with classification problem, where we aim to select a class from list of classes, a regression problem strives to predict a real number. In this particular exercise, we will use a classic auto MPG dataset and build a model to predict the fuel efficiency of 1970s and 80s automobiles. To do this, we will provide a model with description of many automobiles from the time period. These features include cylinders, displacement, horsepower and weight of the automobile. As usual, before starting the notebook, let us connect to a collaborant time and install the useful softwares which are not present. First we will install a software called C-Bon for plotting a pair plot. So, once the C-Bon is installed, we will import some of the plotting libraries like matplotlib.py plot, we will import pandas for manipulating data, then we will import C-Bon for pair plot and we will also install tensorflow 2.0 and install tensorflow library. We will also import a keras and layers library from tensorflow.  At the end of this cell, we make sure that we have the right version of tensorflow present in our collaborant time. We ensure that by printing the TF version which is 2.0.0 beta 1, which is the desired version for this particular exercise. The auto-MPG dataset is available from UCI machine learning repository. Our first job is to get this particular data. In the last exercise, we, the dataset was present in the tensorflow datasets and it was easier for us to import and load the data in tensorflow. In the case of auto-MPG, this dataset is not available in tensorflow, so we will have to first download the file and then load the data using pandas and we will import the data from pandas data frame into tensorflow. Let us first understand that particular process. So, at the first step, we will download the data, we will use kerasutils.get file function for that. The first argument is where we want to store the data and then the second argument here is the URL of the file containing the auto-MPG data. So, let us run this which will download, this particular cell will download the data and the data is now present in auto-MPG.data as printed by the dataset path. Let us import the data using pandas data frame. We will first list out the column names. We have column names like miles per gallon, cylinders, displacement, horsepower, weight, acceleration, model year and the origin. We will use pandas.readcsv function because this data is present in a csv format. We will read data using read underscore csv function of pandas. This particular function takes the dataset path, column names and we tell how to handle the NA values. So, we want to replace NA values by question mark. We want to ignore anything after the tab, so that we specify using the comment argument. The separator is a space and we want to skip initial spaces in the file. After specifying all these arguments, we are able to read the content of auto-MPG dataset in the data frame, in the pandas data frame named raw underscore dataset. We will make a copy of raw underscore dataset and copy it into a dataset. Let us run this code cell and examine what is there in the dataset. So, we printed last 5 rows of the dataset using dataset.tel command and you can see here there are features like mpg, cylinders, displacement, horsepower and so on. Most of the features are numeric features in nature whereas origin is a discrete feature. It just has a value 1 or 2. So, this particular part takes care of downloading the data and loading that into pandas data frame. Once the data is loaded, our next job is to clean the data and remove null values and perform normalization on the dataset. Let us take steps to pre-process the data. So, let us first find out if dataset has got any null values. So, we essentially use isNA function and then followed by some function to identify columns where null values are present. As you can see on the screen, horsepower column has few null values. In order to keep this tutorial or this exercise simple, we will simply drop these rows. So, we will drop all the rows containing null values using function dropNA. Next, we will convert some of the categorical attributes into one-hot encoding. So, we just saw that origin is a categorical attribute and we will use one-hot encoding to convert that into something that is usable by the machine learning algorithm. So, let us pop the origin column from the dataset and replace the origin column with 3 values because origin originally has 3 values USA, Europe and Japan. So, for USA we use the value of 1, for Europe we will use the value of 2 and for Japan we will use the value of 3. So, let us do this particular step of converting the categorical attribute into a numeric attribute and you can see that whenever USA is present, you will see the value 1 appearing, whenever USA is present, Europe and Japan are obviously not present. So, the values are 0, whenever Europe is present you will see a value of 1 corresponding to a column Europe, whenever value is 1 that column corresponds to, whenever there is a value of 1 in the column of Japan that corresponds to the third value in the original dataset. So, this is how we converted our numerical attribute into a categorical attribute and we have a transformed dataset. Now that we have preprocessed and cleaner dataset, the next job is to next job is to split the data into text, into test and training set. So, we will simply use dataset dot sample or sample function on the data frame where we specify the fraction of examples that we want to use in the training set and we will set a random seed so that every time we run this particular colab we get essentially the same training set. So, let us run this particular command, this will select 80 percent of the examples in the training and anything that is in the training we drop and the remaining examples are copied into test underscore dataset. So, running this colab we have two datasets, one is train underscore dataset and test underscore dataset containing training and test examples respectively. Let us have a quick look at the joint distribution of a few pairs of columns from the training set. We will use sns dot pair plot for plotting the joint distribution of a few pairs of columns and we do that for columns like mpg miles per gallon which is our target column or the column that we want to predict cylinders displacement and weights. Let us run this particular code cell and look at the pair plot. So, in this particular pair plot on diagonal you can see the distribution of the values in each of the features. So, here you can see the distribution values in mpg, this particular figure shows distribution of values from the feature cylinder, here you see distribution of values in displacement and in this plot you see distribution of values in weight. The op diagonal elements essentially show the relationship between between between between between the feature on the row and the feature on the column. For example, this particular this particular plot shows relationship between cylinders and mpg. This particular plot shows relationship between cylinder and displacement. This particular plot shows relationship between weight and displacement. As you can see that there is hardly any relationship between cylinder and mpg. So, cylinder and mpg seems to be uncorrelated whereas, displacement and weight have some correlation with mpg because as weight goes up mpg seems to be coming down the same thing is happening with displacement and as displacement increases the mpg seems to be coming down. You can see that mpg and weight are correlated features because as the weight increases displacement also tends to increase. So, we can get some useful insights into the features by looking at this pair plot and how this features affect the outcome can also be seen in this pair plot. Let us look at the overall statistics of the data using described command. So, we first use described command on the data set and obtain training stats we we remove the field for miles per gallon because that is the field we want to predict and we do the transpose of the stats so that we can display it in a nice tabular fashion. So, you can see all the features of the model on the row side and on the column we have various statistics of for each of the features. So, you can see that there are 314 rows in the data set that is why we have count for each of the feature is 314. Then we can look at the mean of every feature then the standard deviation and pretty much the mean max and few quantiles in between. As you can see that different features are on different scales for example, minimum number of cylinders is 3 and maximum number of cylinders is 8, minimum weight is 1649, maximum weight is 5114. So, you can see that the features are on a different scale and in order to bring them on the same scale later will perform normalization operation. Before doing normalization we will split features from the label. So, right now in our train underscore data set we have features as well as labels. So, we will use pop command and we will remove the column corresponding to the label the label column. So, we essentially remove miles per gallon column and that gives us the training label and using the pop command on the test data we get the test label. Let us normalize the data leaving aside the labeling column. Now, this normalization is a very important process. So, we want to make sure that we do we perform exactly the same normalization on test data as a training data or any other future data set that is coming to the model for prediction we apply the same normalization. So, in order to do that we will we have already calculated the strain statistics we define a normalization function that does the z score normalization which is defined as the value minus mean divided by the standard deviation. So, we calculate we perform normalization on the training data as well as test data and we will also store these normalization parameters which are present in train underscore test and we will apply them for the prediction as well as any test on any other test instances. Now that we have explored the data and pre-processed it the next step is to build a model. Here we will try to build a neural network model to perform the regression task. Let us look at the architecture of neural network model that we will be building. Before proceeding further I would like you to I would like you to pay attention that we have 1, 2, 3, 4, 5, 6, 7, 8, 9 there are in all 9 features in our model. So, we will we will essentially use a Keras sequential model or a feed for a neural network with one with couple of hidden layers. The first hidden layer is a dense layer with 64 unit. The second layer is also another dense layer with 64 units. In both the layers we use activation as ReLU and we have finally a dense layer with exactly one unit. Let us look at this particular architecture on a board. So, again coming back to neural network we have to fix the architecture of the neural network. We need to specify number of hidden layers, number of units in each hidden layer. You also specify the input layer or what is the input looking like and output layer. So, here we are using 2 hidden layers each using 64 units. Let us say we have 9 features and output layer we just have one unit. Let us let us draw this pictorially. So, we have an input layer and we have first hidden layer containing 64 units, then we have second hidden layer which also has 64 units and we finally have one hidden unit or one unit in the output layers. These are the hidden layers, this is the output layer, this is the input layer and we are using dense layer. So, in dense layer we connect each node from the previous layer to this particular node and then all the 64 units. There is an additional bias unit on each of the hidden units which I am not showing here explicitly. So, each of the hidden units, so we write down the names of parameter, there is one parameter on each of the arrow plus one additional parameter which is a bias. Here we have 9 plus 1, 9 coming from input plus 1 coming from out, 1 coming from bias. So, we have total 10 parameters per unit in the first layer. In the second layer here we have 64 inputs from the previous layer plus 1 bias parameter. So, we have 65 parameters per hidden unit and here we have 64 inputs plus 1 bias which is again 65 parameters. We use, second thing we have to care about is what kind of activations we will use, we will use ReLU as an activation function here, also use ReLU as an activation function here where we do not explicitly specify activation function, so by default a linear activation function is chosen. So, whatever is the linear combination that we saw in the last slide, in the last lectures, so we have essentially x i, w i, i is 164 here in this case. So, this linear combination is outputted as it is out of this particular node, so linear activation. So, this is how the model will look like, we will use this particular model, let us code this particular model using tensorflow.keras API. So, you can see here we wrote a special function called build model and this particular part of the code is building the model. Layers.dense defines the first layer where we use dense units, we are going to use 64 such kind of units using activation of ReLU and the input shape is exactly equal to number of features that we are using in the training data set. Next we also stack another dense layer with 64 units which uses ReLU activation function and finally, we have an output layer containing one dense unit. After specifying the model, let us also specify the optimizer and the loss function. So, in case of regression, we use minimum squared loss as a loss function which is the standard loss function for regression and we use RMS prop as an optimizer. We are using RMS prop with learning rate of 0.001 and we track metrics like minimum absolute error and minimum squared error. Let us look at mean squared error and mean absolute error and let us look at mathematically what do they mean. So, in regression framework what happens is that we learn, so this is our training data for example and let us say this is the model that we learnt. Now we make end up making some error on each of the points where you can see that the point on the line is the predicted point and the point denoted by cross is the actual point. So, there is a difference over here there is some difference here there is some difference here and so on. So, if we essentially look at every value of y which is yi or let us use like the different notation. So, we take ith value of y and we look at the predicted value of ith example and squared it. So, this define a squared error per example. If we sum it across all the example we get some squared error and if we divide it by 2n we get mean squared error. Instead of doing the square instead if we just use look at the difference or mod of difference between the true value and the predicted value and average it out this is called as mean absolute error and this is called as mean squared error. Let us go back to colab and now train the model. So, let us run this particular code cell and let us build a model. So, now we have a model let us see how model looks like. So, you can see that so, this is a model summary we have a sequential model or a feed for a neural network we use the dense layer to begin with which has got the output shape of which has got 64 outputs and then we have we are using essentially 3 dense layer 2 of them are hidden layer. The last layer is a last layer is an output layer last layer has exactly one output. So, there are 65 parameters then there are 65 into 64 that is the number of parameters over here. Now that we have built a model let us try the model on few examples. Let us take a batch of 10 examples from the training data and call model.trainit on it mind you I mean we have not yet trained the model, but we are using randomly initialized weights and seeing whether model.predict works. Yes, it seems to be working and it produces result as expected of shape and type. So, we use this in order to make sure that the model has been set up properly and all shapes of the tensors are set as expected. Let us train the model for 1000 epochs and record training and validation accuracy in the history object. We here use a print dot as a callback so that we can print a dot to record the progress of the model. We give the normalized training data and corresponding labels as input to model.fit function and we record all the history in the history object that is written by model.fit function. Let us train the model. So, model is training as you can see from the progress and once model is trained will visualize the history data whatever is stored in the history data it seems model is training is complete. So, let us look at the last 5 rows of history data history will have 1000 rows one for every epoch. And you can see that how the loss is actually it is very interesting to note here loss is quite fluctuating loss is going up then coming down then again going up and again up. So, let us look at what is happening to the validation loss.  So, let us plot the history and see how it looks like interesting. So, you can see that training data in terms of mean absolute error the training error is going down but validation error is going up the same thing we observe in the mean squared error that while training error is going down validation error does not seem to be improving. So, this points to some kind of a some kind of a overfitting overfitting problem in this particular data set. So, we will in order to overcome this particular overfitting problem we will try to use early stopping as a means of correcting this issue. So, we will stop the model before it starts overfitting. So, we will look at we will keep an eye on validation error and use early stopping call back for stopping the model before it starts overfitting. So, early stopping call back is set up in this particular way we use keras.callback.earlystopping where we use validation loss as a monitoring mechanism and we wait for 10 epochs if in 10 epochs validation loss does not improve then we will then then then we decide to stop the model and we use and let us train the model again with the early stopping call back and the prints.callback and we will plot the history to see how model does this time. Nice this model looks much better compare with the earlier model earlier model training error seem to improve while validation error became worse or at least stayed the same. So, that was an overfitting problem, but after doing early stopping regularization, regularization is one way of addressing the overfitting problem. So, we applied early stopping regularization here and after applying early stopping regularization we see that training loss and validation loss or validation error both seems to be improving as we keep training for more epochs. And we stopped after we saw that for 10 iteration validation loss does not improve so we stopped so that that is what we did with early stopping. And we can see that in terms of mean absolute error the training loss and validation loss are pretty close by both in mean absolute error as well as in mean squared error. So, let us evaluate the model on the test data and obtain numbers like loss mean absolute and mean squared error. So, we have mean absolute error of 1.85 miles per gallon. So, now we have a model we will use the model now to predict the miles per gallon for the test data. So, here we will give normalized test data as an input and ask the model to predict miles per gallon. Let us plot the predicted value of miles per gallon. So, we have true values of miles per gallon and the predicted values of miles per gallon and you can see that in most of the cases predicted values the true value and the predicted values are actually close by and our model seem to be working reasonably well. Let us also plot the error distribution the mean absolute error distribution. Mean absolute error distribution is not really Gaussian, but we expect we might expect that because here we have very small number of sample we just had 314 rows out of which 80 percent were used for training and only 20 percent were used for test. So, this brings us to the end of the regression exercise. In this exercise we learnt how to use deep neural network model for the task of regression. We also studied how to remedy or how to prevent overfitting using early stopping as a regularization mechanism. In the coming session we will look at how to use this deep neural network model to make predictions on the structured data. We will also study how to store and retrieve the model for the deployment purpose and also study underfitting and overfitting through some practical examples.


TLO was pop We are on kick. No, we're not okay. We're on twitch But we not live anyway, so you can leave a like comment subscribe turn on your post notification bells Let's continue to grow the family from Chicago to the UK if we do go live and you miss it right above me This is where you can catch any of the highlights and things of that nature. Don't forget. We do got merch I'm not wearing any right now because it's dirty, but we got it And don't forget we'd also got patreon as well man. We post on there Monday through Friday, man Tomorrow's Monday, so be ready man. The link to all of this is down in the description Just hit link tree and that with the angle pop up As you see I rushed through my intro because I'm ready for this Liverpool kids attacks twitch streamer My fellow scouts people what's our lord? So that's a jog up now y'all ain't doing that to me I ain't gonna hold you but that's how y'all moving out there. This is this is cool.  Whoa Cook sucks Out he said faces were blurred To not give them the attention they wanted beginning my stream in Liverpool England with technical no, no, no We're gonna give them the attention they wanted Here I am some people wake up and check their phone mute I'm gonna watch this whole you know what I'm saying. I'm gonna watch the whole video though With my shoes That wasn't even on camera you did that for nothing No, do you guys expect I don't I don't want to see the preview I gotta see I gotta watch the entire video because I want to see the build up to it This is why I have you guys on my shoulder and I have this cell phone in my hand It's because I found out that um, I lost my selfie stick and imagine being an iris streamer with no stick I know these selfie sticks will break or I lose them so I got a spare one But I don't have a spare phone holder So he's not from England obviously he has no accent or the uk but for the stick Maybe he's just visiting Fast forward here. I have a question for you Wherever your business needs to fast forward right into a commercial didn't we salute Hey, w branding you for a cell phone? On the website it says you guys had two of them. Okay. He's looking for this will do Had a grand adventure blah blah blah Too very boring minutes later and that was what's going on the past few days and now that I'm finally free You know, we've had this internet problem What's up guys Ah, you wouldn't it's uh Chinese you heard of doyen. Hey guys.  I'm pretending. I'm practicing practicing Victor underscore the underscore and paler gifted five saps Looking for the way again He said that's because of us the scouts has got you that What's your name on it?  I'm serious.  I laughed too because I couldn't understand. Hold on. How many subs he got? 23.4 this video took him to a new level man. That's crazy You shouldn't be onto it. Anyways, you gotta be 13 and older. Are you guys gonna be around here? What are those? What's wrong with my shoes? Hey, not everyone can have nikes like you man. They're not air max is my boy.  Yeah. Yeah. All right. I'll be I'll be back.  Not enough Not enough What I am what's up, brother I mean, what are you gonna do when you get on? Hi my guy. I'm not a zoo animal You got to know that this is coming though.  I charge $20 for a shout out like that. Does this scam for 2000 people?  I'm a question. Uh, ethernet cables. Is that the only aisle that would have some? Got it. Okay. Thank you gonna lose my wallet. No, they're not gonna keep my wallet guys That wasn't even on camera. You did that for nothing life It's great entertainment scouts Why would you fake a fall like that?  Can you give me a right to Tesco? You need to save me How much just copy the one that he took a picture of me Yeah, let's go. I love this.  Hey, he lit right now up there Hey, I'll be having a time of my life interacting all that. I don't care He kind of scared though. He kind of like Sign away from it. Not me man What a great You said hello many times already That's what everybody always tell me you're not gonna convince me not to come to the pool You know I'm saying the pool like that's the homies at the end of the day till you guys showed up No I don't know what that means. You say you say no, please. No What say you guys are crazy. I say no, no, no, no, I don't know what that means That's just like saying I don't think so Superb cap that is super You too, what's God No, God, please. No No, man, dude, you might as well start lightly jogging Good. That's I it's it's weird, man. It's weird.    Okay smart man though. Never go directly. Where are you supposed to go when they get on that? Okay, let's go Down in the street.  I'm walking. Hey, come on that way. I gotta run into the outside To these kids again. I got no choice. I gotta go this way Ah, look who's coming back take a uber I don't know what to do. I scared them. No, I did not clearly The whole gang the whole gang I'm not talking to you Agreed That's a smart man Oh, they didn't came back on the pedal bikes He got a satchel across him. He he might be young but he with it And he's them as air max is I already knew it but like come on now Can I help you? You guys must not have any hobbies I say you guys must not have any hobbies. This is not going down the right way You gotta be friendlier than this You get negative You on they home court. You can't get negative.  They will go aggressive now. Why do you think they're upset? Is my upsetness does that not mean anything to you?  No faces. Don't get in my video simple Ice cringe Ice cringe about to get negative Let's go What should you do on it? Don't answer that Do not answer that Because in Chicago somebody asks you you good. That means you want to fight They want to they want to shoot the ones with you they want to they want to bang out with you They don't do it. I'm all right. Are you all right? Oh my god, he could ask the same question I'm sorry Can you like give me some space brother?      Let me just push all the buttons now Oh Is it done?    You don't want to do nothing I guess so I was minding my own business walking to test school one of the kids had a mask I mean they were harassing me all the way over here, but one of them when I was down here He put on his mask And punched me in the head. I haven't on camera life, you know, it's gonna show us.  I don't know.  Are they gonna get their brothers or something to find me? Uh, I have a hotel in the city center Give me a lift back. Okay I mean, here's the thing like I'm not trying to ruin their lives here, so That's that's true. Yeah Yeah Yeah, sure. I've never been in a patty wagon before Okay, sure. Sure. This is all fine. Nice.  This is a normal thing in LA live streaming In Liverpool, they don't probably see many people like this doing this like Oh, yeah, yeah, yeah, sure. I'm carrying the camera. I'm carrying one of the male slap me on full police or Brolin signed An affidavit. Okay deal Deal Okay, okay Okay, we'll take you down to the three grace.  That's rough A lot of buildings. They're absolutely beautiful. I think we got the gist of it. Shout out to the Liverpool, man You know, that's the homies out there.  I'm gone


Hey Kevin here! Welcome back to Invisible Hands, the series that takes the mystery out of economics. Hey Fiona, here's a riddle. Okay. It's something that everyone participates in every day, but few people really understand. We get almost everything we need from it, but it's not a thing or a place. Can you guess what it is? Um, ghosts? No, guess again. I know! You're talking about markets! Ah, hi Professor. Hold on, markets?  Well, okay, markets are also a place, but in economics, they're a process. That's confusing. Yeah, kind of is. Uh, Professor, help us out here. Uh, alright. Markets of the name we use to describe the complex network of voluntary exchanges between individuals in an economy. Every time you buy, sell or trade with someone else, exchanging value for value, you're participating in the market. So I can be part of the market even if I never leave the house? Yes, but there's a lot more to markets than just buying and selling. Markets not only help increase the wealth in a society, they also transmit tons of important information about what people value, and they even promote trust and cooperation. Really? Markets sound super. Supermarkets. It's like a, it's like a pun. Come on. No? No. Anyway, in order to understand how markets work, you first gotta understand a few things about them. That's right, five things to be exact. Voluntary exchange, gains from trade, specialization, division of labor, and emergent order. That sounds like a lot, again. Don't worry, it's all related, and once you understand the basics, everything will make sense. Let's start with voluntary exchange. That's really just another way of saying trade, which we define as the act of freely and willingly exchanging goods or services. No money. Yeah, or money in return. Well, that's simple enough, but why does it matter if trade is voluntary? Remember how we talked about subjective value? Oh geez, how could I forget? Voluntary exchange matters because it's the only way we know how much the individual people involved actually value the goods or services they're trying to get. What do you mean? Well, as long as people are free to say yes or no based on their own preferences, we know that when two people agree to trade with each other, both parties expect to benefit from the exchange. Otherwise, they wouldn't make the deal in the first place. Yeah. Oh, duh. So when people agree to trade, you're saying that they choose to trade because both sides believe it will make them better off. That's exactly right. Trade moves resources from folks who value them less to those who value them more. So even when no goods or services are being produced, people can actually get wealthier just by trading with each other. Yeah, like when you bought that pickle and pineapple pizza, you got the weird dinner that you wanted and the pizzeria got the money that they wanted. It's a win-win. Yes, that's true for individuals and it's true for societies as a whole. Because society is made up of individuals, right? Yes, but it gets even better. Trade encourages people to build trust and cooperate with each other. Some economists consider voluntary exchange to be the cornerstone of civil society. There's even evidence that trade between nations helps to prevent war. That's what you meant by gains from trade. Absolutely. Cool. But what happens if we force people to trade with each other? We'll talk about this more in another video, but unfortunately we don't get all the benefits I just mentioned. Why not? If we force people to trade with each other, their real values are being ignored. Forced exchange can actually make people worse off. Imagine how you'd feel if there was a new rule that said you had to buy the professor's paintings. Oh, gross. My paintings are wonderful and that rule would benefit me greatly. Yeah, but would Fiona be any better off? No. When we force people to buy goods or services they don't want or prevent them from getting the things they do, we're actually making people poorer. It also means we're not really cooperating with each other or building trust. When people try to plan or control the market, they're doing so at the expense of individual differences. And that's really important. I can see that. I certainly don't want anybody telling me what I'm supposed to like. And there's even more to it than that. You see, we all have different skills, talents, and interests as individuals. We're all good at some things and not so good at doing other stuff. Like how I'm great at math, but really bad at cooking. And remembering to water the plants. Exactly. Or like how the professor is good at economics, but terrible at painting. Hey, I don't really have much control of my arms here. But all this diversity means that we're all way better off if we specialize in the things we're good at and trade with people who are good at the stuff we can't do. That's right. With specialization and cooperation through trade, we can create greater wealth than we can ever hope to achieve working in isolation. And the bigger the group we can trade with, the greater the wealth we can generate for all. So you're saying I should get a job doing math, but hire a chef? As someone who has eaten your cooking. Please. Aw. Don't feel bad. Kevin can't do math. I've graded his papers. What's really important to understand here is that the division of labor makes us all wealthier and better off. It also allows us to create significantly more complex and complicated things. Like something as simple as a peanut butter and jelly sandwich, it would take tons of people with different skills to grind the flour, grow the fruit, pick the peanuts, and actually make all the stuff that goes into it. Yes. When people specialize in doing the tests they're best at and trade with other people for all the things they can't produce as efficiently, our society ends up with increasingly better and more valuable goods and services made at increasingly lower costs. We all win. Plus remember more trade means more trust and cooperation with other people and That means more friends. It sure does. Yeah. A lot of people think that markets are just about competition. And while competition is always a reality in a world where resources are scarce and wants are unlimited, free markets are actually enormous networks of people cooperating with each other voluntarily. How so? In an important sense, competition and cooperation are two sides of the same coin. In the market, people compete over ways to serve others in order to be more successful as entrepreneurs, business owners, employees. The way to personal success in the market comes from finding out a create value for other people. So competition in a free market economy is actually competition over who can serve other people the best. That's right. But you know what's even more amazing? Endless salad and breadsticks? Well, yeah. But no, all of this happens without anyone actually planning, designing, or controlling the actions of people participating in the market. And yet order spontaneously emerges all the same. Markets form, goods and services are produced, people get stuff they want and need in exchange for the things or money they value less.  It's a logical order that emerges from individual human action and isn't due to any kind of deliberate planning or design. That is pretty incredible. It makes sense though. The same thing happens with language, right? Yeah, there's no one person or even a group of people in charge of all human language. And yet over thousands of years, individual people working in their own interests created a complex and highly structured communication system that works pretty good. Pretty well. Oh, yeah. Pretty well. I guess you can also see emergent order in all other aspects of culture, like art, music, gating customs, even the internet. Definitely. And for each of these examples, there's no mastermind at all. It's a common misconception that for order to exist in human society, there has to be a central planner making decisions for everybody. In fact, history shows that attempts to centrally plan society result in chaos. Yeah, one key insight from the economist F.A. Hayek is that complex social orders are emergent and actually can't be centrally designed or controlled. He referred to the notion that the economy must be centrally planned as the fatal conceit. Fatal? It's just a metaphor. But ideas do have consequences. Understanding the spontaneous order that comes from voluntary exchange is one of the most important insights we can learn from the economic way of thinking. Yeah, adopting the right ideas can help us create a society that works well for everyone. You know, knowing that all these amazing things can happen when individuals are free to create, dream, and make stuff on their own, and cooperate with other people to make a better world. It really is kind of beautiful. Yeah, it is. I'd cry, but I don't have any tear ducts. So what's your dream? What's the one thing you want to be known for? And do you understand the market process better after watching this video? If you have questions or just want to know more, check out courses.feed.org for tons of additional information. And we'll see you next time.


When we looked at the services stratum, we saw two important entities, the service control function and the content delivery function. So understandably, these two are very much related to the how applications and services are provisioned. But the overall management, execution, configuration and essentially all the aspects related to the underlying network are contained in the transport stratum. So the transport stratum, if you recall, had two parts, the transport control and the transport functions and the transport control functions. Now the transport control functions are very very important and it is like the main engine or the centerpiece of the overall engine architecture. So in this module, we should look at how the control architecture and protocols are implemented. Now this module is an opening to couple of modules that we shall soon be seeing, inshallah. So we shall see the architecture first and then we shall look at the network access and control functions in respective detail in some of the modules that follow. So the network access and control function has a central position in the overall engine architecture. It interacts with the applications, the customer, the services stratum, the transport stratum including the radio, the resource and admission control function, the mobility management control function. Let's look at this figure once again. Here we see that the transport stratum has the transport functions and the transport control functions. The transport control functions has the resource and admission control, mobility management and the network and access control functions. Now the network and access control functions, let's zoom in into the network and access control functions. Here we can see that it has a couple of modules. These modules can be seen one by one. We are going to start with the access management functional entity and then we'll go into subsequently more detail. So the network access configuration functional entity is the network access and control configuration functional entity is the first one to begin with. What it is responsible for? Number one, since NGN is based essentially on the IP address, so it is responsible for the allocation of IP address, both in terms of the client IP address and the server IP addresses. The server IP addresses could be related to the DNS server and the other proxing servers which are used in for instance signaling for voice over IP like SIP servers. In IP multimedia subsystem. So DHCP, the dynamic host configuration protocol is a natural mechanism to allocate the IP addresses. Now these IP addresses could be assigned in a permanent manner. It's also known as the home address or it could be a temporary IP address known as the care of IP address. Now these addresses are related to something called the mobile IP, which when the right time comes, we are going to look at it. Mobile IP is responsible for provisioning mobility and maintaining the end to end socket intact. So the responsibility of the network access configuration functional entity is to make sure that the IP addresses are allocated and they are properly utilized to ensure that the connection is not compromised. So the binding or the association between these two addresses is also the responsibility which is implemented by the network access configuration functional entity.


Okay, so I have a lot of friends, including myself, I guess, who are either studying different languages or linguistics or something like that. And because no one's memory is perfect and no one is a super genius, sometimes you got to look some words up. That happens. And whenever someone asks me what to use just as a dictionary, I recommend Stardict. So Stardict, I mean a lot of people will go online to sites like Wiktionary or word reference or whatever depending on what language we're talking about. And that takes time and it requires an internet connection, which I don't always have. I might be out doing something. So I like to have a program on my computer that does everything and actually more. And that's what Stardict is. It is a nice little tool you can download. It works on Mac and Linux and Windows. You can get it at Stardict.org. It's in all the Linux repositories. You can sudo apt-get it or pacman-sit or whatever. And it's just a really nice program. It doesn't have dictionaries in it itself, but what you do is you download different dictionary files and it sort of keeps them all in one position so you can access them all from the same interface and it's really nice. So you can go here and install it or whatever. Once you pull it up, it'll look something like this. And it's pretty intuitive. You just put whatever word you're looking at. Let's say puzzle. And it'll look up all the different dictionaries that you've downloaded. For example, I have a French to English dictionary and English to Hindi and English to German. And it looks up puzzle in all of these dictionaries and it effectively gives you the result of that. So now, of course, when you first download it, you don't have any dictionaries in it. So what you've got to do is go download them yourself. And pretty much anyone can make these. But if you go to the Arch Linux Wiki for SDCV, SDCV is just the command line version of this program. There are actually some lists of different dictionaries you can get. So for example, this one, yeah, so here are all the different language to language dictionaries, some of which I have. You can just download the tar ball, extract it, and then let's see in Linux, yeah, at least in Arch Linux, you put it in user share, star-dict-dict. In Windows, I think you put it in program files, star-dict-slash-dict, I'm pretty sure. I'm not sure about Mac though. But anyway, once you extract the files and put them there, you should be able to access them here so you can put in a word, hostile, and it'll work. Now that's one part of the program. I don't actually use this interface that much. Now you might notice up here you have this little icon that says star-dict. I think this little thing actually popped up when I was over here. So what this little thing does is whenever you highlight any word in your GUI browser, let's say access, it just looks up that word in whatever dictionary you have. So it looks up in every single dictionary, checks that word. So this is really nice. If you're reading like some language you know 80% of and there's some words you don't know, it's a really nice tool just to instantly look anything up. I actually don't use this much either. What I use is the actual command line utility, SDCV. So SDCV works pretty much as you would expect. You just type in a word, let's say word. And it looks it up in every dictionary. Now you might say, well, let's say I want to know the word for word in just Spanish or something like that. But it gave me all this text from all these different languages. That's fine. What you can do is there are different arguments you can give it. Let me list out my dictionaries. And you can use the U tag to choose a specific dictionary that you want. Let's say Spanish to English. And let's say we run across a mysterious Spanish word that looks like that. Oh, it means dog. So of course if you, let me move this to another screen. And to make this easier so you don't have to type everything out, you can of course make bash aliases. I don't have any of these on this machine but on my other one I do. So let's say for example I want a command that says look up a Spanish word. I'll just say span word. That's going to be the name of the command. And that's going to be SDCV. I'm just going to copy what's over here. Look up in a word in SDCV using the dictionary Spanish to English. And you got to use the slashes for your quotation signs or whatever. So now whenever I say span word it'll automatically look that word up. Bam. I don't have to do any of that typing. And you can have different bash aliases for all of this, you know, you can, for whatever languages you use most often. So that makes this a really effective tool. And I will add, so this, you know, these are all great programs but I will add they're also useful potentially for programming. I haven't necessarily done this but since it does, since SDCV is just feeding you text, let's say like, I don't know, look up a word, since it's just feeding you text, you can use it in concert with, you know, grapple or, you know, whatever utility you have to use the dictionary program to like manipulate text. So that's an option for any of those, those of you who are doing more corpus-y things in linguistics. But anyway, this Stardict is just a really great tool. I recommend it to anyone. It's free and open source and everything and it's great. So yeah, that's about it. All right.


I just thought I'd share with you one of my favorite products for this up-and-coming Q4. So it's this carrot patch toy here as we can see currently being advertised on Facebook. If we just take a scroll down and take a look into the comments section and the engagements, we can see it's got 11,000 engagements, 4,000 comments and over 2,000 shares. Not only that though, if we take a look at the actual comments section itself from some of their customers there's no better way of verifying a winning product than actually seeing comments on Facebook from happy customers. We can see Laurie bought this product for a dog, Angela bought this product for a dog, Alisa bought this product for a dog, Eileen did, Brandon did, so on and so forth. If we actually take a look at some of the newest comments as well, we can actually see people commenting on this video add 15 minutes ago, two hours ago, four hours, eight hours, 20 hours, 22 hours. So we can see that it is current. So as you watch this video today, this is an active winning product that's being sold successfully.