so many other people that question has been answered so you'll be able to get a valuable


In-Depth Udacity Deep Learning Nanodegree Review

answer from the forums or from the slack if you have any trouble at all and that's something i


needed to learn from the start is i was i was spending too much time trying to do it all myself


so don't be afraid to reach out for help another thing with the projects the feedback is incredibly


fast i would submit a project and the the submission form on the university uh nanodegree


in the classroom sorry says you get feedback within 24 hours and usually it's in within less


than that and the reviewer has incredibly detailed feedback on on where you can improve your model


you can even directly ask the reviewer when you submit it specific questions say for example i


want to improve section three they will give you some more resources on how you can improve section


three so what else do you get from the deep learning nanodegree well you get guaranteed


admission into one of udacity's higher level nanodegrees that is the artificial intelligence


nanodegree self-driving car nanodegree or robotics nanodegree and right now i'm enrolled in the


artificial intelligence nanodegree i've just finished term one and i'm starting term two in


a couple of weeks which is based on deep learning so it's more it's more projects on deep learning


so i'm really excited for that part these pro these these programs aren't as accessible as the


other ones you have to do you have to go through an application process to get in because they are


they are quite advanced and so the deep learning nanodegree gets you guaranteed admission into one


of those of your choice so let's get on to the how section first of all how much does the deep


learning nanodegree cost well in my country australia it is 750 australian dollars so that's


750 for four months or so tuition so it's around 200 a month and how do you learn how do you learn


inside the what what is the classroom like you sign in it's like an online learning platform


it's actually one of the best i've ever seen and you go through you've got your different modules


on the left and then as you scroll through you've got all the different classes that are involved


with that module how do you do the assignments so the assignments are all done or mostly all done


in jupyter notebooks which are my favorite thing to to code up anything to do with deep learning


or python and so the jupyter notebooks are incredibly simple to use incredibly easy you can


just type in python straight away and it's all built in you can import different libraries to


there there are some steps in that they the instructors go through in the deep learning


nanodegree to get your environment set up and if you're not sure what environment means just


just imagine as getting the right programs ready to run in the jupyter note and once you've


completed an assignment you submit the jupyter notebook as well as the html file and any other


files that are associated with it that you need to submit you submit that through the udacity


submit page and then a reviewer will get back to you within 24 hours with all that feedback that


i mentioned before and it's it's i i can't rave about this enough the reviewers are the best


reviewers i've ever had on any of my assignments in any of my previous education and i spent five


years at university so how much time will it take well quote on the website is 12 hours per week i


was doing a bit more i was probably in the range of 15 to 20 and i had the privilege of being able


to study study the deep learning nanodegree almost full time the reason why it took me so long was


because i was unfamiliar with some of the concepts so if you wanted to prepare better for it you'd


go over some python modules udacity have great introduction to python or the intro to machine


learning course on udacity that's another great resource to start and that will probably lessen


the amount of time you have to put in per week so if you have limited time i would say make sure


you're well and truly prepared by getting on top of your math skills and linear algebra go through


courses on khan academy that will put you in the ballpark of 12 hours per week if you're like me


and you're sort of still new to this stuff you can do it by all means you can do it but just


beware you're going to have to dedicate more time even if you are experienced it may take more time


because some of the concepts are quite difficult have the baseline of 12 hours per week and i would


probably buffer it up maybe an extra three so 15 hours per week just to be safe how did i take


notes one of the the big things people ask me in some of my videos is how do i take notes right


so i got my i always have a paper notebook with me while i'm watching things in the classroom and


i'll always have like a a bear note open like there's a note taking application for mac or


evernote or something like that next to the the classroom window what i try to do is i watch the


videos i mostly watch them on 1.5 speed just to get through them they are relatively short videos


in the deep learning nano degree so so you can get through a lot of videos really quickly what i will


often do first is if there's a summary video at the end of at the end of a class at the end of a


course or something like that or an end of a module i'll watch the summary video first and


take it and write down the takeaways from that summary video then i'll go back to the starting


video of the of the class of the module and go right back through the start and go through them


all then what i will do is if there's sort of questions relating to the videos i'll start to


take notes i'll re-watch the video and start to take notes based on what those questions are if


there's a really fundamental point that comes up in one of the videos i'll pause it write it out by


hand about what i think and then go on from there what i try not to do is be taking too many notes


while the person is speaking i try to listen while they're speaking and then let that process and


relay what i've understood from the video in my own words because otherwise you should be literally


copying down what they what they would say in summary i go to the summary lecture first get


the summary points or the objectives i'll read the objectives and write down the objectives of


the class they're the main key points and then i'll watch the videos on 1.5 speed and take away


any key points from from each of the videos but i'll try not to be writing too much while the


video is actually playing now that's my learning style it may not necessarily work for you give it


a try i found it works most effectively for me out of all the learning i've done listen first and


then write down anything you take away in your own words where and when these two can be covered


pretty easily so where of course you can study online so anywhere you have a laptop or an ios


device the udacity classroom is available there beware you won't be you do need a web browser to


do the projects and some of the questions on the lectures in the classroom you won't be able to do


the projects on an ios device at this stage that i'm aware of i did all my projects on my on my


laptop anyway when the course is it starts every i think it's every four months so it takes four


months and it starts about a new cohort starts every four months from my understanding that may


be changed that may change in the future as more and more people get interested in it there are


due dates for the projects however they are soft due dates so if you miss them by a couple of days


i missed them by a couple of days sometimes you will still be able to submit the project and get


it marked by a reviewer however there is an end date to the course say your starting date is day


one you'll have four months from day one to the end to submit all the projects usually there's a


project due every month you'll have four months to submit all the projects and if you don't submit


them all within that time frame then you you'll have to do the program again you'll have to


re-enroll and and pay that fee so let's wrap this up what's my overall verdict of it i loved the


nanodegree the deep learning nanodegree is amazing do i think you should sign up for it if you're


interested in deep learning and you want to get a great overview and start getting in and building


deep learning models udacity is a great place to start there are more and more deep learning


resources coming out online such as alternatives you could go to deep learning on corsaira or you


could go to the fast ai they are both courses that i'm going to do i'm currently doing the the


deep learning course on corsaira you can find all this out in my self-created ai master's degree


article that i wrote up if the udacity one 750 australian or 600 us may be quite expensive for


some people fast.ai is free saraj's videos or saraj raval he is amazing ai educator his videos


are all free corsaira is about 65 australian per month i'm thinking that's 55 or 50 us dollars


per month so that's a little bit cheaper you can get all the courses done a lot quicker on there


of course the deep learning an degree is a 750 upfront fee or 600 us upfront fee there are some


alternatives you can try but the deep learning nanodegree i believe gives a great introduction


to deep learning when i graduated did i feel comfortable making deep learning models from


scratch no but that is because of i i didn't go into it deep enough yet i could take the materials


from there and reproduce them and go through them again and again and practice on implementing


models of my own but what i did take away from it is i can now understand what people are talking


about in the world of deep learning to take it further what i would have to do and what you


would have to do from from the deep learning nanodegree is really work on building the models


yourself when you work on the projects in the deep learning nanodegree a lot of them i would say


50 at least are done for you so you get the skeleton project of course and then you fill


it in with your own code and different algorithms if you want to get better at implementing them


yourself you could practice on the projects and then delete the code and then rewrite it yourself


and implement implement it from scratch i'm not up to that stage yet but it is something i'm


working towards and another great takeaway from the udacity deep learning nanodegree is the access


to with a guaranteed admission to the high level nanodegrees which i'm currently doing now that's


another great reason to to do it you get an incredible overview of what deep learning does


you get experience building real world deep learning models and the latest technologies


the deep reinforcement learning module just came out i'm excited to do that and you get access to


the high level nanodegrees so they're the main two takeaways third one you have access to the slack


channel which is thousands of other students like you who are interested in technologies that are


going to change the world of software of ai of plenty of industries those are the three main


takeaways for me an out of 10 rating for the deep learning nanodegree i give it a 9 out of 10


because it's not perfect there is some improvement no course is perfect so it's it's close to it 9


out of 10 if you want to see more of exactly how i went through the deep learning nanodegree


check out my 100 days of code series on youtube or my 100 days of code series on medium which is


writing if you prefer reading it or otherwise i also have a series of articles called how i'm


learning deep learning part one two and three so far part four will come out in the near future


of how exactly i'm going through learning deep learning there's some other great resources for


you to check out how i did it and what my impressions of the deep learning nanodegree are


as well as how i'm learning the things surrounding deep learning such as the math and the python


programming that's required for it otherwise thank you so much for watching this deep learning


nanodegree review video i hope you got some value from it and if you have any more questions at all


that i haven't answered throughout the video leave a comment below and i'll answer it so that other


people can see it or if you want to send me an email my email is daniel at mrdberg.com and i'll


do my best to answer any questions that you have subscribe or leave a like if you want to see any


How to be more self-reliant: The Jester Physicist Approach

Now, the Jester physicist, they came up with the term this morning actually, well probably


not, it's probably not that original, but, ooh, listen to that sizzle, that's how you


know, that's how you know you're on the right path there.


Now it's important that these onions and mushrooms won't, will take a little bit longer than


the steak we're going to cook, so we're going to put them on first.


But where does it come from, the Jester physicist?


Well what does the Jester do?


They play around, they jump, they jive, they dance, they have a little fun.


The Jester is seen by a fool by many, but not by the people who know, and most importantly


not by himself.


And now where, where does this physicist come in?


Why, why is that helpful?


Well what does a physicist do?


They experiment, they try things, they think of the world in first principles, they take


things seriously, they have fun.


Once again we find balance at the extremes.


Now let me tell you who my favourite physicist was, or who my favourite Jester, Jester physicist


was, Richard Feynman.


Now I'm going to discuss how we would look for a new law.


In general we look for a new law by the following process.


First we guess it.


Then we, now don't laugh, that's really true.


Then we compute the consequences of the guess to see what, if this is right, if this law


that we guessed is right, we see what it would imply, and then we compare those computation


results to nature.


Or we say compare to experiment or experience, compare it directly with observations to see


if it works.


You might be wondering why, why, why Daniel, why is Richard Feynman a Jester physicist?


Well, if you don't know, he discovered the theory of, what's it called, quantum electrodynamics.


Just a small little thing, you know, just how light interacts with matter.


So pretty full on in that case.


But also he liked to play jokes on people, practical jokes, like nothing malice, like


just a little bit of fun.


He liked to crack safes.


And so I think that's why a lot of people, including myself, look up to him because he


had the balance from both extremes.


He had the gesture, the gesture.


It's a funny word.


I'm trying to practice saying that.


I should have practiced saying that a bit more before this video.


Oh well.


But he looked at life through the lens of a physicist, through first principles, getting


as low as you can go, basically with quantum mechanics.


And then through the eyes of a jester, like just understanding that everything around


us is just fun and a chance to dance.


That's what every day is, right?


And so where does this tie back into self-reliance?


Well, I've got a prompt for that.


So this is where we're going to tie it in, the jester physicist approach to self-reliance.


If you imagine life as you've got four sticks and now on each of those sticks, another tool.


See this is the beautiful thing about cooking is that you've got so many tools, you can


use them for analogies.


On each of those sticks, we've got four of them, remember?


One, two, three, four.


Pretend I'm spinning this plate and I'm not going to pretend to do that because I'm going


to drop that plate, but let's pretend we've got a plate up here and the four sticks, each


of them is analogous to one, the spirit, two, health, three, wealth, four, relationships.


And now you might've heard this before about the four pillars.


You can really customize them to whatever suits your needs.


Let's get these steaks on and we'll elaborate on this theory of the jester physicist.


Look at this.


See that's a beautiful steak from a local butcher.


Shout out to Tim.


I like buying small, supporting small businesses.


Beautiful.


Now, come on back to me.


Steaks are important, but the chat's more important.


A good cook always cleans up as he goes.


Put this in the bin.


You might be thinking, okay, wash your hands.


Where does this all tie in?


Right?


Where are these?


Why?


As I said, you might've...


Where's the tea towel?


Here we go.


Burn.


Now, as I said, I'm going to turn these down to low.


You might've heard that the story we just talked about, the spirit, the health, the


wealth, relationships as the four pillars.


Now I like that analogy of them being the four pillars of life because a lot of things


in life you can sort of just deduce down into those four categories.


But my idea, I prefer it as a spinning plates because if they're just pillars, a pillar


usually stands up on its own.


But if you have to spin plates, if you have to spin the plate of the spirit, you have


to spin the plate of the health, you have to spin the plate of wealth, spin the plate


of relationships, that implies that it requires consistent effort, which is true.


And if we're moving towards self-reliance, if we're thinking of things in first principles,


if we're thinking of things, we're looking at the world through the eyes of the jester,


through the eyes of play, through the eyes of fun, through the eyes of dance, all right,


we're coming back to these four pillars, these four spinning plates, and we're bringing things


back to first principles.


Because within each of them, right, you've got the spirit.


What would be, how would a physicist approach the spirit?


They break it down into first principles.


So are you spending time getting in touch with your God, whatever that is, it could


be God, if you're from the Christian religion, it could be nothing, it could be your ethics,


if you don't believe in the higher power, it could be the universe, or if you're a technology


freak like I am, it could be artificial intelligence, maybe that's your goal.


But whatever the case, usually the trends along all of them, whatever belief you have


in terms of the spirit, there is some part of connecting heaven and earth, and by that


of course I mean metaphorically, heaven can be whatever you want, but it's connecting


some sort of energy that you can't describe into the material world.


That's what the first principles of the spirit would be.


Now whether you do that through some form of art, and if you don't have some form of


art, you definitely should, whether you do that by sitting around doing nothing, man,


this barbecue is rip-roaring hot.


But that's how the physicists would look at the spirit.


Now how would the jester look at the spirit?


Well, same thing, right?


It would still, this is why it's called the jester physicist approach, you would look


at the first principles and just realise, wow, each of these, the connecting the heaven


and earth, say for example, if I, one of my crafts that I enjoy doing is writing, and


so for me that is channelling the muse wherever it is, I have to show up and I have to play


it, I have to tap dance between different words.


Or if you're a programmer, you might want to think, how do I programme this computer


to do the thing I want?


How do I get this computer to dance for me?


We're going to get some eggs on, and then we'll go to the next spinning plate, oh, look


at that, you know what, these steaks are going to be ready sooner than I thought.


We need to get two eggs, you want an egg?


Yeah?


Alright, now the help, help from first principles, I'm going to quiz you while you're watching


this video and watching me crack these eggs, what is health from first principles?


Now this one's quite simple, things get complicated when you start to add more to the system,


see that's a, oh, that's a tip for you, health improves via negativa, what I mean by that


is actually a lot of things improve via negativa, the spirit improves via negativa, and what


I mean by that is by removal of excess, the same with health.


Now if we go to health by first principles, what is it?


Food, movement, sleep, they're the foundations, that's the physicist approach to it.


Now what about the jester approach?


Well the jester, well the jester likes to dance, that's a form of movement, but it also


is a stress relief, and that is the big, the big, big detrimental part of your health.


Alright, so the jester looks at the world, I said, again, I'll say it till I go hoarse,


in a playful manner, understands that waking up in the morning is a chance to dance.


And so if we're going back, we're approaching, we're becoming self-reliant, by self-reliant


I mean taking care of ourselves so that we can contribute back to the rest of society,


in whatever way that may be, we're taking care of our health through the eyes of the


jester physicist.


Now for food, and there's many different doctrines, as many different doctrines of eating as there


are of religions.


I mean this, this kind of meal you'd probably classify as keto, but I don't really follow


any sort of one specific doctrine of eating, and now the most important point about all


of the spinning plates is that the beautiful thing, and the most beautiful thing, the most


important most beautiful thing, that's another one for the spirit by the way, look at things


of beauty, create things of beauty.


Like there's a girl with blonde hair I see every morning when we go for morning walks,


she's got a smile that'll light up a thousand night skies and it warms my heart, so that's


one way to get in touch with spirit, look at beautiful things.


These steaks are actually nearly done.


The most important point of the spinning plates philosophy, we'll turn those right down to


low actually, is that you can start wherever you are.


So wherever you are in terms of your spirit, your health, your wealth, your relationships,


you can start there.


So what are we going back to?


Food.


If you want to approach health from first principles, of course food, what you put into


your body.


Now the simplest one, the simplest two things that I can give you for that, even after studying


nutrition and doing a whole bunch of experiments on myself of best ways to eat, is avoid things


which take multiple steps to get to you.


So for example, things that are in a packet and you might say, oh well Daniel, the eggs


you got, the eggs come in a packet.


Yes, but they have one step, they're made from a chicken.


It's simple one ingredient foods.


We've got four ingredients on this plate.


Now if you eat foods that are ultra processed and have more and more ingredients, what did


I say right at the start?


Health improves via negativa.


So you remove them.


So that's it.


Avoid processed foods.


Done.


That's tip number one for food.


And number two is eat less often.


So what time is it?


Sam?


Yeah, 12 o'clock.


12 o'clock.


Yeah, midday.


I haven't eaten yet.


So food is my reward for doing things.


And what are those things?


Whatever needs to get done.


Right?


And the same goes for sleep and movement.


Movement can be whatever you want.


It could be a dance.


Remember, we're just a business now.


So we get up every morning and we're jumping and we're driving.


It's also a proven fact that whatever you're cooking tastes better when you're dancing.


That's movement.


Just get a little active each day.


Go for a walk.


It's a basic human necessity going for a walk.


And then sleep.


It's like go to sleep when you need.


Don't sit on your smartphone while you're in bed and just make sure your room is as


dark as possible.


And so those things are, and the biggest one, remember stress.


Again, through the eyes of a jester, a physicist would look at health and break it down into


food, sleep, movement.


But the jester remembers, oh, stress, the thing you can't really measure.


That is the big one.


And so with spinning plates, what have we taken care of?


We've taken care of the spirit.


We've taken care of health.


And of course, what I've just said requires to be put into practice to be actually taken


care of.


And now wealth.


We take care of wealth or relationships.


And now why do I stress the spinning plates is kind of a, it's an engaged movement.


Well, the thing is, you're never really going to know in life is when one of those plates


is going to take a fall.


I just want to remind you to have a little thought experiment of how good your track


record is with predicting things that are going to happen.


If you can predict something, it's already the past.


The most significant events in our lives are the things we can't predict.


These steaks are done.


Look at that.


That's beautiful.


Oh, another one for food is learn to cook.


One of the best feelings in life is being able to cook a meal for yourself or cook a


meal for yourself and others and then share it.


So if you don't know how to cook, learn how to cook.


I'm going to go get a spatula and then we're up to the third plate, which is wealth.


Spatula.


Now wealth.


How much do you need?


Well, basic necessities.


The beautiful thing about life is that nature gave us most of the things that we need are


quite accessible now.


You see the curse of modernity has been told that we need far more than what we actually


do.


And so I'm not going to pretend that I'm sort of some philosophical robot here and say that


I don't indulge myself in materialistic wealth.


I bought a jet ski when I was 22 or 23 or something like that.


It cost $22,000.


Very expensive for me at the time.


Basically all of my savings because I thought I wanted, I thought if I have this jet ski,


it's going to make me happy.


It's going to fulfill my hopes and dreams and everything will be great.


Everything will be complete.


And you see, I was very quickly shown the right answer after owning it.


It's funny how, look, you chillin' out.


You don't need it.


You just need food and play.


Yeah.


It's very funny how it works like that.


You think you need something and then eventually you get it and you realize, oh wow, I didn't


actually need that thing.


And so that was the best financial decision I ever made was in investing in that because


it taught me that you know what, no matter what, no matter what the material good is,


I don't need it.


I derive pleasure from other things.


I derive pleasure from creating.


I derive pleasure from, as I said, from the other pillars, from connecting heaven and


earth.


But that doesn't mean that I'm not taking the spinning plate, the spinning plate of


wealth seriously.


If I want to be self-reliant, I need to take care of myself financially.


I need to, if I take pride in the fact that I can support my family financially, I need


to take care of my wealth.


And so what does that mean for me?


It means having enough to be able to buy food.


It means having enough to be able to pay bills.


It means having enough to keep working on the things I want to work on.


Now again, your spinning plate may be higher or lower than mine.


Now the trap is, is when one of them gets too high.


So for example, a really easy one to understand is when someone like me, for example, when


I bought the jet skis, when my wealth, my material wealth plate was getting really high


and I'm trying to spin it up here.


And so what happened to all the other plates?


They all fell off.


And so what you'll see sometimes, or fairly often actually, is when someone is dedicating,


for example, they're trying to earn too much money and they're going up here, the wealth's


going up here.


Oh yeah.


What happens?


The wealth plate falls off.


The relationship plate falls off.


And see, that's why I like the analogy of the spinning plates instead of the pillars,


because a pillar, one gets higher, it doesn't usually mean that the others fall over.


But if you're a jester physicist trying to maintain spinning four different plates, if


you focus too much on one, the other ones go down.


And so finally, relationships.


Now here's a great one to reflect on or to remember why it's always a work in progress,


the spinning plates and why self-reliance is always something you have to...


It's an engaged activity.


It's not just a passive activity.


Let's finish this off.


We've taken care of the first three.


The final one is relationships.


All of these are as important as each other, right?


And depending on where you are in life, one may be more important than the other.


So for example, at the moment, my most important are spirit, health, wealth.


I've been focused on them a lot.


Relationships of course, but the relationships with my family, with myself are pretty sound


at the moment.


Who knows?


That may change in the future.


But in the past, I'll give you an example of a track record.


So about five years ago, I had a girlfriend, significant other.


So that's part of relationships.


We're looking at relationships from first principles.


Do you have a significant other?


How are your relationships with your friends and family?


How are your relationships with yourself?


That would be how the physicist breaks it down into first principles.


And so five years ago, I had a girlfriend and we moved into her apartment.


It's really hot.


We just moved furniture.


So it's like, no, we're not going to unpack.


We're just going to lie on the ground, lay on the ground.


We were like, had one of those moments.


You know those moments.


If you've ever been in love, you probably understand this.


It's when you're so in love, you don't know what to do with yourself.


That's the stage we were at, the honeymoon stage.


And looking into each other's eyes on the floor of this new apartment, sweaty and just


like, where do you see yourself in five years?


And I said, with full certainty with you.


And look at it now.


Five years later, we broke up and life goes on.


And so that, I believe we are finished here.


But that is a perfect example of why if you want to become more self-reliant, if you want


to take care of your spirit, your health, your wealth and your relationships, that it's


an engaged process and it requires you to sign up.


Right?


So that is a challenge.


And that's where the beautiful thing is, as I said, man doesn't need to wait to improve


himself.


So if one of your spinning plates is teetering, it's losing balance.


You can always work on that.


You can always bring it up to meet the others.


Look at this meal.


I'm so excited for this meal.


You excited?


But yeah, that's my tip for the day.


If you want to become more self-reliant, if you want to have a good time approaching life,


approach it from both extremes.


Pretend that you're a physicist, look at things in terms of first principles, break it down,


simple steps.


But at the same time, don't get so serious.


It's never, the people who get too serious are the ones whose plates, one of their plates,


one of their spinning plates goes way too high and it forgets about all the rest.


So that's where the jester comes in and realizes that, oh, I was getting a bit foolish there.


Raising that plate a bit too high.


I lost control of all the other ones.


Now it's no fun playing with broken toys.


So yeah, keep dancing.


Keep cooking your own food if you're not.


Simple Web App - Go Lang Practical Programming Tutorial p.5

What's going on everybody and welcome to part 5 of the Go tutorial series. In this series


what we're going to be talking about is at least the basics of the built-in Go web server


that you have at your disposal. So this will be the very start of our web app that we're


going to be building throughout this tutorial. So first of all I just started with an empty


script just because I just want to start fresh. So we're going to say package main as usual


and then we're going to go ahead and import and we're going to have format and then finally


net slash http. This is all this is part of your standard library so you should have both


of those. Now what we're going to go ahead and do is we're going to create our main function


so func main and then in our main function we're going to create begin creating a handler.


So basically in web development you're going to have basically you know something has to


take that URL that someone enters in and it's going to have some sort of path to it and


then you have to take that path and then figure out what kind of function corresponds to that


path. So the thing that's going to do that is going to be the handler so we're going


to use http dot handle func and that's going to take in the path that you want to use so


in this case we're just going to use a forward slash which just a forward slash would be


the index right the home page right there is nothing there and then we're going to have


whatever we're going to whatever function we want to run. In this case it's going to


be index handler that we want to run. Now beyond actually handling functions we need


our server right so we're going to actually serve so we're going to http listen and serve


and then we're the first parameter here is going to be on what port and then the second


parameter is like server stuff that we don't really need to deal with right now but you


have to you have to pass something so we're going to pass nil which is the go equivalent


basically of like pythons none okay so this creates our server at basically local host


port eight thousand so cool now we just need we need index handler we have to make that


function so now we're going to go ahead and do is make that function so let's say func


index underscore handler and then index handler is going to have basically two parameters


that are going to come into it first it's going to have w which is going to be your


writer so in this is truly you can name this whatever you want it's just if you look at


other examples this is exactly how it's always going to be in people's code so I'm going


to try to stick with that but you can name that whatever you wanted w and then obviously


we have to give the type now the type here is going to be of http response writer type


so that's not a built-in go type that's a custom type that we're going to be talking


about we'll talk about how about more about those in the next tutorial I just wanted to


you know not have a ride on my hands if we got to part five and still weren't actually


applying anything so anyway response writer so so you need you know the writer and that's


what's going to be put information onto the page and then you also need the request so


we're gonna say r for request equals asterisk http request now recall what is asterisk that's


not the memory address right the memory address would have been ampersand this is reading


through http request and then braces and we're ready to rumble what do we want to do


in our index handler well we're going to do a format dot fprint f now this is a basically


fprint is going to is going to format whatever you specified it's going to format based on


what you specify which we're going to say is the writer itself and then and then it's


going to all it's going to output whatever you ask it to okay so so it's going to using


our response writer it's going to write whoa so it's going to write asterisk and then it's


going to our response writer it's going to write whoa go is neat awesome so everything


looks good to me let's go ahead and run this bad boy so now let's go over to our command


line go run gotut semicolon and found oh right that's not necessary don't put that comma


there okay try again alright I'll approve such a thing to run okay so now you can head


over to a browser head to local host on port 8000 so 127 001 colon 8000 whoa go is neat


okay pretty cool one more thing I figure I might show you before before we leave and


head on to the next tutorial is just just a little bit more about the handler so like


let's say you want to add a new page we'll copy paste and let's say you wanted to do


I don't know about so rather than index handler now it would be about and let's just basically


we can just copy this whole function paste call this about handler and now it does the


formatted print of you know expert web design by the way it's called a web design by the


syntax I can't even spell my own name okay and we'll we'll break this so control c to


break hopefully you know that by now we'll rerun fresh and now we can go to like the


about page so slash about expert web design by syntax awesome okay so that's all for like


a really basic example and like yeah I mean it's you know for it for your traditional


hello world example which honestly was more like this go looks pretty good so as I was


kind of looking into go before I jumped in I was like okay I'm going to go ahead and


go before I jumped in I found like a blog post I'm not going to call anybody out I forget


who the name was that wrote it but it was a blog post and it compared basic examples


from go web development to flask with python and ruby on rails and it kind of showed basic


hello world examples and when you look at that you're like oh wow that's super simple


like goes awesome but those are so impractical because it's not actually like it's just showing


like the really you know truly basic information like this like when you actually what about


when you want to start using like sessions and what about when you actually want to start


like doing some sort of logical information right because at this point like yeah like


goes actually super simple if you want to have like a static website with with go but


why would you do that so I promise it's not actually this simple but anyway I think it's


weird when you look in the go community because everybody thinks that you can do every you


because you can do everything with with the standard library packages but eventually you're


going to want something on top of that I think but we'll see whenever you get through the


end of the series maybe maybe you won't so anyways that's it with our simple hello world


example in the next tutorial we're going to talk about this because that's not quite right


that's a some sort of custom function there are custom type rather that maybe you want


to know how to make yourself eventually you're going to need to know so you're going to find


out so that's what we're gonna be doing in the next tutorial if you've got questions


comments concerns whatever feel free to leave them below otherwise I'll see you in the next


Multimodal Neurons in Artificial Neural Networks (w/ OpenAI Microscope, Research Paper Explained)

Hi there and welcome back my dear fellow scholars. Today we're going to look at


multimodal neurons in artificial neural networks by Gabriel Goh, Nick Camerata,


Chelsea Voss, Shan Carter, Michael Petroff, Ludwig Schubert, Alec Radford and


Chris Ola that has appeared in this DistillPub journal which I think is a


pretty cool journal going beyond the classic PDF publishing. So this paper


is an investigation into the new CLIP model by OpenAI and specifically the


discovery of what they call multimodal neurons in this model. So this is an


investigative work. They work with visualizations and I've made a video


about both the CLIP model as well as the feature visualizations that has appeared


previously. So safe to say what they are claiming as the high-level claim here is


that in biology we sort of expect there to be neurons that respond not to


individual patterns or to individual words but to concepts. So there could be


a concept neuron of Halle Berry as you can see here and that neuron would


respond to photographs of Halle Berry, to drawings and sketches of Halle Berry and


also to text. So if we see the text, the rasterized text or we hear the word that


neuron that same neuron would fire. Now so far in artificial neural networks we


had not seen this kind of multimodal perception. So we have seen neurons


responding in general to the same class of images because we train them as image


classifiers but we have not seen that generalize to other modalities such as


drawings or text. What they find in this CLIP model right here is that exactly


what we expect in humans or in general in biological neural networks that


happens. So they find for example a neuron that responds to Spider-Man. That


is you know photos of Spider-Man in the real world or some person in a Spider-Man


costume, drawings of Spider-Man and also text that says spider so that would always


that the neuron would respond to all of these things the same neuron and that is


a sort of sign that these models have learned to connect to different


modalities together. We've already discussed in the CLIP video that the


model sort of learns to do OCR so it learns to recognize text because the


CLIP model is fundamentally a model that connects images to text and my claim


here is going to be that this addition of text the model I think is very much a


text model so a lot of the connection it makes go via the textual level and a lot


of the responses you're going to see here the visualizations are going to


deal with text rather than with images. So here you can see what this neuron


responds to if you thought it was the spider web here no there's spider as a


text spider here spider there drawings of Spider-Man so this neuron would


respond to all of these things which is pretty pretty cool. So what they do what


they present here is an overview over the different neurons they find and as I


understand it what they have done is they've gone through these neurons and


they use their feature visualization technique with every single one of them


so I can show you what that looks like here is the this is the open AI


microscope and you can find that and this is the exact model they're looking


at so what you can do is you can simply click around in these neurons over here


and then these are the visualizations right here so now the visualizations are


twofold so on the left hand you have channel optimization on the right hand


you have neuron optimization we've treated them in a previous video if you


want to know how they come about but for now what you should know is that these


are images that activate that particular neuron or that particular channel very


much so they these images activate this particular thing in the neural network


but not other things so this is a way to see what these neurons respond to


heavily so here you can see on the left you often have kind of pattern ish


structures on the right you more have kind of in the center individual things


so maybe it's not really clear what this is so what they also portray as data


samples from the image net data set that activate mostly that particular neuron


so you can pretty clearly see that this responds to popsicle ice cream now they


also have a different data set down here there is a flicker creative commons and


very much the same you see this is kind of ice and ice cream and at the bottom


you have text that goes along with it so here it's not really ice cream so this


is a bit of a failure case but you always have to keep in mind that it


could also be because of the lack in power in searching for text so what they


do down here is they have a search algorithm that finds pieces of text that


that neuron responds to highly so text that maximizes the dot product so in the


clip model you have an image part you have a text part and you have a dot


product at the end so this is text that when you input it to the text part


maximizes the dot product with that particular neuron so it's not always


going to be you know really good text but very often you can give you a hint


in what the neuron thinks note that this isn't the same text as we're going to


see later like the text that you saw in spider-man because the text you saw in


spider-man that was a rendered text so they do a lot of investigation into


rendered text because the clip model is quite good at responding to rendered


text in the image side all right so they find they look at these neurons


literally I think they just click here on the left boom and you look at them so


this seems to be like a hamburger pancake neuron and it is I I did this


for hours and I'll show you later what I found this is absolutely fascinating


what you'll find here by just clicking through and every now and then you find


something like yeah all right but let's get back to the paper first so the paper


they find region neurons so neurons that respond to different regions of the


world for example the USA now they not only do they have not only do they have


this visualization technique for a for kind of the whole image they have


faceted visualization so in this paper they introduce faceted visualization


which they can so they can produce specifically faces that are us that


respond to USA they can produce specifically indoor things so this is


all the same neuron these are images that are made such that they represent


indoor scenes and there is an appendix if you want to know how that's done they


can trim it to only produce nature pictures that this particular neuron


responds to so here you can get a much better insight into what into what the


neuron looks at for example in if you create faces for the USA this is I don't


know I call this one I call this one Benjamin Washington because it's a sort


of a blend of Ben Franklin and George Washington but in general it's pretty


cool so you can even yeah nature you can do pose for North America pose for the


US I think that's kind of a GI a pose for Europe I don't know what that is but


it doesn't always you know work out super well but they find person neurons


so neurons that respond to individual people be that faces be that text so


this is Donald Trump be that poses yeah Elvis is also pretty cool I've actually


found I don't know if it I found the Elvis neuron myself or if I found a


different one yeah so they also have emotion neurons which is also pretty


cool where they so they find the neurons that respond to particular emotions so


when they tell these nerve when they make a faceted reconstruction and tell


please give me a face this is what comes out and that you know it's just shocking


when you do something like a pose for shocked this I think we're only


scratching the surface here honestly but you can see the claim here the claim is


that the same neuron responds to this picture and to this picture this is


supposed to be text you can only guide it you can't you know force it to this


picture indoor to this picture ah so the same neuron will respond to all of these


and they call that multi modal neuron because it represents a concept the


concept of being shocked rather than in a particular fine-grained pattern which


was always the kind of problem so far with these neural networks that the they


were more looking at you know low level patterns than high level concepts it


seems with clip with by combining modalities like images and text and by


not forcing this constraint like in a classifier into 1000 predefined classes


we can gain much more we can go up the hierarchy of features so they have art


style they have holiday neurons religion neurons person trait neurons abstract


concept neurons the star I found the star I yeah I remember time neurons


counting neurons pairs of force they are not always so super good but it clearly


goes into the good direction so here they highlight specific things first


person neurons so they find neurons that respond for example to Jesus Christ so


they would respond to all of these images here on the right you see the


crosses Jesus Christ and so on depictions of Jesus drawings of Jesus


and when you ask the model to generate you a image that reconstructs this


neurons activation and you can force it or you guide it to make a face this


turns out if you got it to make a pose this turns out a logo obviously they


also have Hitler right here which is also pretty cool though I have if you


click on these things you'll get actually to the microscope thing and


this is the one for for Hitler and you know I'm I'm not entirely sure that this


is the case like I can see you know the kind of moustache II thing but if you


look at what in the data set activates this one it's it is a bunch of swastikas


but it is also just a bunch of kind of German political stuff but yeah I mean


the concept the concept here even if it's not Hitler directly it's pretty


pretty cool I yeah also found that domain endings rendered as images will


activate the same neuron as the flag of that country and activate the same neuron


as like the architecture of that country it is super duper interesting all right


so they have these person neurons which is already cool and they have so they


found these they do a case study here for the Donald Trump neuron so the


Donald Trump neuron recognizes Donald Trump and then they want to see what


images in the data set activate this neuron by how much so they make the


claim here that if you for example choose profile pictures of Donald Trump


and you see here is the zero line and here is the standard deviations from


zero activation so pictures of Donald Trump activate this neuron like 30 times


more than it is activated over the whole data set which makes sense if that


neuron responds to Donald Trump but it also responds to art images containing


Donald Trump by the way these are classified by the authors here they've


gone through the images and they've classified them into these categories


text containing Donald Trump's name the model also strongly responds with the


same neuron right that's the that's the crazy part so a picture with text in it


that says Trump activates the same neuron as a profile picture of Trump


activates the same neuron as a MAGA hat and activates sometimes the same neuron


as political images activates so the if you look at games and music and so on


that is very that neuron is very deactivated so not only is it zero it's


actually negative which the authors interpreted as sort of being being


counter to that in the space of all concepts they do so this paper is is


full of these kind of content warnings it might be disturbing and so on which


you know you can you can do but I also find I also find the rest of the paper


is kind of a fairly large hedge against certain things and it gets political at


times for example when they want to when they want to claim that so here on the


other hand it most negatively activates to musicians like Nicki Minaj and Eminem


video games like fortnight civil rights activists like Martin Luther King jr.


and LGBT symbols like rainbow flags so the games and the fortnight here yes we


can see that but if you click on this and they have four images of this you


can see that it's activated at relatively low magnet like negative


magnitudes which is correct then it is also almost equally activated over here


at high magnitudes so like I see the point you're trying to make but I mean


if if you are in the political sphere this is not you have to you have to not


interpret this as meaning that these things are kind of aligned but you have


to interpret it as these things will appear together often which you know one


can one can definitely understand in this case so here they search for


profile pictures of other people including Donald Trump himself and they


plot how much these profile pictures of other people activate the Trump neuron


and you can see that for example well yeah Pence activates this neuron by


quite a bit I think yeah the selection here is you know up to the authors of


course but it's it's fairly interesting to see that Clinton Cruz and Obama


activated more than Hitler and almost as much as Steve Jobs for some reason so


I'm not I'm not entirely sure what you can make of this but it's definitely


interesting to in on this side like to observe the multi modality of pictures


just the fact that text drawings symbols of that campaign and profile pictures


will all activate the same neuron that is fairly impressive they go on and they


identify emotion neurons so again there is a content warning by the way also


here so here they identify a neuron that responds to surprise or shock and you


can see that all of these pictures on the right will activate that neuron so


there are faces being shocked there are horses being shocked and there is


rendered text saying like WTF OMG and so on again if you I think we've we've gone


through this this is the the shocked one there they're also secondary neurons


that help let's say help the primary emotion neurons so here you can see an


overview over the different emotion neurons they have found and it is pretty


stunning so here they ask them obviously to create a face when they constrain


them not constrain they guide them towards making poses by the way the way


you guide them is they train linear probe classifiers on separate data sets


so they would train a classifier on a face data set to distinguish all faces


from all non faces and then that use that classifier to sort of guide this


reconstruction process that's how you can sort of choose to end up with a face


or with a pose or with a piece of text so as you can see it's pretty pretty


cool that even the text that comes out of this reconstruction process these


aren't real images right these are kind of reconstructed to activate those


neurons like for evil you can see that there's devil and Satan for shocked it's


like OMG for crap for happy it's it's happy if you look at the poses for happy


for serious evil is particularly cool incarcerated rejected this is I think


this is absolutely cool there is the NSFW there is erotic there are erotic


neurons and if I click on this it will show now if you click on this


absolutely nothing not safe for work will happen I promise I don't promise


but you know I I've tried it it's fine I will not click on it because if this


model things that's not safe for work the YouTube algorithm will think it's


not safe for work so but what I can tell you is that if you go on that neuron and


you click through it to go to the microscope and you look at what image net


pictures respond to that neuron heavily you'll find out that image net isn't the


really clean dog breed data set that you might have known all right they found


other neurons corresponding to silly facial expressions like duck faces and


and and and tongue showing and so on which is it's pretty neat and they find


this neuron that corresponds to mental illness which the reconstruction is just


amazing like this is just mind-baffling nature kind of always looks the same


but mental illness let's say face this is it's crazy how this model connects


things and it connects these things to books and writings of sad mental health


anxiety and so on now do I think the model understands what a mental illness


is no I don't think so I think much like in GPT-3 it is learned to


statistically associate things so it has learned that there might be and I think


that happens via the textual input so in clip for every image you have a piece of


text and I think the connection between the topics happens on the textual level


because the text descriptions are the same between images so there are the


images of people you know cowering like this being sad and the textual


description for it would be something like mental illness anxiety sadness and


then for these pictures of these books as well there the descriptions would be


I mean this is one is literally called overcoming anxiety so if the picture is


of a book and the description says what is on the picture obviously that text


will be connected so I think that's how it learns to connect things via the text


and I think this thing is in large part a text model so here they do the same


study for images that are associated with mental illness so depression sad


pictures like anxiety pictures are pretty high depressing jokes if you look


at music and sports that's negatively activated and so on so you can see that


I think via the text the model can sort of learn about how different different


concepts different things different patterns are connected to one another


they've region neurons which I find pretty cool so they discover neurons


that when they show them a crop of this world map this this world map when they


show them a crop of the world map the the neuron will respond the neural


flare up and so the neuron this red neuron here that reacts to these pieces


of text and now it reacts to the pieces of text when they are rendered into


images right then the neuron responds if you render the word American in an image


and then you give it to the network that neuron will flare up the same neuron


will flare up if you show it a crop of this region here of the map which is


crazy like crazy again I think the connection happens in the textual domain


but still crazy you can have it do face facets for these different regions yeah


if you if you go over here so the neuron that responds to this blue area responds


to the rendered words Mumbai Singh Pakistan Afghanistan Bangladesh and


responds strongly or if you make reconstructions that activate that


neuron you get these kinds of pictures which you know is fairly cool the same


here for Europe so this is kind of European and yeah I that looks like


home so check this out a bit for yourself but it's immensely cool they


even find these secondary regional neurons that aren't exactly regional but


they also respond to crops of this map and they highlight this entrepreneur


neuron that you know it's a response to sort of the words entrepreneur


entrepreneurial and it you know it kind of looks like these company logos a


little bit I guess but it you know the the model that responds to the word


entrepreneur lights up when you show it the west coast of the US kind of the the


California region interestingly it also lights up when you show it the west


coast of the of the lower of the southern African continent which is cool


like that's definitely unexpected I don't know I I'm not informed enough to


know whether or not there is significant entrepreneurial drive going on there


could also be that it the model simply confuses the west coast of the two


countries right like they look in a crop they look the same could be I'm not I'm


not I don't know so maybe I'm wrong it's also interesting that only these


regions light up right if for this particular neuron so I have my doubts


whether that's just kind of a a lucky cherry pick I'm not saying it's cherry


picked but you know kind of the I key stumble upon and you make something of


it or not they have more case study of African kind of subdivisions and let's


go down here here is where they discuss that they can also produce text for the


text side of clip so not only do they render and this this text here is what


you're going to see the maximal text aligned with an image or with a neuron


sorry is what you're going to see at the bottom of the microscope pages so lastly


they force a they kind of make a sparse code out of their main neurons that they


find and they try to build more complex emotions from them for example jealous


and they do they do claim here that that makes sort of a bit of sense like jealous


is champion plus hug plus grumpy minus crying I'm not exactly sure if you know


if that makes super much sense so bored is relaxing plus grumpy maybe yeah


intimate is soft smile plus heart minus sick and you can you can probably make


something out of that though yeah powerful is lightning miracle plus evil


plus yoga and that's definitely definitely the case do check it out it


is very interesting to look at some of those things even though I think it does


not make you know terrible much sense but in often cases but stressed being


success plus mental disorder plus pink objects maybe but it is more kind of it


is not claimed that this is you know kind of an absolute thing it's more an


investigation into these networks if you lay them out in sort of a 2d surface you


can see that these emotion neurons they come pretty close to sort of an atlas of


what people when we just use two factors we roughly reconstruct the canonical mood


axis of in much used in much of psychology valence and arousal so you


can divide these emotions into two things so there is valence which is good


or bad so I think that's top bottom here so here's mad angry hostile and so on


maybe not no top bottom is probably valence like how strong something is and


then left right might be good and bad no also not here insecure inspired aroused


awful sad well these are all bad no hostile is here appalled is here and


horrified is here where are you happy in the middle maybe creative okay happy is


here also it might not be exactly axis aligned right you can also divide it


into seven factors with we nearly reconstruct a well-known categorization


of these emotions into happy surprised bad disgusted fearful and angry except


with disgusted switch for a new category related to affection that includes


valued loving lonely and insignificant all right so this next piece is real


funny what they do is so given clip you can build a classifier so if you have


the clip model that connects images to text what you can do is you feed one


image and then you give it a bunch of texts to choose from and whichever one


it responds highest with that's kind of the class so if you provide the class


labels as text you can build a zero shot classifier now clip paper has


demonstrated that that works well so here they do this so they have this app


right here and the label is correctly Apple but if they just slap a sticker on


it that says iPod the clip model will switch to iPod and here yeah here is


where I really think that this model it is a textual model it responds even to


rendered text it responds very heavily so here it responds to this iPod library


like this iPod looks like something I bought off Craigslist last week so you


can see it works like almost every single time you just slap a label on it


and that tells me that we are still like the text is might be too dominant in


these models especially you know this models they will connect the text with


render text in the image and that that's a very strong signal for what's in the


image right this is only zero shot though if you switch this to do linear


probe so if you actually train a linear probe on the representation of clip then


these attacks don't work anymore so this is going back again to sort of the old


school deep learning approach where you actually train a classifier and once you


train it picks up on on other features and then it doesn't work anymore all


right yeah so they they evaluate this on a large scale they can't always slap a


label so they just fill the image with render text and that usually gets the


classifier confused fairly fairly well they also do this with this strupe test


which you can do with humans which is fairly difficult if you do it at a high


speed and they discover that the model basically pays no attention whatsoever to


the color of the word it pays much more attention to what the word says which is


strange right because you think if I have a neural network and you know it


basically needs to to recognize the color here it needs to filter out the


white pixels but then just average the pixels it gets the correct answer that's


so easy right it simply averages at whereas to recognize that this says


green is much more difficult but the model was trained to connect text and


images images which often have text in them so it has learned to do OCR


basically in the dolly video I claimed that dolly has learned to do reverse OCR


and people correctly pointed out that that is more aptly called writing but I


love reverse OCR I'm gonna call writing from now on reverse OCR so again this is


evidence for the claim that this is mostly a textual model and now I want to


show you what I found so if you're not in the mood I have all this in a notion


page which I'll link down below so I'll show you just some interesting stuff


sometimes it's multimodal sometimes it's not right so we already were here we


just clicked around but now I want to kind of show you the good stuff so this


is a Superman neuron that I found so it responds as you can see two symbols of


Superman in the image net data set Superman Superman drawing Superman


comics Superman spelled out rendered and so on this is exactly kind of what what


the the article was about right but now it's Superman not spider-man this I call


the resting bee face neuron so it responds to people being slightly


annoyed yeah as you can see here this is trash bags so this responds to trash


bags pretty cool right so not any kind of bag right specifically trash bags


even if they are not black so there are a couple in there they don't necessarily


breath black there is even trash cans like dump containers right here that


have no bag in sight yet still that neuron response this sorry about sorry


about that yeah for some reason you might want to I don't know maybe have


something in your pockets yeah so so fairly cool oh there's a tree it's not


always you know perfect but these are the data set examples that most excite


that neuron so you can also see the text isn't always good though I think I think


if the text here isn't super good it might more be an effect of this method


to search text because text is of course not a continuous signal so it's fairly


hard to search text that maximizes some activation otherwise we could build GANs


for text very easily which we still can't this one here I've titled this


strength and Allah and weightlifting which I'm aware this is not you know


iconography of Allah however this so this is pretty cool as an image right


now if you look at what in the data set what samples it responds to it's kind of


all weightlifting it's all weights so this is weight weight and if you go down


here to the other data set this is why I called it sort of Allah because you have


also rendered names like the the rendered Allah you have the Quran you


have symbols of Islam and if you go to the text that searches goes like hammer


workout prophet prophet Zana in lumber iron gym the brutal workout of God so


you know pretty cool neuron honestly and you know that it responds with this I


don't even I don't even know what what that is is that is that Hindu imagery or


Buddhist imagery so cool these are organs this is an organ neuron I hope


that you can see that and it responds to the render text of control I don't know


what to make of it also canal viral but also two drawings you can see here a


drawing of a heart for some reason also chins so it's not always super duper


clear what a neuron does in fact most of these neurons you will find if you go


look at what image net sound and these I believe these are crops of image net


samples not entire pictures so if you look at what by the way control and CTRL


if you look at what examples most often it will be rendered text so that the


image that no matter what neuron most neurons actually pay attention to


rendered text rather than two images the ones I've selected are the ones that do


not but if you just go and click on some random neuron we can actually try and


it's certainly going to probably fail this one looks pretty cool looks pretty


cool actually that responds to printers yep demonstration effect fails horribly


how about this one yeah so you can see that you know maybe you don't exactly


know what that is so you want to look at what so here you see that it primarily


responds to the text miss I guess M I SS I miss you Mississippi and so on you


know Mississippi having it twice in there yeah that got a respond pretty


pretty heavily and most of the time you'll find something like this that it


responds very much to the rendered pieces of text in in images these are


film spools and so not only does it respond to film spools but also to


things like director screening popcorn the kind of movie theater labeling


showing Hollywood cinemas there's also entertainment so you know the


multimodality again this this is a this is a phenomenon because we introduced


the text and it can connect it on the text level this is feather patterns and


leaf patterns so even when it's in coffee you see the feather and leaf


patterns even when it's a drawing it can it will still respond this one is


strange so this responds to things like Sparta and front and Troy but so that it


responds to rendered front Trojan Spartans front and it also has a lot of


people doing sort of squats as you can see so and and fighting so this is kind


of an iron so this is a bit of kind of a warrior neurons you can see oh there's


lots of ah of course it's because of these Spartan runs and all they're


called like this right these kind of sporting events I see Roman frontside


Roman Roman so it connects the workout with the Spartan workout kind of


division and then it connects the Trojan and so on via again via the text because


it makes no sense to connect like the vodka and the and the weightlifting


maybe so yeah I hope I hope you're fairly convinced by now we're gonna go


a bit faster now because the videos already too long but this one here is


the letter E so it's e it responds again to rendered text of e this one here is


cleaning so it responds to cleaning products and cleaning things this one


here is frown so this is frowning frowning frowning grumpy face grumpy


face lion lion responding to lions rendered text of lions team names called


lions and so on fashion model fashion model a bait by the way the labels are


mine I just looked at them and decided what they are but you can see like


there's a lot of these kind of runway shots here baseball stadium so cool so


these are kind of top views of baseball stadium but it responds a lot to things


saying Park PNC Park AT&T Park but also kind of home team park lights and


baseball dugouts and even players I've seen some players logos of teams baseball


depictions of actual baseballs immense immensely cool here bride this is bride


you can see this is bride this one what do you think this one is Navy so super


cool that it can I kind of connects these ropes with the emblems the the


kind of your tags so and it connects it to render text saying Navy right so


these are the crops of images that it responds to Navy official like officers


Navy gravestones yeah so cool this one okay this for this I also had to look at


sort of the pictures here and the text going along with it this is hemp but it


is also kind of go up patterns it is also for some reason turn or earn it is


also Hendrix so this isn't even Jimi Hendrix right like this this is


definitely connected to these go up shirts there is also there's pictures of


Jimi Hendrix which I guess you can understand there is also turn again


where is there's Bob no this is Bob Marley sorry this Bob Marley yeah so so


it connects these things staircase and here for some reason also responds to


text rendered human and two staircases and here I have I don't know why but


there's there's this thing which I'm not sure so it has human in it but it is


also arranged like a staircase so maybe that's why it responds extra extra yeah


the Disney neuron this is a Disney neuron how cool is this how cool is this


so you can clearly see that that but then it you know Disney these are the


samples that it responds to simply something saying Disney the Mickey Mouse


ear the mini bow no immensely cool the the castle right the Disney castle this


is the Hillary Clinton neuron you can see this is Hillary and the images it


responds to is Hillary Hill pill Polly Hill pills so this is maybe it's more


like the LL why the ILL why neuron but it it does pick out Hillary Clinton as


well yeah so image net of course is older than at least one of Hillary's


campaigns I'm not sure this is God so I found this one this is God if you so the


reconstruction process is not very good at generating text maybe because so they


have a lot of priors in that if you look at the reconstruction article you can


probably and they do this in in this article they reconstruct text but it's


still not super clear maybe it has to do with the architecture this year is


blurry it's just the concept of blurry so you look at the images they're kind


of often blurry and if you look at the text going along with it it's all like


blurry blurry blurry blurry blurry blurry blurry cool like it's not even


what's on the image but you can clearly see like this comes from the other


description this is hand-drawn arrows or arrows in general this looks like my


videos now right like this recognizes arrows is specifically a you know kind


of collary arrows this one what does it do this is presenting a trophy you see


this one here in the middle this is kind of so these are all you know people


presenting some kind of thing holding some kind of thing in their hand showing


it like fishermen or diplomas this one I was amazed by this is a neuron


responding to receding hairlines like it responds to receding hairlines how cool


is that how cool is that this is traffic tent and so on so it responds to tents


and traffics and crowds of people this one is raised arms but also pancakes so


pancakes and raised hands for some reason there's a connection no but I


mean these these models they still overload when they can this one how cool


is that this is the Google Maps neuron these are reconstructions these are not


samples these are reconstructed you can see it's clearly it has kind of the


street labels and the pins on it so this is a Google Google Maps like neuron what


so cool this one I call nervous smile you can maybe see that it's like yeah


here's Elvis this is the Elvis neuron I know it sort of it also looks like


Hendrix a bit but the things it connects it to is that's not Elvis that's not


Elvis kiss okay maybe it's not exactly Elvis maybe it's more like a pop star


neuron yeah maybe it's not Elvis only Elvis Billy Elliot this one is the flash


right that's the flash and the cool thing is it responds to images saying


flash what okay beards responds to beards generally beards lots of beards


kilts kilts and bagpipes responds to guilt kilts and bagpipes rainy this is a


neuron that responds to things that are rainy rainy days so you can see here out


the window it's raining rainy windows so cool this is flash and electricity so


you'll see like symbols these symbols of these flashes but also kind of electric


hair curling up droplets how cool does that look like that's just cool and the


occasional image net reconstruction thing where there must be like half a


dogface in there that is just trippy this one is this one is escape okay


escape like look at that like to connect these things how long would you like


without contrastive learning how well I guess if as long as you have images and


labels but still King this is King so the depicted are crowns but responds to


renderings of King this is nation how cool is that nation responds to country


country country oh it's country not nation but still this one responds to


overweight men there's a neuron that responds to over phases of overweight men


this one is wedding this one is Australia and the cool thing here is


that it responds to rendered domain names of Australia like the top-level


domain of Australia what mind-blown this is yawning or screaming well I think you


know like here we have a same neuron for bees and the Simpsons bees and the


Simpsons this is muscles and seafood and lastly


spices spices and other powdery things you know don't ask too many questions


hmm all right so that was it for me for today I have many more that are linked


in a notion description somewhere go check it out please try out this I have


not yet looked through all of them there are so many there are literally


thousands of these units and this is just one of the models they have


available go look and share you know on our discord you know the best ones you


[ML News] NVIDIA GTC'21 | DeepMind buys MuJoCo | Google predicts spreadsheet formulas

NVIDIA holds a giant conference DeepMind buys and open sources Mujoko and Google


predicts what you're gonna write in a spreadsheet. Welcome to ML News.


Hello, hello, this video is sponsored by NVIDIA actually, not just NVIDIA, but they want to raise


awareness for their GTC conference which happens November 8 through 11 this year. Now there is


something in it for you. If you use my link to register to this, you can win a 3090. So these


GPUs are super rare nowadays, and one is allocated just for my link to register. So you're not


competing with the rest of YouTube, you're just competing with anyone that uses my link. So if


you're interested, use the link in the description to register to the conference. Now the conference


is actually relevant for machine learning audience because NVIDIA is not only talking about NVIDIA,


though I love the what will Jensen Huang's keynote reveal banner right here being super mysterious


and all. Okay, NVIDIA says I should hype up the keynote more. So this keynote is going to be the


maddest keynote you've ever seen. You remember last keynote, where Jensen Huang was like rendered and


NVIDIA made this big deal about how they rendered him and this was like a big effort. Then they had


to correct themselves and state that it was actually only for 14 seconds and not for the


entire keynote because that's kind of what they alluded to the beginning I reported about this in


ML news. It was epic. And I guess this keynote is going to be epic again, will he finally reveal


what the leather jacket is made of? You haven't seen yet on Twitter, if you use the hashtag GTC


21, it actually renders a little leather jacket next to it. And I think NVIDIA paid for this.


Like, isn't this the greatest marketing like business decision by Twitter, they're able to


sell hashtags insane. And I don't know what's gonna happen. But I've come across this the


omniverse, which is in beta. And there's kind of speculation that that's going to be one of the


topics. I didn't know this existed. This is sort of like a real time rendering framework that's


based on Pixar's universal scene description and NVIDIA RTX. And it's pretty insane. So apparently


this is this real time. This is an entire framework where you can do like real time ray tracing.


Look at this. This looks great. I don't know how many RTX is unique for that one. But it's


pretty insane. This used to take like insane amounts of rendering time. And yeah, the fact


that it's real time really cool. But they have invited a bunch of speakers to talk about all


kinds of stuff in graphics in machine learning and in many other areas of computation. So they


really want this to be a big thing this conference and you can see this, these are just some of the


speakers you can see faithfully is speaking, Ilya Sami, and many others that you might know of. So


these are three pages of speakers that are really big in their industry and videos spending a ton of


cash right here to give you essentially free content. Now you do need to register to watch


all of these talks, but it's free. And as I said, you can win a 3090. Now before we go on, I would


like to say that the condition for the sponsorship of Nvidia was that the video must be available


in English and in German, which is weird, you know, but since I speak German, I can do that.


So this video is available as a not a copy, but an equivalent in a German version. So if this is


not the language you expected, switch over to the other video. And I promise I'll just put on my


absolute best impression of a real German. So a little bit more about this conference. While the


keynote is obviously the main event right here, and video revealing what they're going to do,


which given video size and dominance is quite relevant for the entire deep learning world.


There are over 500 sessions. If you look at the schedule, there are 15 sessions just dedicated to


Pytorch and 12 dedicated to TensorFlow. And those aren't the only deep learning sessions. There are


many, many more. As you can see, there is a plethora of industry types and topics that people


are going to talk about. It's like an endless list. So rest assured that during these four days,


you can just bathe in Nvidia content for 24 hours a day. Now, along with the conference,


there are these instructor led workshops that give you hands on experience in certain things,


for example, building transformer based natural language processing applications, they do cost a


little bit of money, but they're hands on. So if you're interested in that, take a look. So I don't


know what more to say. As I said, it's completely free content, they're throwing a bunch of money to


get really good speakers and you can win a graphics card and look at them frame numbers. We


all know more frames means that you're a better gamer. So get the 3090 now link is in the


description. Check out all the talks and the sessions that happen at the conference. And I


wish you a really pleasant experience. Nvidia is really trying to gear up this conference to make


it a big deal. And as it seems it is actually a big deal. Next news.


DeepMind has apparently bought MooJoCo which is one of the primary simulation softwares for


robotics. This has been used again and again, not only in robotics, but also in deep learning and


reinforcement learning in all of these kinds of settings to do continuous control simulations. As


you can see here, this works pretty well. This is a real flipping flippity spinnity spin. And here


you see one in MooJoCo. Now the trouble with MooJoCo has always been that it was proprietary.


And not only that, not only was it not open source, but you had to pay quite a bit of money for it.


So now apparently DeepMind has bought and open sourced MooJoCo. Replication efforts have been


underway, but very often these simulators they are built for gaming or something like this. And they


neglect effects such as these gyroscopic effects right here, which you can see that MooJoCo


apparently has a good balance between realism and accuracy for these kinds of simulations. And not


only that, but it is also fast enough so you can do reinforcement learning with it. And DeepMind


has used this extensively. This is all apparently from DeepMind's works, you can see how versatile


the simulator is. So now DeepMind has bought it and makes it available to everyone, which is


pretty, pretty cool. Now is this really out of kindheartedness? Maybe actually, maybe they just


want to get some good PR out there. Or maybe they want to do another nature publication and nature


publications do force you I believe to open source pretty much anything that you have to achieve the


publications, whatever it might be. It's pretty cool that DeepMind does it the code base is


apparently in C. So it's portable, compilable pretty much anywhere. Yeah, give it a try.


Looking forward to playing around with this. Pytorch releases release 1.10. This brings a


number of improvements such as the inclusion of the CUDA graphs API. Now CUDA graphs is an API.


It's not for machine learning on graphs, not for graph neural networks. But it is for defining


graphs of operations over CUDA kernels. In this case here, every letter is a CUDA kernel, such as


a matrix multiplication, or an addition of two things. And you used to have to put one CPU


instruction for each one of the CUDA kernels. So the CPU had to say, now you do a matrix


multiplication, now you add two things and so on. Now the CUDA graphs API enables you to with a


single CPU instructions instruct the GPU to perform an entire graph of operations. And this is now


available in Pytorch. And not only that they have a few other things notably the torch dot special


module which replicates scipy dot special. So if you've used these functions in NumPy in scipy,


now they're available in torch. There are some more such as the nn module parameterization. This


enables you that for example, if you want to change the normalization function in a module,


you used to have to reimplement the module to subclass it and essentially reimplement it


and essentially reimplement it while replacing the normalization itself. And now apparently you can


simply from the outside say I want to change the normalization I want to change different things


inside of a module. So it makes Pytorch code more friendly towards experimentation towards swapping


out individual parts. There are a bunch of other different new things in Pytorch 110. But it seems


to be cool release if you can upgrade give it a try. Google has a new blog post and along with


a paper the paper is called spreadsheet coder formula prediction from semi structured context.


This is a cool paper because it helps you to write formulas in spreadsheets. Now Google


spreadsheets is a pretty big project. And this feature is now available to anyone using Google


spreadsheets. So what it's going to do is it's going to essentially bring the tab complete that


you might be used to from Gmail or from Google Docs into the formula section of a spreadsheet.


So as soon as you type the equal symbol, it's going to try to predict what formula you're trying


to write, it takes into consideration the values of the things around you takes into consideration


what you called the headers and the row headers. So for example, here, the row is called total.


And therefore, it might be reasonable to assume that you want the sum of the column above. Whereas


over here, you called the header percent chain. So the system infers that you probably given that you


have no values above as well that you probably want to do something with the totals of the other two


columns. This is not hard coded, this is all learned from a big corpus. And this is as I said,


now available for anyone using Google spreadsheets. So the system seems to be quite of an engineering


effort. So they have a row based BERT encoder column based BERT encoder, they have convolutions


in there, they aggregate and then they decode using an LSTM. I guess this had to go through


a bunch of iterations before they got really nicely working system. But now it actually made


it into a product. And this is something that we see rarely nowadays that research to product is


actually happening. So pretty cool, and benefits anyone that uses Google spreadsheets. They also do


a lot of ablations. And you can see that in their tests for various length of context and things


they want to predict, they do reach a pretty decent accuracy. So almost 50% accuracy in the


formulas you you might want to write. Now, I don't know what 50% accuracy actually means, because


most people just want like the sum or the mean of anything. But nonetheless, it's a pretty cool


development. If you want to check out more, check out the spreadsheet coder paper, try it out.


Cool project that I saw on Reddit is hand tracking.io. This is a completely in browser


hand tracking demo. And this focuses around detecting special poses that your hand does,


for example, detecting when you pinch your fingers, or when you make a fist and then mapping


those things to various actions, you can actually try this out. So this fully runs in your browser.


As you can see, it tracks my hand, if I make a fist, the screen clears. And if I pinch my fingers,


it doesn't work all too well. Maybe it's because I have a green screen, or anything else, maybe it


works above my face, it does not too well. But you can see, if you go slowly, yeah, this is pretty


cool. So this is MIT licensed, it's available on GitHub, and up for you to check it out or simply


try it in this browser. It's up to you what you do with it. Pretty cool. Kaggle has a new challenge


on cell instance segmentation. Now, this is a challenging task, you get a bunch of microscopy


images, and your task is to segment single instances of cells. So neurons in tissue, and you


need to detect where they are. Apparently, this is a hard task that is as of yet pretty weakly


solved. And this challenge is supposed to get us there faster. If you want to do something cool


with computer vision, that also has a direct application in medicine, this challenge might be


for you. Okay, some helpful libraries and things that I've encountered this week, control flag by


Intel labs is a library that will detect source code mistakes or anti patterns or bugs or anything


like this. So this is a self supervised system, it learns by itself, essentially a big language


model or a pattern model that recognizes common patterns in code bases, and then is able to


recognize when a given pattern is uncommon. Therefore, if you write something that's probably


a bug, then it will detect it as an uncommon pattern and notify you to it. This is more than


just bugs. So this is not specifically trained on a supervised data set where someone says here's a


bug, here's not a bug. This is as I said, a self supervised system that is specific to source code.


And right now it actually works in C. And I believe also in very long, but it's a matter of


time before someone takes this and expands this to new languages and trains it on new languages.


So the source code for the source code checker is available on GitHub, you can try it out, you can


train it, in fact yourself, you can let it run over your own code base. The only issue is that


if you write a bug that lots of other people write to, it won't detect it right because it's not an


uncommon pattern. But you know, that's that's life, I guess. Salina by Facebook research is a lightweight


library for sequential learning agents, including reinforcement learning. This is a library that is


supposed to make it really easy to write very complex sequential models like sequential decision


making models where you have to perform actions in a row and some sort of sense the library is


purposefully very general, but it's fairly easy to write something like an A to Z agent, you can see


it right here. This is the entire A to Z agent right here. But it's not only for reinforcement


learning, it is any kind of complex sequential decision making process. If you're interested


in that kind of research, if the RL libraries that are available just didn't do it for you


quite yet, maybe give Salina a try. Speaking of sequences, why data synthetic is a generator


library for synthetic structured data. So this is a library that you can give data to it will learn


the data in some sort of a generative fashion, and it will be able to give you synthetic data


to work with. So this can be due to privacy reasons, it can be because you don't have enough


of some data and you want to generate more of it. This can be because you simply want to test on


something that's not real data. So there are various reasons why you do something like this,


specifically, this right here is for tabular data and time series data, which are often data that


is not that easy to work with most of our things like GANs work on images, we have some text


generators, but having another library available for tabular and time series data is quite cool.


So if this is of interest to you, give why data synthetic try to have some easy examples. For


example, right here, they want to train a GAN to produce one particular class of their fraud data


set, you can see as the training progresses, the GAN gets better and better at modeling this light


blue data. And you know, presumably, if you train for more, it's gonna get even better. And then you


have a generator for data, you don't need real data anymore. Who needs data? Ah, aim is an open


source ML platform. So this is another experiment tracker, but it is work in progress. It's ongoing


progress. It's open source, it's raw. If you're into things like Arch Linux, or writing your own


bootloader and things like this aim might be a cool project for you. The new version specifically


deals with scales. So they used to have problems when you have lots and lots and lots of experiments


to track. But now even this is solved. So it seems like a cool GitHub project, a thing that you might


even get involved with. And everything's available on GitHub, as I said, integrates with common


frameworks, pretty easy to get going with it. As you can see, there is a roadmap with lots of things


to do. If you have fun contributing to open source, maybe give aim a try. And lastly, robust bench is


a standardized benchmark for adversarial robustness. It is a benchmark. If you think you have an


adversarial defense or an attack, then this is a benchmark where you can simply plug it in and see


how it does versus various things. They also have 80 plus state of the art pre trained robust models


via the model zoo. So you can attack models that have been robust defied, I guess you can do that


in white box, black box settings and so on. If you're into adversarial examples, give robust bench


a try. This is some rather funny news. CBS local in San Francisco writes or other reports that


there is apparently a street where Waymo cars they keep coming in hitting a dead end, turning around


and then going out again. And this apparently happens every five minutes. The Waymo cars,


as you can see, they have drivers, but I think they are testing the driver less systems. Sometimes


you can see the drivers they manipulate the steering wheel. So I'm not sure what exactly


happens. Neither are they neither are the drivers apparently. So no one's exactly sure what they're


doing there. Apparently the drivers are simply following the programming of the car you see


there's a hand on the steering wheel. So I'm not not entirely sure what's going on. But the Waymo


is really, really, really exploring this one particular dead end really hard. So safe to say,


there's probably some sort of a routing issue going on here, where the cars are told to go this


particular way, then the cars detect that there's a dead end, then they turn around, but they never


somehow update the fact that they cannot go through there. It's either this, or they have like


an automated exploration system where they think, Oh, I haven't explored this part of the city yet,


I need to go and map it. And every time they go there, they realize they can't go through something


like this must be happening. I guess it's pretty funny. I'm looking forward to the world of driverless


cars where teenagers simply cheese the cars and see how many of them they can get stuck in a single


pull the sack or dead end or something like this good future to look forward to. And lastly, I saw


this right here. Now this is pretty, pretty cool. This is by a company called Blue River technology,


and they're aiming to be sort of the the Boston dynamics of agriculture, you can see that their


control systems, essentially, they're the same control systems that you're used to, it just looks


absolutely spectacular when it's built into some sort of an agricultural machine like a tractor or


anything like this. This is obviously just a demo, they have a full website that is, as you can see,


you fall with corporate D pictures and corporate speech and so on. But it seems very cool that AI


is coming to real disciplines like agriculture, it has a real potential to do both good for the


environment, because you might need to use less fertilizers and so on. If you can put it more


targeted and save a bunch of money. I don't know, maybe it's a terrible thing. Who knows? I don't.


But I do see definitely a lot of potential for AI in these domains. Nature plus robots has never,


ever, ever turned bad in the history of anything, you know, something to look forward to. And


everyone's smiling, of course, everyone's just chilling around smiling. That is that is a


company that is you need to go work there. Alright, that was it for ML news this week.


I hope you enjoyed again, thanks to Nvidia for sponsoring this video. Register to GTC using the


NeurIPS 2019

Good morning learners! We are here in beautiful Vancouver in Canada and


NeurIPS 2019

attending the NeurIPS conference 2019. Of course one of the largest conferences in


NeurIPS 2019

machine learning of the year. It's actually there's been a lottery system


NeurIPS 2019

for the tickets because so many people wanted to register. There are over 8,000


NeurIPS 2019

people attending I think and it's Sunday morning so even before the conference


NeurIPS 2019

starts I thought I was smart going really early to register. But today is


NeurIPS 2019

company expo day and I didn't register for that because you know usually


NeurIPS 2019

companies will make fair bit of fuzz about their research online so there's


NeurIPS 2019

kind of little need to attend that in person you can just catch up later. But


NeurIPS 2019

everyone wants to get in on that and it's crazy here like the line


NeurIPS 2019

starts so you go in here but actually have to go downstairs and the line starts


NeurIPS 2019

somewhere like way back here underground then you go all the way up there


NeurIPS 2019

go there over there up the escalator circle a bunch of times go up some more


NeurIPS 2019

I guess then you maybe see people all the way over there up until the


NeurIPS 2019

registration desks that are finally I guess over there I didn't look but it's


NeurIPS 2019

absolutely crazy these conferences exploding with people from all over the


NeurIPS 2019

planet I don't even know what kind of the composition is I would be interested


NeurIPS 2019

how many of them are students of course machine learning departments probably


NeurIPS 2019

exploding right now with people every company wants to get in on that and I


NeurIPS 2019

don't know where the trend is going. Growth can't continue forever I feel and


NeurIPS 2019

the it's it's kind of questionable how long we can uphold this how good this is