I'm involved in or supposed to be involved in this week so I'm not entirely sure what's happening


Building my first Amazon Alexa Skill! | Learning Intelligence 28

next. I did send an email earlier today reaching out to ask and let them know my availability


and I think that's a tip, that's this episode's tip is don't be afraid to follow up. So if you


meet with someone who's especially someone who wants to help you or someone you think you can help,


just because they haven't reached out to you doesn't mean that you can reach you don't have to


or you you aren't allowed to reach back to them. Follow up with someone to show that you care about


the relationship that you're forming. A few typos in that message but that's the tip, follow up.


Now voice user interfaces. So we've been going through this classroom on Udacity AI Nanodegree,


did the welcome, did the overview, applications, Alexa skill, conversations with AI with Alexa,


VUI best practices and what's a VUI? Well a VUI is a voice user interface. What exactly is a voice


user interface? Well have you ever used Siri or Google or Alexa for that matter? It's the


interaction you have with that with that smart speaker. So it's not just replying back in a


robotic voice, it's natural language and that's the epitome of what a voice user interface is.


And so if I was interacting with a speaker it's exactly as if I was talking to you or talking to


someone else. That's the pinnacle of voice user interfaces. I'm up to the lab which is essentially


building an Alexa skill. Now this is the workflow here, we have the Echo device and then we have the


speech recognition, natural language processing, text to speech all through Amazon Alexa and then


it goes into a Lambda function which is essentially a bunch of computing power on the cloud, just think


of it like that and then it goes to the Alexa skill kit and then it goes to the Alexa app or


it goes to the speaker. So this is a two-way arrow here and these are all two-way arrows actually


and you can go back and forth. So I'm really keen to try that out. Oh and one more thing, I've got a message


from my mentor, let me just show you what this looks like, exactly what this looks like. I told


her that I passed the Capstone project, that's awesome congratulations all the best for VUI.


Thank you so much Ayushi, shout out to Ayushi, she's awesome. And I've got something to show you.


It's a Yui Mega Boom 2. Not that one. Oh what's up future Dan? Show them the smaller one. Yeah


you're right, let's show them the real one. Time to play with Alexa. Do you have these things in the


future? Oh yeah, you wait and see. It is, oh I can't even explain it to you. Imagine that thing and it's


just everywhere. Get on the bus, there, get in the hotel, there, go to a friend's house, there's my voice


everywhere. That thing's just the beginning. Now that's cool, let's get it open. Some cool packaging,


I like the blue. What's it say? Designed around your voice, add voice control to your Bluetooth


speakers, far field voice recognition, voice control for your smart home. Cool box. Here we go.


It's so small, it's like the size of, hey hold on, I know what they're doing here. They're just making


a mini version of the Yui Boom. So that's it and I think we have power cable and


power brick. Apparently I just have to plug it in and say Alexa, let's try it. We're about to awaken


the beast. Good timing, my dogs are barking. You should be alive. I should probably turn it on the wall.


Alexa come alive. Thank you, please wait as your operating system is initiated. Alexa, live.


Hold on, am I supposed to do something? Alexa are you alive? Hello, I'm here. I probably need to connect


it to Wi-Fi. Wi-Fi isn't set up on your Echo Dot. For help, go to your Alexa app. Be right back. I've got the Alexa app.


Time to sign in. We're in. Welcome to Alexa. Thank you, thank you, thank you. We have an Echo


Dot English, connect to Wi-Fi. You've connected to Echo Dot. Go ahead and finish the setup in


your Alexa app. We're ready to go. Alexa, tell me a joke. Ever heard of the band 999 megabytes?


Probably not. They've never made it to a gig. Haha, you got me. Alexa, what's the weather like?


In Sandgate, it's 27 degrees Celsius with intermittent clouds. Tonight, you can look for lots of clouds


with showers with a low of 21 degrees. Thanks, Alexa. That's really cool. It's like a version of Siri, but


the fact that it's always on and I don't have to say the word hey in front of it, I mean, it's only


three letters, but the fact that I don't have to say it, it's just it's even more accessible. But


I'm more excited than rather than using it than to build some stuff on it. So that's the project for


the classes I'm doing on the AI nanodegree. So that's what we're going to get into. And as soon


as I'm done, I'll show you what it's like. P.S. If you want to build your own Alexa skill, I'll put a


little guide in the description that Amazon themselves have published. It's on GitHub. Check


it out. There's a link in the description for that. Alrighty, so we've made some progress on the Alexa


skills lab. I've officially got my first skill on the skill store. We've got nutrition facts,


Mr. D. Burke studios, but it's still in beta mode. You can see that little dev us sign on the bottom


left of my skill there. It's still really basic. And now this is the AWS Lambda platform, which is


essentially how your skill runs. So you've got the skills kit here and then you've got nutrition facts,


which is the knowledge. And then you've got the server logs here. But how does this work? Well,


essentially, it's just some JSON code here, which is JavaScript. And I'm creating a little database


of nutrition facts. So there's one for you. A gram of protein has four calories in it,


and you can fill this up. And the idea is to be able to ask Alexa to reiterate some of these facts


directly from this database in natural speech, which is amazing. This is the Alexa simulator


in the Alexa developer portal. Let's try it out. Open nutrition facts. There's one I tried earlier.


Here's your fact. A gram of alcohol has seven calories in it.


Oh, yeah. But then we try it on the real thing. Alexa, open nutrition facts. Sorry, something went


wrong. So I haven't fully got that connection down pat yet. But I've been going through another


tutorial. Aside from the GitHub one I linked before. This one's on Codecademy. So I'll link


it in the description as well. I'm finding it another great way to learn how to make Alexa


skills. And check this one out. Alexa, open Codecademy. Welcome to Codecademy. So that's


just a really entry level skill just to get your feet wet, just to try it out. And I'm having so


much fun playing around with this new interface. I mean, voice is such a baby platform. And you guys


know how much I love nutrition. I mean, that's one of my missions, right? To use AI to help the world


move more and eat better. And so if I can start by making a simple little nutrition fact app on the


Alexa skill store, well, that's a foot in the door, right? That's a step in the right direction. My


next goal is to fix up my skill to make it actually usable. As I put the finishing touches on this


video, I'm reading through all the comments from Learning Intelligence 27 where I told you guys


about the internship. And it's blowing me away. I'm truly, truly inspired by all of you guys.


Siddharth, Yash, Shaik, Ajjan, Empiricist, all of you. I know I'm missing some comments out because


there's so many. I'm just absolutely blown away by your support. Thank you so much. I think it's


time we wrap this video up. Should we use Alexa to help out? Alexa, what's my catchphrase?


I screwed up | Inside a $600 Million tech company - Part 3

Oh hey, didn't see you there.


It is day 2 or, well day 3, the 3rd day I'm actually in Melbourne and I'm going back to


realestate.com but I know yesterday or last night, I'm not sure how it worked out, it


might have been last video or something, I said I was going to meet someone for a book


writing proposal on machine learning and what not, they were from another company that works


on these sort of techniques and what not and they reached out to me and wanted to see if


I was interested but I've got a confession to make, I pulled out of that because I finished


late at the experience I was having at realestate.com and I didn't communicate that well enough


to the team I was supposed to be meeting up with afterwards and now it's completely my


fault, all problems really come out of a lack of communication and I know I say, I preach


that I went against my own advice that I give all the time, it's to communicate well, it's


to build relationships and what not and I just didn't step up to the plate, I should


have let them know that I was going to be late or tried to reschedule and then I was


still going to the place to meet up and then I realised on the way there, I realised on


the way there that I rate things on a scale of 1 to 10 and if something, I can't use the


number 7 and if something is below 7 I don't really want to do it and if something is an


8 or above, well then it's definitely worth doing, so I thought about it and I was like


oh where does this rate on a scale and it didn't, at the time it didn't pass because


I've got so many other exciting things on, I think they rated above that so instead of


saying yes to something that I wasn't really going to be 100% interested in and not exactly,


like my actions didn't match my ambitions, I love riding, I love all this other stuff


but if I loved it that much I would have been on time, I would have been there so it's completely


my fault and that's just an example of how you can miss such great opportunities through


a simple lack of communication, so that's a lesson from my failure so you might not


have to do that in the future but I'm sure we're all learning, it's all going to happen


again but I think the best thing is going forward, you can never over communicate, so


note yourself, you can never over communicate, I'm going to go have some coffee and breakfast


and do some riding at this cafe I went to yesterday, yesterday morning, it was amazing,


so I'm going back.


Just finished up some experience with Adam on the road, so shout out to Adam, thank you


so much mate, we left from the main office of REA, the building, went out to a suburb,


about a 40 minute or so drive away and what we were doing was Adam was educating an estate


agent or an agency, that's the right terminology, I'm not across all the terminology, educating


an agency on a couple of the new products REA or realestate.com that you have built


and one of them was name page, so essentially you can pay if you're an agent to get on the


front page of the website or the front page of an app and I think I've got a demo somewhere,


some kind of demo video where you search for an agent's name in a specific, or no, search


for a property in a specific area and the next time you come back to the app, if you're


an agent and you paid for that front page placement, you'll appear on the front page


and what I loved about it is rather than sort of go straight hard and traditional salesy,


it was more of an educational piece, just letting the agency know what the features,


what the benefits were of the new product, of the new feature and I think that's a really


big point, rather than going straight up and I mean there's a place for all of this in


every kind of sales, but rather than going straight up and saying you should buy this


product or you should use this or you should spend X amount of dollars, it's really important


that when you go to somewhere, people aren't really going to understand what exactly the


product is and that's okay, what matters most is what benefits can that product give to


that person and that's a lot what we talked about at Apple when I used to work there is


rather than telling someone the iPhone has 12 megapixels of the new camera or the sensor


is this big or something like that, what are the benefits to that person?


So I think that's a really good takeaway, if you're going to explain a product or a


service to someone, put it in terms that explains the benefits to that specific person, give


them example of how it can help them, that's a key lesson that I learned from that and


I think been slowly learning it over time as well.


Now I think I'm getting a phone call from my brother, let's see what he wants.


What's happening my man?


I need a...


Yeah well here's what you got to say, here's what you got to do, it's no one's fault right,


but it's you're going to take responsibility for it, okay so you say something along the


lines of alright I didn't know, it was my responsibility to communicate with you effectively


that I didn't really know what was going on, I probably didn't communicate as best as I


could have, in the future here's what I'm going to do to rectify this, but at this stage


the job that I did last time remains in this state.


So that's all you can do I think really, there's no other way to do it, just say it's my responsibility,


here's the action I'm going to take next time.


That's it, just always bring it back on you, there's no point trying to externalise the


excuse onto something else.


Yeah I know, I agree with you and that's the way I was taking the email before I contacted


you to make sure that was alright.


Yeah just own it, own your L's bro, own your losses.


Hold on, back on another train, I just finished up some talk, he's at UX on the


user experience manager at REA, and I got some insights on how they approach UX calls,


how you approach UX calls or user experience, I'm using this acronym like I know it isn't


here, user experience in a systematic fashion that you can apply it to other requirements,


not just real estate or real estate.com, but it's home time for me, I'm going to fight


this patch.


When I get home or when I'm on the plane or something like that, I'll concise everything


that I've learnt or the main principles and takeaways that I've learned over the past


two days and do a little segment or something like that and I'll also write them down so


Rainy day on the farm (preparing garlic)

ស្ទំ ស្ទំ


ស្ទំ


Short Term vs Long Term Satisfaction

What's going on y'all? Episode 103 of the Daniel Burke Show. Sorry it's been so long since the last one.


Short Term vs Long Term Satisfaction

I know I said I sort of wanted to do these weekly or fortnightly or something.


Short Term vs Long Term Satisfaction

I'm still trying to work out that routine. No excuses though. We're gonna do one today.


Short Term vs Long Term Satisfaction

And what's the topic of the day? Well since it's a Friday, I figured I want to do something fun.


Short Term vs Long Term Satisfaction

Because Friday, F, fun, you know, you know how it goes. Everyone's happy on a Friday.


Short Term vs Long Term Satisfaction

I'm happy every day, well I try to be at least.


Short Term vs Long Term Satisfaction

Actually, speaking of that, I had an article published recently on Thought Catalog.


Short Term vs Long Term Satisfaction

It's called How I Stop Feeling Depressed.


Short Term vs Long Term Satisfaction

So although I'm positive and optimistic and outgoing, fun, whatever, 99.999999% of the time,


Short Term vs Long Term Satisfaction

I still have those days where I feel down and sort of not wanting to do anything.


Short Term vs Long Term Satisfaction

So I wrote an article on what I do to stop feeling down, stop feeling depressed.


Short Term vs Long Term Satisfaction

And Thought Catalog, the editors from Thought Catalog, reached out and republished it on their website.


Short Term vs Long Term Satisfaction

So I was pretty happy with that. It's the first piece of writing I've actually had republished somewhere.


Short Term vs Long Term Satisfaction

Either the Medium or my blog or something like that.


Short Term vs Long Term Satisfaction

So I'll link it in the description or in the show notes so you can all check it out.


Short Term vs Long Term Satisfaction

And show some love, show some hate, I don't mind, as long as you read it.


Short Term vs Long Term Satisfaction

But what's today's episode about? Yes, Friday, it's fun. We're gonna make it nice and quick.


Short Term vs Long Term Satisfaction

I figured, let's do 5 minutes of fire. Alright, how are we gonna do this?


Short Term vs Long Term Satisfaction

Well, I've got my iPad here, which is a timer, and I've got a topic that I've sort of been thinking about.


Short Term vs Long Term Satisfaction

And my dog's interrupting me here, trying to get through my door.


Short Term vs Long Term Satisfaction

I'll be with you in a second. So I'll record this, I've gotta be quick because she wants me to feed her or pat her or something like that.


Short Term vs Long Term Satisfaction

So I'll be with you in a second, Seven. Yes, my dog's named Seven.


Short Term vs Long Term Satisfaction

But that's another story for another time.


Short Term vs Long Term Satisfaction

So, today, 5 minutes of fire, what's the topic? Short term vs. long term satisfaction.


Short Term vs Long Term Satisfaction

So let's get started. Without any further ado, I'm gonna start the timer.


Short Term vs Long Term Satisfaction

Podcast listeners, you won't be able to see it, but YouTube viewers, you can watch this.


Short Term vs Long Term Satisfaction

5 minutes, let's go.


Short Term vs Long Term Satisfaction

So, I've been thinking about this for a few days, and I read somewhere once, and I've heard from a lot of people that if you think about something for a few days or a bit longer,


Short Term vs Long Term Satisfaction

you've gotta write something about it, or you've gotta, these are from creators by the way, from people I look up to.


Short Term vs Long Term Satisfaction

And if you're thinking about the same thing for a long enough period, you've gotta write something about it, or you've gotta make something about it to get out of your head.


Short Term vs Long Term Satisfaction

And this is what I'm doing today. So I've been thinking about short term satisfaction vs. long term gratification,


Short Term vs Long Term Satisfaction

or either way around, short term gratification vs. long term satisfaction. You can put it either way.


Short Term vs Long Term Satisfaction

Now, why? Well, because I've been trying to rework my life for the last few months.


Short Term vs Long Term Satisfaction

If you've been following along with the previous episodes, you'll know that, but if not, just take that into consideration.


Short Term vs Long Term Satisfaction

So I've been trying to work out, okay, where am I getting short term satisfaction, and where am I getting long term satisfaction?


Short Term vs Long Term Satisfaction

How can I find a balance in the middle?


Short Term vs Long Term Satisfaction

So for me, I think short term satisfaction is something like if you were to buy something, like you get that really gratifying, satisfying feeling of buying something new.


Short Term vs Long Term Satisfaction

Or if you do it, for me, it's working out. Like I find working out easy now.


Short Term vs Long Term Satisfaction

It's easier for me to work out than it is for me to not work out during the day.


Short Term vs Long Term Satisfaction

I get a really good feeling, I get a pump, I've been doing it for 6, 7, 8 years now, so I'm quite good at it.


Short Term vs Long Term Satisfaction

I've still got a lot to learn, but for me, it's a short term satisfaction.


Short Term vs Long Term Satisfaction

Like I get 45 minutes and I get a really good feeling.


Short Term vs Long Term Satisfaction

What I'm trying to get more of, or trying to think about how I can implement it more in my life is long term goals, like long term satisfaction.


Short Term vs Long Term Satisfaction

Now, what is long term satisfaction?


Short Term vs Long Term Satisfaction

Well, it could be working on a project for an extended period of time, or going to university, doing a 4 or 5 year course, or something like that.


Short Term vs Long Term Satisfaction

It's these longer periods, starting a business, something that takes a longer period of time to actually get that satisfying feeling from.


Short Term vs Long Term Satisfaction

And why do I want that?


Short Term vs Long Term Satisfaction

Well, I've always worked best with a long term goal in mind.


Short Term vs Long Term Satisfaction

And how I mean is like, you have a long term goal and then you'll be working towards that, but in the short term, you'll be incredibly fierce.


Short Term vs Long Term Satisfaction

You'll be working as fast as you can in the short term to hit your short term goals, but incredibly patient over the long term.


Short Term vs Long Term Satisfaction

So what's that look like for me?


Short Term vs Long Term Satisfaction

I'm just going to use myself as an example.


Short Term vs Long Term Satisfaction

So a long term goal for me could be, I want to start my own business or build my own company.


Short Term vs Long Term Satisfaction

And a lot of people think, like including myself, like there's a shortcut to everything, right?


Short Term vs Long Term Satisfaction

Like I could build it in a week or in a day or something like that.


Short Term vs Long Term Satisfaction

And if you don't reach it in that time period, well, you get unsatisfied.


Short Term vs Long Term Satisfaction

And I've been there in the past, don't get me wrong.


Short Term vs Long Term Satisfaction

I've definitely sort of tried to do something a lot quicker than what I thought I could do and become deeply unhappy.


Short Term vs Long Term Satisfaction

So now I'm sort of trying to delay gratification for a longer term as well as balancing it out with short term gratification.


Short Term vs Long Term Satisfaction

I haven't quite worked out the full balance yet, but ways that I've sort of found that are best to do that is realize what your long term goal is.


Short Term vs Long Term Satisfaction

So say, or you don't have to have a long term goal, right?


Short Term vs Long Term Satisfaction

Realize like what you're sort of working towards in the grand scheme of things.


Short Term vs Long Term Satisfaction

And maybe if you want to get fit, right? You want to be active and healthy.


Short Term vs Long Term Satisfaction

I had an email the other day from someone asking, what's a 21 day fix?


Short Term vs Long Term Satisfaction

And I don't like that concept of, I relate everything to fitness and nutrition because it's what I know best, right?


Short Term vs Long Term Satisfaction

And so I got an email asking me, what's a 21 day fix to jumpstart my diet and jumpstart my body and etc.


Short Term vs Long Term Satisfaction

Jumpstart my weight loss process.


Short Term vs Long Term Satisfaction

And I replied, think about instead of thinking about the 21 day fix, think about the 21,000 day fix.


Short Term vs Long Term Satisfaction

So what can you do for the rest of your life that's sustainable, right?


Short Term vs Long Term Satisfaction

And for me, thinking about that is scary at the same time as liberating.


Short Term vs Long Term Satisfaction

And it's exciting, exciting and scary at the same time.


Short Term vs Long Term Satisfaction

And where am I going with all this?


Short Term vs Long Term Satisfaction

Well, what's a practical example?


Short Term vs Long Term Satisfaction

Because right now I've been talking so much meta.


Short Term vs Long Term Satisfaction

So how can you fix this? How can you, I don't know, put some steps into action, right?


Short Term vs Long Term Satisfaction

What are some action steps that you can actually do rather than me just sort of rattling off this whole spiel about how I'm trying to live my life.


Short Term vs Long Term Satisfaction

So what I've learned from others and read and what not and practiced myself is a reward system.


Short Term vs Long Term Satisfaction

So look at your big picture. Look at your big goals.


Short Term vs Long Term Satisfaction

What do you really want to achieve?


Short Term vs Long Term Satisfaction

And if you don't have them, that's okay.


Short Term vs Long Term Satisfaction

These things come over time.


Short Term vs Long Term Satisfaction

It's only after trying a whole bunch of things that you realize what you actually want to do.


Short Term vs Long Term Satisfaction

No one's born with a passion.


Short Term vs Long Term Satisfaction

So think about what the big picture is and then break it down into small little chunks.


Short Term vs Long Term Satisfaction

And this time is about to go off, but I'm going to pause it so it doesn't make a sound.


Short Term vs Long Term Satisfaction

So yeah, think about what the big overarching goal is.


Short Term vs Long Term Satisfaction

Like for me, it's I want to help the world move more and eat better, right?


Short Term vs Long Term Satisfaction

So that's a very unspecific goal, but that's the big overarching goal, right?


Short Term vs Long Term Satisfaction

The overarching one doesn't have to be very specific in my opinion, but in the short term, make them ultra specific.


Short Term vs Long Term Satisfaction

Like, okay, I'm going to do this today. I'm going to work out this.


Short Term vs Long Term Satisfaction

I'm going to post a video on eating better. I'm going to write a post about how I lost weight.


Short Term vs Long Term Satisfaction

Like that's what I've done just recently actually.


Short Term vs Long Term Satisfaction

So short term goals, really specific, time-based, and I don't know, something you can complete within a shorter timeframe.


Short Term vs Long Term Satisfaction

Let's say less than a month, less than a month or three months.


Short Term vs Long Term Satisfaction

Long term goals, you're looking at three to five years or an unspecific.


Short Term vs Long Term Satisfaction

Like have that sort of as the end goal and the pathway you're going to build it with these short term goals.


Short Term vs Long Term Satisfaction

So the final, the big long term, the big moonshot will be, I don't know, the gate at the end of the road.


Short Term vs Long Term Satisfaction

And then each of the short term goals will be the road that you're building towards to get into that gate.


Short Term vs Long Term Satisfaction

Now, that was five minutes of fire. What's the daily challenge?


Short Term vs Long Term Satisfaction

Think about your short term and long term goals.


Short Term vs Long Term Satisfaction

I've got to go work out with some friends. I've got to get some of that short term gratification.


Short Term vs Long Term Satisfaction

But over the next few weeks and months, I've got to keep working towards the overall picture,


Short Term vs Long Term Satisfaction

which is helping everyone move more and helping people eat better.


Short Term vs Long Term Satisfaction

But that was a new experiment for this episode, episode 103, The Daniel Burke Show.


Short Term vs Long Term Satisfaction

You can check me out anywhere, mrdeyburke.com. All my social media handles are at Mr. D Burke.


Short Term vs Long Term Satisfaction


How to avoid knee surgery: Strengthen your glute medius (here's how)

Day 28, rest rona, the theme for today is preparation.


Now after a lot of trial and error,


when I used to be a weight lifter or bodybuilder


and whatever and I used to lift heavy weight


every basically every session,


I found myself I ran into a lot of lower back injuries.


I had lower back spasms and all that sort of stuff


because I was just basically going to the gym


and just start lifting as heavy as I could


as quickly as I could.


And eventually got to the point where I couldn't do that.


And so I kind of figured out, okay,


maybe I'm getting all these injuries


because my body's not doing things right.


And so physio sessions, a lot of reading and whatever,


and basically just trying to figure out my own problem.


I learned that my glutes didn't activate well enough


when I was doing heavy lifts.


So picking something heavy off the ground.


And my lower back was compensating for it all


because glutes, fairly big muscle.


And when that's not activating correctly, fairly,


some other, not that the lower back is a smaller muscle,


but when it doesn't have the aid of the glute


and you're trying to put all the strain on just lower back,


you're gonna run into issues.


And so this session is because of famous preparation,


it's a little glute mobilizing slash activating


little series of movements that I like to do


whenever I'm going to be doing lifting heavy objects.


So as in doing heavy deadlifts, doing heavy squats,


maybe some kettlebell swings,


or I just wanna start using my lower body.


I'm gonna try and activate my glutes first.


So let's follow along, get on the ground.


It's gonna be completely ground-based.


There's no rest.


We're just gonna go one leg and then into the other leg.


And then we do it twice.


So each leg gets done twice by the end of it.


We're doing 10 reps per movement.


By the end, you should feel a little pump


in your butt cheeks.


So let's go here.


We're gonna start on the ground, elevate,


keep about a six inch distance between your two feet.


Now you can have a little rest up here like I am.


And we're gonna start with the first movement.


It's just forward by six inches.


So coming back to center, here's center by the way.


So when I say center, that's center.


And now this whole time,


I'm trying to switch on my glute here.


So you can even poke it.


And we're just gonna go forward and back for 10 repetitions.


And that's about 10.


So come back to center.


And now we're gonna go back for 10 repetitions.


So again, just squeezing.


Squeezing, it's a lot easier for me to squeeze my glute


on the way back.


So keep going.


That's about, I'm not really keeping count.


But that's about 10.


And now we're gonna go all the way from the back


past center to forward for 10.


So it's one,


two,


three,


four,


five,


six,


seven,


eight,


nine,


All right, now we're gonna do 10 circles clockwise.


Still keeping that distance from our bottom foot.


Just drawing it as best you can.


Nine,


Now, anti-clockwise, 10 circles.


Concentrate.


Five,


one more for good luck.


Beautiful, 10.


So now we're keeping it there.


No rest yet.


We've elevated it.


Now we're doing 10 bicycles.


So pretend you're pedaling a bicycle here.


Wonderful.


Seven,


eight,


nine,


Excellent.


Now I'm already,


my right glute is already on fire.


So now, if you wanted to make that a little bit harder,


if you found that easy,


you can of course up the reps to 15,


12 or 15 or something like that.


I've just found that 10 suits me pretty well.


Now, or even add an ankle weight,


but I don't have an ankle weight.


So we're gonna do the left side.


Now this one will probably be a little bit harder


because even though,


remember, start at centre,


and now we're gonna go forward for 10,


just forward and back to centre.


Even though we only used our right leg,


it's still gonna use some of the other,


other part of the hip,


other part of the glute to compensate,


to help out, to help lift that leg up.


So I always find the second one's a lot harder.


There we go.


That's 10, now 10 back.


And again, each time you do it,


you could probably just go first with your left


and then go first with your right, whatever you want.


And keep going back.


Yeah, I'm feeling this one already.


We're lighting a little fire.


Keep it up.


This is gonna get your glutes nice and activated.


Now, 10 back and forth, one, two.


And I've probably got dirt on my feet


because I've been walking around in the backyard,


feeling the earth,


feeling the grass under my, this is good,


we're getting a burn.


That's about 10.


Now, 10, I'm gonna start anti-clockwise this time,


trick my brain up.


Three, four, five,


six, seven, eight,


nine, 10.


Now, clockwise, one, two, three.


How well can you draw circles with your left foot?


Five, six, seven, eight,


nine, 10, beautiful.


Now, 10 bicycles.


The slower, the better.


We're not going for speed here,


we're going for trying to prepare our glutes


for what's to come.


For lifting heavy weights if we need to,


or it's just good if you,


a lot of males I've found through my research apparently,


don't activate their glutes for heavy lifts


and just in general, so it's always good to,


that's 10 by the way, if you're counting on.


So it's always good to be able to switch them on.


I mean, if you spend a lot of time sitting,


your hamstrings and your glutes


probably don't get much attention.


So you could do this at any point during the day,


really, if you wanna go for a run.


If you wanna switch those legs on.


So let's go down.


Just a great way to get warmed up.


So now, same thing, just on the other side.


I go through this two rounds,


and I find that's enough for me.


Maybe you need to go for three rounds.


Again, we're just starting from middle, from center,


we're going forward.


We've done about five already.


And trust me, once you learn this sequence off by heart,


I've probably done this hundreds of times now.


Once you learn this sequence off by heart,


you can adapt it to how you need.


Maybe you find it's better if you just do circles.


But I found this is like back and forth,


because there are three different heads in the glute,


I don't know them all off by heart.


I just find that this is a pretty good all-rounder warmup.


Now, for the next one,


I'm gonna do a little bit of a warmup.


Now, all the way back and forth.


Another great one is if you have a band,


is to do squats with a band and step side to side.


But since we're just focusing on no equipment


for this little movement series,


we'll use no equipment.


Oh yeah, the second round,


we're really light knees on fire now.


Circles.


We'll go clockwise.


Clockwise.


Three,


four,


five,


six,


seven,


eight,


nine,


And clockwise.


Beautiful.


When I first tried to do this,


I couldn't do it all in one hit with no rest.


And still sometimes, depending on how fatigued


my legs and muscles are,


I'll have to put my leg down like this


and take a chill out.


Feel free to take a chill out whenever you need.


Since I've had a little bit of practice,


I can usually go the whole session.


That's about 10.


Let's go.


10 cycles.


Oh yeah.


We are really feeling the burn.


Leave a comment below.


When did you start feeling the burn?


Usually for me, it's by the back end of the second session.


So this is perfect timing.


Keep it going.


You're getting a little fun little challenge after this.


Okay.


Now, that right one is well and truly activated.


What I meant by the,


if you have a band,


one you could exhilarate, like just do this after.


We'll do the left leg in a second.


Is put a band just while I remember


and just step side to side like this with the band.


And of course, each time you're stepping side to side,


you're stretching it out,


using your glute to step sideways.


Now let's go.


So I might do this as well


if I'm going into like a Brazilian Jiu Jitsu match


or something like that.


Because a lot of it is a hip bridge.


All right.


Keep going.


Center the foot.


Anytime you need to use your glutes, basically,


it's always good to get them active.


Help out that back.


That's about 10.


Oh, the last one's always the hardest.


Eight, nine, 10.


I might've missed a few here and there,


but again, we're cooks, not chemists here.


When it comes to movement,


sometimes you wanna get sets and reps down,


but other times you just wanna move.


You just wanna, oh, this is great.


Keep it going.


Clockwise first this time on the left.


I can feel my face going red here.


Means we're using our concentration, you know?


Keep it up.


Now anti-climbing.


Keep it up, now anti-clockwise.


You could even use this as a full-blown glute workout.


Just do this for three or four sets.


Six, seven, eight, nine, 10.


Maybe one more in case I missed one.


Now bicycles.


One.


That's it.


Again, just all the time, maintaining that distance.


By maintaining that distance, you can go higher if you want,


but I found that about six inches,


about shoulder width apart is best for me.


Any lower, it's too easy.


Any higher, I'm getting too deep into the hip.


Beautiful.


Oh, feeling alive.


Your butt feels alive, your body feels alive.


Now you can, another one is you can go like this.


Hold a squeeze there for a bit.


But I think for me, that's usually enough.


Like if I just go through those two,


that's usually enough for me to say


that my glutes are nice and active.


Give that a try, leave a comment below.


Remember, just six inches apart,


10 reps forward, 10 reps back, 10 reps back and forward,


10 reps clockwise, 10 reps anti-clockwise,


and then 10 bicycles.


And of course, you can upgrade the repetitions if you like


or downgrade them, maybe start with five


if you can't do 10.


So if you're gonna lift something heavy


or if you've been sitting down all day


and you wanna utilize probably the biggest muscle


in your entire body, your butt,


for lifting something heavy,


rather than just getting your lower back


to take all the weight,


do that little warmup prep at the start


and then you'll be ready to move.


So as always, keep moving, stay safe,


Eyes tell all: How to tell that an AI generated a face?

Hi there!


We need to talk about GAN-generated faces after we spotted this paper, beautifully entitled


I.


Stell.


All.


Aww, when did paper titles become this catchy?


I personally do not have any trouble with making research sexy, as long as there is


no all-you-need in titles anymore.


As you have already guessed, this paper is highlighting that GAN-generated images, at


least with StyleGAN2, do not render the pupil accurately enough and therefore one can


build a GAN-generated face detector upon this feature.


Ms. Coffee Bean saw this paper received an enormous amount of attention on Twitter, with


reactions ranging from dismissing the importance of this pupil feature, because the next GAN


generation will fix this problem anyway, to cases of surprise that GANs did not capture


this human feature.


So what are the implications of this paper that highlights the pupil feature in particular


and more generally, how to tell GAN-generated images apart from real ones?


This is what we will discuss in this AI Coffee Break.


Hey, do you have your coffee ready?


Because now we begin!


This paper here highlights a problem in GAN-generated faces.


GAN stands for Generative Adversarial Networks.


If you want a refresher of how GANs work to produce images, check out our previous high-level


explanation about this.


So the authors investigate how GANs fail to generate circular or elliptically shaped pupils


for human eyes.


Therefore the authors propose a method to automatically estimate the shape of pupils


and determine if it is human-like enough.


Since GANs tend to generate weird pupil shapes, this should automatically detect whether the


depicted person is real or generated by a GAN.


But Ms. Coffee Bean sees here two categories of problems.


Let's go to problem number one.


The paper is framed like GANs in general have this property of generating weirdly shaped


pupils.


But if we look closely into the experimental section, we see that the authors have quantitatively


estimated this property only with StyleGAN2.


So to generalize to GAN-generated in general is a bit of a sneaky exaggeration.


Do not get us wrong here, it is absolutely the case that many other GANs have the same


problems with pupils too.


At least when we zoom in a lot.


For Ms. Coffee Bean the pupil problem is not even that strong in this case, which is strange


because the authors reportedly used exactly the same cool website, thispersondoesnotexist.com


do check it out, to retrieve some StyleGAN2 generations.


In any case, GAN generation quality is not there yet where details are rendered well,


just look at the eyebrows and eyelashes too.


These little details are easy to overlook if these GAN-generated images are used in


settings where pictures are not looked at in full detail, for example in bot accounts


on social media where the profile picture is so tiny anyway.


So understandably there is need for an automatic bot checker as the authors propose.


But the argument of using this pupil detector widely grows weaker, because StyleGAN2 won't


be NVIDIA's last generation of GANs ever, and other parties are training powerful GANs


too.


Especially since GAN-generated face detection is an arms race, now that the defenders know


of this pupil feature, the attackers will do their best to make the right generations


in the next version circumventing automatic GAN-generated face detectors.


So what we can learn from this paper so far is that when zooming in and looking at the


tiny details, we see problems in GAN generations.


It's not really news that tiny coherent structure is hard to enforce, look at the details of


hair, everything about the eyes, teeth or tongue or lips for example.


Ms. Coffee Bean should we write a paper now and lips tell it all?


Just kidding.


But coherent structure over larger distances is even harder, especially for elements with


highest diversity in the images in the training set.


We argue that pupils would be the first ones to be fixed in GAN generation really, because


that feature occurs in most of the faces, but structures spanning longer distances in


the image that are only exhibited by a subset of the data is far more problematic.


Just look at the background that is highly diverse in the training data and should be


diverse in the generations too.


It seldomly makes sense, unless it's uniform, like where is this person even situated?


Or look at the ears for example, which are not often featured both in images because


of the lateral angle or hair covering them up.


This picture was generated by StyleGAN2 too.


And look how the ear lobes are at different heights.


But sure, all these problems are not that easy for automatic detection with hand-crafted


features like the pupils are.


Funny side note, the problem with the StyleGAN generating bad pupils is weird, perhaps unexpected,


but it's well known that neural networks tend to pay attention to very weird tiny features,


like tiny details between animal species when it comes to their fur structure, while being


sloppy on more obvious features for humans, like learning that dogs usually have four


legs.


They surprise us negatively as much as positively.


Personally, I was astonished when learning that neural nets even pay attention to visual


chirality, being able to tell whether an image has been mirrored or not by judging from things


that are statistically more often encountered in one mirroring than in the other, like buttons


on men's shirts.


For more, check out our video.


Now let's get to the problem number two of this method.


The authors acknowledge that detecting how elliptical a pupil is could discriminate against


people who just happen to have not circular pupils.


The authors come with no real suggestion here to circumvent the problem and say that it


is an infrequent phenomenon and abnormal pupils were not found in the real images of the datasets


they used, consisting of professional photographs.


That this dataset doesn't contain any abnormal pupils does not mean anything to Ms. Coffee


Bean, since this could be due to any kind of selection bias.


Therefore she would like to stress the warning here not to use only an eye pupil normality


checker if at all, otherwise imagine people being told, sorry, we don't allow you to


create your Tinder account because our anti-GAN AI tells us that you have robot shaped pupils.


Bye!


So what to conclude?


That humans given enough image resolution and knowing where to look for errors can still


know if an image is GAN generated or not.


But the GAN that is making detection humanly impossible is not really that far away from


now, so we need automatic methods that detect those.


A method based on strangely shaped pupils is useful for now for StyleGAN2 and its likes,


but is short-lived like so many things in machine learning.


What Ms. Coffee Bean likes about this paper is that it puts things into perspective for


non-machine learning experts.


Let us know what you think about the paper in the comments and do not forget to like


and subscribe.


How to Create Your Own Data Science Curriculum with Coursera in 2021

I used to work at the Apple store, right?


And we had this system where you had a problem


with your device, you'd come in and say,


hey, I've got a problem with my device.


And we'd be like, oh, what's your problem?


And if we could fix it on the spot,


typically we would try to do that.


And, but if you couldn't,


if we couldn't fix it on the spot,


well then we'd book you in for an appointment


called a genius bar appointment.


And you see, I was one of those geniuses.


So it was my job as a technician to go up


and help you fix whatever problem you were having


with your Apple device.


And then one day, I remember,


remember the exact seat she was actually sitting in.


It was this girl and she had a problem with her phone


and I saw her name on the list, we checked her in before.


So I went over and said, hello, introduced myself,


asked her what the problem was.


And when I did go over,


I noticed her scrolling through a blue app.


And so of course to make conversation,


I always loved just making conversation,


meeting new people every 10 minutes.


I asked her, what's that app you're scrolling through?


And she said, Coursera.


At this point, I'd never heard of what Coursera was.


And I'm like, oh, okay, what do you do on that app?


And she's just like, well,


there's a whole bunch of different courses on here


and you can pick them and you can learn something.


And I was like, oh, okay, are you,


is that part of whatever you're studying at university?


And she's like, no, I just think it's,


it's always good to be learning something.


And we continued with the appointment.


I solved her issue of course,


but that one line,


that was like one of those lines where I was just like,


oh, it's always good to be learning something.


You see up until that point,


I'd only ever learned things that I had to,


as in I'd followed what I had to do at school,


I followed what I had to do at university,


I followed what I had to do with on the job training,


but that one line changed my entire concept of learning.


That taught me, well, I mean, that night,


I went home and researched, looked up what course there was,


and I discovered this treasure field.


I was like, oh my gosh,


you mean I can just sign up to this course


and start it now?


And yeah, I could.


It's funny how just tiny little chance encounters like that


can just change your entire way of thinking.


And so it wasn't actually until a couple of years later


when I left Apple and decided, you know what?


I wanna get into building the programs


that I'm helping to troubleshoot on these computers.


I wanna teach myself how to code.


And as I went through that,


learning to code for the fourth time,


I stumbled across machine learning.


And then lo and behold, I stumbled across Coursera,


and I figured, hmm, I've already spent enough time


at university this decade,


how about I just create my own online AI master's degree?


And so I did, I wrote a blog post about it,


and I just pieced together different courses


from around the internet and put them together.


And Coursera happened to be one of the resources


I used a fair amount and still to this day use.


So when Coursera reached out to me and they're like,


hey, Daniel, we've got this amazing offer


on our new service called Coursera Plus.


It's 100 US dollars off until January 10th.


And I mean, if you're watching this in the future,


even if you don't get the special deal,


it's still a good deal.


So would you be interested in making a kind of a


create your own data science curriculum video for 2021?


And I was like, yes, of course I would do that.


I've actually been meaning to make a video


like this for a while.


So big shout out to Coursera for sponsoring this video,


as well as creating an incredible platform


where eager learners like myself


and perhaps you watching this video


can create their own curriculum.


So without any further ado, let's dive straight into it.


What I've done is I've gone through the Coursera Plus.


There's about 3000 or so courses from there.


And about a year ago now,


I've created a video called five beginner friendly steps


to get started with machine learning.


And I've recreated that video slash curriculum


using purely Coursera courses.


So let's jump in.


So here we go.


I've created all this in Notion by the way.


And then you might be wondering why Notion?


Well, you could actually use anything.


I used a blog post to start my curriculum,


but Notion is just, it's really cool.


Let me just show you rather than talk about it.


But anyway, here we go.


2021 Coursera data science curriculum.


Now some yarn about how to create this.


If you're looking at the link in the description,


you can click duplicate.


It should be in the top right corner.


But because mine is, I've created this on mine,


I don't have the duplicate button.


Let's step through this.


So what's the goal?


The goal of this curriculum is to learn the foundations


of data science and machine learning with Python.


Now, why Python?


Well, it's the programming language


I have the most experience with.


You can write data science and machine learning code


in other programming languages.


However, I don't have experience with those.


So I'm gonna stick to what I know.


Now date started yesterday


cause I created this little document yesterday,


but how about we do a little at


and we can go at today.


See, this is the cool little things that Notion has.


And date to go through, December 31 at the end of this year.


So you'll find as we go through this,


there's actually a fair amount of content here


that you could go through.


It could take you an entire year.


But remember, this is a beginner friendly,


starts from the foundations and anything worthwhile


or anything worthwhile to learn takes a while.


I said, while a few more times there, it doesn't matter.


So this is what I like to do


whenever I'm sort of creating my own curriculums in Notion.


This is the exact same layout I used


for my getting TensorFlow developer certified.


So the materials that I followed for that,


I usually have status to do notes, curriculum,


curriculum tracker, extras and log.


So status is just a one line of exactly what I'm doing.


So I'm currently putting together an outline


of what I'd like to learn throughout 2021.


Boom, that's all we're doing.


We're keeping it simple.


And I'll often change this as I make progress.


So this entire like curriculum itself


is really just a note to yourself,


like a way to keep track of what you're learning online.


Because what does a university course give you?


And again, this video is not,


I actually haven't said this yet,


but this video is not arguing


whether you should go online or university.


But if there's anything 2020 has taught us


is that every individual needs to be in charge


of their own health and their own education.


So this is catering to that education piece.


So to do, I often put a bunch of little just tidbit


to do's here right below the status.


So choose course outline, we've done that.


We're gonna see that in a second.


Set up curriculum path and notion, boom, we've done that.


Sign up for Coursera Plus, let's click this.


So this is the Coursera Plus deal we've got here.


Coursera Plus, corsera.org, corseraplus.com.


Now, this is in Australian dollars,


cause I'm Australian.


You can get 14 day money back guarantee.


So if you go through this, you sign up and you realise,


holy crap, I don't wanna do this.


You can get your money back within 14 days.


But right now there's a hundred US dollar discount.


Again, my numbers might be different to yours


depending on where you're from,


but it's only valid through till 10th, 01, 2021.


You've got a whole bunch of great universities here


and different organisations you can learn from.


Yada, yada, yada.


I'll let you go through the details about Coursera Plus.


What we're mainly focused on here is


how quickly you can create your own curriculum.


So sign up for Coursera Plus.


Let's pretend we've done that.


Now, these are just the five specialisations


I've gone through on the Coursera Plus.


Here we go.


So we've got a whole bunch of different specialisations


and courses that you can do that are included.


So you sign up to this, right?


You get unlimited access to everything down here,


which is actually an incredible amount.


There's actually too much.


Like there's 3000 plus courses,


guided project specialisations


and professional certificates.


Now, too much for one person to go through.


So that's why I've narrowed it down


to five beginner friendly steps


that you can get started again.


This is for beginners.


If you have about a year's experience in machine learning,


you probably wanna sort of follow your own curiosity


at that point.


Now, these are the main to-dos.


Of course, you can update those as you need to go.


I've got my notes here.


We'll dive into that, what that means.


Curriculum outline.


This is probably one of the most important points.


So what and why I'll be going through


to learn the foundations of data science


and machine learning with Python.


I always start off with the learning how to learn course.


I did this and again, another one of those things


like a book you read and it changed your life forever.


This course changed the way I view learning forever.


I believe this should be compulsory,


the learning how to learn course.


Let's click on that.


For everyone who's embarking


on any kind of learning journey,


whether it be through university,


whether it be through school,


whether it be through online, this is phenomenal.


If you're going to be creating


your own self-driven learning pathway,


you might as well learn how to learn.


Learning is the ultimate meta skill.


If you learn how to learn, you can learn anything.


So this is number one.


We come back, oh, we need to go back to Notion.


Now we get specific into learning


the foundations of data science with Python,


machine learning and data science with Python.


So if you have zero programming experience,


you're probably gonna have to learn Python.


Again, you could choose whatever programming language


you want, but if you choose Python,


you probably have a plethora of resources


or you do have a plethora of resources available.


And I am a big advocate for Python because it's what I use.


I can't advocate for other languages


because I don't use them.


So Python for everybody, specialization,


this will take you from not knowing any programming


to knowing Python, the fundamentals of Python.


I mean, Python is a general programming language,


so it can do basically anything.


So you're going to have to get familiar


with the fundamentals if you're going to start writing


data science and machine learning code with Python.


Speaking of which, that is the next one.


So the applied data science with Python specialization.


So we've gone through learning how to learn,


we've gone through Python for everybody, or you have,


and then you're going to go through the applied data science


with Python specialization.


Again, Coursera Plus has unlimited access


to 3,000 plus courses, so if you don't like the path


that I've laid out for you, you can customize it yourself.


But I've gone through them and I believe this is phenomenal


to get a foundation in data science and machine learning.


So this one, I've personally gone through this one,


I have a video on that, or I have multiple videos


on this, I believe.


It will start to get you, so again,


Python is a general programming language.


So this will specifically start to get you hands-on


with data science or data science specific Python code.


So the Pandas library, the Matplotlib library


for visualizations, and the Scikit-learn library


for writing machine learning code.


Now we come back.


Next is the machine learning specialization.


So now, so this one, the applied data science


with Python specialization, is mainly focused


about manipulating and visualizing data,


two massive steps with machine learning.


So this will get you, give you experience starting


to work with data, to get it ready to be used


with machine learning models.


And now, for actually using machine learning models,


is the machine learning specialization.


So if we come down here, what have we got?


So it goes machine learning foundations,


a case study, machine learning, regression.


So regression is a problem to predict a number.


So say you want to predict the prices of houses,


you would use probably a regression machine learning model.


Machine learning classification.


So classification is a problem like say,


diagnosing whether, or using a machine learning algorithm


to say whether an image is of pizza,


or an image is of a burger.


Clustering and retrieval.


So clustering is say, to group like individuals together.


So say you had a lot of customers to your website,


how do you group the ones that mostly purchase


in some months with the ones, or against the ones


that mostly purchase in winter months?


Again, you might not know when you,


before you cluster them, which months they purchase in.


And then information retrieval,


such as pulling out information from a database.


So then we go back.


So once you've gone through


the machine learning specialization,


the final one I've recommended here


with this five step approach


is the mathematics for machine learning.


So up until now, this curriculum has had a focus on,


of course, learning how to learn, learning Python code.


So the fundamentals of one of the most popular


machine learning and data science programming languages.


And then once you know the fundamentals of Python code,


you've started to learn data science specific Python code.


Then you started to, once you've learned how to manipulate


and visualize data with Python,


you've gone into writing machine learning algorithms


with Python.


And now, since we've been focused on code,


you might be curious as to what all that code is doing.


Because right now, you're just writing Python code


and it's triggering some machine learning algorithm


to do something to find patterns in data.


Well, what's going under the hood is math.


So that's why we've got at the end,


mathematics for machine learning specialization.


So once your interest has been sparked,


once you know how to diagnose a machine learning problem,


you might want to, or you probably will, I mean, I did,


dive deeper and figure out the nuts and bolts


as to what's going on behind that machine learning algorithm.


So we'll come back, that's the outline there.


Let's go on to the curriculum tracker.


Curriculum tracker is possibly the most important part


of this entire curriculum, other than the material itself,


other than you personally going through that material.


But this is how you stay on top of what you're learning.


Because remember, what does a university course offer you?


It offers you accountability.


But what we're creating here with creating our own curriculum


is accountability for ourselves.


So I just use the basic Kanban system here.


So I keep it as a card.


I put the learning how to learn course here, right?


And I've broken it down.


I've got a to do at the top.


I've got part one, part two, part three, part four.


Then I've broken it down.


I've got some headings here.


So let's put this over this side and this over this side.


And we will go learning how to learn.


So all I've done is I'm going to open this one up as a page.


All I've done is broken the topics or the syllabus


into little headings so that I know


I need to go through part one, what is learning.


I've got what is learning here.


I'll add all of my notes into this section there.


Then I come down to part two, chunking.


I'll add all of my notes there.


And the same thing, so on, for part three and part four.


So I could go back and I put a little due date here


for myself.


If I want to start today, which is January 3rd,


and I want to go till, say, the 1st of February.


Now where did I get this timeline from?


I believe all of the courses give you an estimated,


there we go, 15 hours to complete.


So maybe that timeline is a little extended.


But this is where you want to create.


If you're creating your own self-driven curriculum,


you can decide how fast you go.


One of my favorite blog posts, I'll link this below,


is called There's No Speed Limit by Derek Sivers.


Basically what it says, if you'd like to learn something,


you can go as fast or as slow as you want.


But check out that post.


That's a little bonus for this.


Now if we come back to our curriculum,


we're going to go up here.


So see how I said before, we'll come back to notes.


This is where my learning how to learn notes live now.


So if I wanted to reference something back there before,


I could just click in here.


And then I get the card.


So we could do the same.


Let me demonstrate how we do the same for one


of the other courses.


So we go here, Python for Everybody Specialization.


Going to open this as a page.


We've got a beautiful little Python symbol there.


That's one of the little tidbits about Notion


that make it fun to use.


So I've got a due date, February 1, 2021 to April 1, 2021.


Of course, you can adjust this for whatever you need.


But I typically like to do one thing at a time.


So I'll go through the learning how to learn course,


and then I'll go through this.


But let's go here.


So say if we go Python for Everybody, boom.


Let's write down.


Let's see what's in here.


And I mean Coursera, you could probably


develop this feature for yourselves


instead of me having to do it through Notion.


So if you're watching this, that's an idea.


Well, that's a great web app, actually.


I love a web app that does specifically this.


And to do, we got programming.


So I'm going to break all of these courses down.


Programming for Everybody.


Because what I'm doing with this is making it so that,


of course, if we looked at all of this in one hit,


there's a lot going on.


But when we break it down into smaller steps,


like we're doing now, so using Python to access web data,


so that's course three.


And then number four, using databases with Python.


And then that's it.


So it's four courses.


Now, where's the timeline here?


OK, approximately eight months to complete.


But that's at three hours per week.


So again, there's no speed limit.


If you decided to perhaps commit 20 hours per week,


which is three or so days, depending


how much you're doing, you could accelerate this by six times.


So really, instead of eight months to complete,


it might only take you two.


So now we'll come down here.


And then what I'll do is I'll break these


into their own individual headings.


So that way, I have, so part one is programming for everybody.


And then I'll write all my notes for that section there.


And then two, we'll do the same thing, create a little heading.


I might speed this up.


Boom.


Wonderful.


So now I've got a little section.


And we go back, go back.


So this is back in our curriculum.


What I can do here, oh, I don't want to do that,


is I can press Enter.


And then if I want to link to page,


and then if I want to search for Python, there we go.


So now I've got all my notes linked here.


And I can do the same, or you could do the same,


for the other courses.


So the applied data science with Python specialization,


machine learning specialization, the mathematics


for machine learning specialization.


And now we've got them in a Kanban view.


Another beautiful way that we can do,


that we can look at them, is to change it into a timeline view.


So you can see where you are.


Let's say, for example, we wanted to start


at the start of the year.


So look, I've set up some due dates for each of these.


So I know how far or how long I have


if I want to stay on top of what I'm working on.


So again, learning how to learn goes to February 1.


And then Python for everybody goes from February 1 to April 1.


And then I've got the applied data science specialization.


I've given myself two months for that.


It goes from April 1 to June 1.


The machine learning specialization,


that's a pretty hefty one after looking at the curriculum.


So I've given myself three months for that,


from June 1 to September 1.


And then finally, I finish off the year with mathematics


for machine learning specialization.


So this is just a really cool way to not only keep on top


of what you're learning, but to also keep on top of your notes.


And I mean, once you've added some notes here,


if you want to come back, everything in Notion is searchable.


So you might be thinking, OK, yeah, Daniel,


I've gone through all of this.


It's actually pretty cool that I can just create it


all into my own curriculum.


I mean, this took me all of an hour.


But again, don't be fooled by how quickly you can put this


together or get confused with how quickly you can put this


together as to how quickly you can learn it.


Like I've literally laid out a year's worth of study here.


Again, you can take as long or as short as you want.


But you might be wondering, what are some extras that I can do?


Well, you might want to go on to the deep learning.ai courses,


but you will need some prerequisites for those.


And these will definitely set you up for them.


Now, the deep learning.ai courses aren't included in Coursera Plus.


So you'll have to keep that in mind.


But all of these are.


And then there's probably one more thing.


And that's this article here, Read Through Q Experience.


So this is an article I wrote.


Because a lot of people ask me sort of,


I've gone through some foundational courses.


What do I do next?


How do I get experience?


Well, I put together, that deserves its whole video.


I think I've got a video on that.


And I've also got a full length article on it.


So make sure you check it out.


This is what you should do after you've done some, there we go.


I've done some courses.


What do I do now?


Hint, touch the stove.


The main point in this article is basically,


if you're looking for a job, start it before you have it.


And of course, share your work.


So we come back here.


There's one more thing I want to do.


And this is, log is probably one of my favorite sections


of this entire curriculum.


Because it's just a little daily updater of reminder or something.


No, I don't want a page.


I just want today.


It's just a little way to sort of write down what you did that day.


So today, I created my 2021 data science and machine learning


curriculum in Notion.


Tomorrow, I start learning how to learn.


And so when you first start, you're not


going to have very many of these.


But I like to just write two or three sentences


at the end of each day of what I did that day.


And by the end, you'll be able to scroll through and go,


holy gosh, on those days where you feel like you've learned nothing,


which is almost every day, you go, wow, I'm


a lot further than when I started.


So make sure you play around with this Notion template.


I mean, of course, you don't have to use Notion.


You don't even have to use Coursera.


Everything that I've listed in here, you can learn online.


One of the benefits of using Coursera or Coursera Plus


is that everything is in one place.


And if you are going to sign up to Coursera,


they have that $100 promotion until January 10.


If you're watching this video in the future, that might be expired.


But that's OK.


Press the Duplicate button of this anyway,


and use it to create your own curriculum using Coursera


or not using Coursera.


The main takeaway of this entire video


is that the internet has provided a resource for you


to create your own learning journey.


If you have any questions about anything that we've discussed,


leave a comment below, and I'll answer it.


If a few people are asking the same question,


I'll add it to the blog post version of this video,


which you'll be able to find where you find links.


But as always, keep learning, keep creating, and happy 2021.


A brief history of the Transformer architecture in NLP

Hello!


If you are here to join our little lesson about the recent advances in the Natural Language


Processing or short NLP field, you are exactly at the right place.


We will follow the young but explosive life of the Transformer architecture that revolutionized


the AI field of NLP.


It all started a few years ago, where the fun in AI was happening in computer vision,


with the great success of CNNs on the ImageNet challenge, the success of Neural Style Transfer,


and of Generative Adversarial Networks, and the list can go on and on.


Computer vision was attracting headlines and NLP was not so much in the public's attention.


Of course NLP researchers were working hard too.


For example 2013 was the year of Word2Vec, based on the idea that neural networks should


be forced to learn similarities between words based on word distributions.


For example, the word Apple is often used near the same words as Microsoft or IBM, so


it is mapped close to these in the vector space.


But Apple is also used often with words like pear or other fruits, so we see that we cannot


disambiguate words contextually with this method.


2014 and 2015 were marked by the rising popularity of the recurrent neural network that made


it feasible to not only solve classification tasks better, but also tasks for sequence


to sequence modeling.


An example for generating a sequence from a sequence is machine translation.


The idea of recurrent neural networks or RNNs is simply put the following.


The same neural network block is applied recurrently on the input sequence.


Reaching the sequence end, the module ideally has learned one single vector that captures


the meaning of the whole sentence.


And from this single vector, one can start to translate by reverting the process in the


decoder.


But one problem of the method is, it works well for short sentences like I am working


hard, that can be perhaps captured by a single vector, but not for sentences like the second


one.


The RNN block reaching the end of the sequence is so overwhelmed by the input that it forgets


what the beginning of the sentence was.


2015, 2016 were the years that took care of this forgetting problem by employing the attention


mechanism.


This attention is nothing more than a way to say which part of the sentence should get


more importance.


In other words, attention tells the RNN what it should focus on and not forget.


Yet another problem with recurrent neural networks is that they are recurrent.


Unfortunately sentences are processed sequentially, word by word, like generally humans do.


For a machine this means however that it needs a lot of time to process huge corpora of text.


And here it is, where 2017, BAM!


The finally long-awaited ImageNet moment came also for NLP with the Transformer architecture.


It first crashed the party with the attention is all you need paper.


Like the title already says, if RNNs and attention are so slow because of the sequential processing


of RNNs, let us just use only attention and throw away the RNN part.


And boom, here it was, the Transformer.


In practice this is a little more complicated than this and we will explain the Transformer


architecture in the next video.


Here we put it very simply, Transformers, like RNNs, handle sequence data, but not in


a particular order.


This means that it can train much faster with parallelization.


Now focusing less on how it works, we focus what a Transformer means and what came out


of the original Transformer.


Because this architecture and its descendants proved to be improving the state of the art


across the board, like in machine translation, sentiment classification, coreference resolution,


common sense reasoning and so on and so on.


It has been even also successfully applied on translating from one programming language


to another or for solving symbolic mathematics.


And if you want to see the Transformer in action, do not hesitate to play around with


demos like the one here.


Coming back to our history lesson.


2018, Google researchers developed a bi-directional version of the Transformer and they called


it BERT.


Yes, you heard right, BERT from Sesame Street.


Now let's not get distracted by the weird names researchers choose for their model and


let us focus on the word bi-directional regarding the Transformer.


Bi-directional means that it allows the information to flow forwards and backwards as the model


trains, which results in better model performance.


Versions of BERT are one of the most successful advances in NLP.


Like Word2Vec, BERT is also mapping similar words closely, but it is context sensitive.


This means that a different word vector is computing for a word if it is encountered


in different contexts.


For example, if we have two sentences, like this game is just not fair and I had a great


time at this fair, the word fair will have very different word vectors with BERT, but


equal valued word vectors will be computed by Word2Vec.


And after BERT, everything just escalated for attention-based architectures.


In 2019, researchers developed ROBERTA, Ernie 2.0, XLNet and this to only name a very small


fraction.


All improve upon BERT, both conceptually and in performance, but these architectures just


keep coming, there is no light at the end of the tunnel yet.


And now to make it even crazier, we close the circle by going back to the computer vision


community because the transformer is also making waves there.


The transformer proved to be so performant on text sequences, that it was also applied


to pixel sequences, or as we usually call them images.


And wait, there is even more!


If we can apply transformers on text or images, why not apply them on text and images simultaneously?


Here we are in the so-called realm of multimodality, where the goal is to process different input


sources at once.


So still 2019 was the year of VLBERT, VisualBERT, VILBERT, UNITER and this is just to name a


few.


All these papers came out around the same month of August 2019, three of them on the


same day.


What a time to be an NLP researcher, hard to be not both overwhelmed and overly excited


at the same time, to finally end this crazy enumeration.


The transformer works so well on multiple tasks and in many cases even though it was


not explicitly trained on the task of interest.


So we are now at a point in NLP where we have to investigate if a neural network trained


on task A is also able to more or less solve a task B.


For more information on how to probe for this, check out our previous video linked here in


the video and in the description below.


If you want to know more about the inner workings of the transformer, wait until our next video.


Learn TensorFlow and Deep Learning fundamentals with Python (code-first introduction) Part 2/2

Hello and welcome to part two of this fairly long or lengthy, it was originally going to be 14 hours long video


But YouTube's video limit is 12 hours


So I've separated it. Part one, if you're watching this you should have already watched part one


So if you're hearing me say you should have already watched part one


Click on the link to part one and make sure you go and watch that but this is part two which basically


Continues off right where part one of this learning TensorFlow and deep learning code first video series


Starts off


So I want to hold you up all the links you need such as the course github repo all the materials will be down below


And if you want to sign up to the full version of the course which is about 20 plus hours more of the materials


Than what you've already covered there'll be a link to that in the description below as well


without any further ado


Happy coding


Now we've seen that our neural network that we've built can model straight lines with somewhat better than guessing


accuracy


however


When it comes to non straight lines, it's not doing too well. And so we also mentioned


The missing piece we're like treasure hunters here. The missing piece is non linearity


So I cannot emphasize enough how important this concept is for neural networks


Now before we write any code, I want to introduce to you a little question here


I want something to think about just over the next few videos while we we start to learn about non linearity


What could you draw if you had an unlimited amount of straight in other words linear and non straight or?


Non linear lines. So just think about that. What kind of patterns could you draw?


You have an unlimited amount of straight and non straight lines


Let's look at our data again. We have some linear data


So you might think is this possible to model with straight lines? I think so and


Then we have some non linear data


Not possible to model with straight lines now you could post an argument to say


Oh, well, Daniel, what if you just draw really small straight lines and go in between the gaps here?


Well, that is one option, but let's just pretend that we need straight and non straight lines to draw


The patterns that we need so keep thinking about this question


And again before we write any code, we're going to have a little bit of a play around. I


Introduced the tensorflow playground in a previous video


However, we haven't looked at it together


I'm just going to playground at tensorflow org and


We can set up our data here what I might do is zoom in nice and far


Okay, beautiful. So we have a few things going on here. We have data now


this data looks very similar to our circulator that we're working with except they're using orange and blue dots instead of red and blue dots and


We have some features x1 and x2 we could pretend that this is our x0 and x1


now right now this neural network has two hidden layers what we might do is reduce this down to one hidden layer and


one hidden neuron


So a very simple neural network and let's have a play around what are these?


We've seen the learning rate. All right, let's switch Swiss to what have we seen before?


I believe Adams is the default is


0.001 and


Activation hmm. We haven't really played around with these all we might do is switch this to linear


Now we've seen what a linear data is what linear data looks like and we've got regularization rate problem type


Classification. All right, so let's just leave it at these settings for now and I'm going to press play and see what happens


So we get the test lost


That starts to oh, it's just evening out


at about


0.5. What does that remind us of?


Do you remember our model that we built previously that only ended up with?


50% accuracy. I think this is what's going on here. I


Know to test this out. How about we recreate this neural network of what's going on? We've already got the data


we've already got we can create the hidden layer and


We can set the learning rate. We can we might see how we can set the activation to all right, let's do that


Let's go back and let's write some code


So come in here


Now, how might we do this?


Just as before we're going to set the random seed and


TF random dot set seed


42 and now let's go number one


create the model model for


TF Kerris


Sequential we come down here and then we want to go to you have Kerris dot layers


dot dense


one and the activation can be


What did we set the activation to in here linear? How might we get access to that?


well one shortcut is to just go linear as a string or


We could do TF Kerris dot activations dot linear


All right. Now we've got a parameter that we haven't seen before


Activation but we we actually have seen this before if we go back to our slides and


I think we might have to go a few back here. Yeah


There we go. We've got an activation parameter now


We're going to start to look into we've seen how we can add layers to try and improve our deep model


We've seen how we can increase the number of hidden units, but right now these aren't really working for us


So we're up to changing the activation functions


Alright, so we're we're working with this hyper parameter here now, let's go back and


What we're going to do is compile our model


Because we want to recreate what we've done in TensorFlow playground and see if we can replicate those results


this is a very a very good exercise to go through for yourself if you're playing around with the TensorFlow playground and


To just re recreate what you can see in there


Binary cross entropy. This is the because we're working with a binary problem now


This is another way you can write the losses. You can actually write the losses in many different ways


We've seen this one binary cross entropy and we could even do it


Let me just introduce you to writing things as strings


cross entropy


Just in case you see it something like that somewhere else and the optimizer


We can also set this to Adam that would be the same as writing it out like this tf. Kerrers


Optimizes dot Adam the benefit of being able to write it out like this is that we can set the learning rate


Remember LR is short for learning rate


and the metrics can be


accuracy


So we've got the same thing so far


Come back to our neural network playground features X. We haven't passed our model data yet, but we have these


These are our blue and red dots


We've got one hidden layer with one neuron and the activation is linear. The learning rate is 0.001. Okay


Now, let's see if we can number three is fit the model


And we're going to start to add the history variable here whenever we start to fit models now


Because this will come up later on just want to start building that habit


Alright


Let's do it a hundred epochs


I mean this one's run for over a thousand


but we're just going to stick with a hundred so we don't have a incredibly large output in our notebook and


shift and enter you


All right, what kind of results are we getting well looks like our models performing worse than guessing right now


Is that aligned with the okay? So the tether flow playground is getting quite similar results


They're basically still guessing as well


if you were trying to separate these two the blue and the orange dots if you were just tossing a coin for a


Thousand different times and you got heads and tails five hundred different times. Well, you're going to get these about these results


hmm now


Let's remind ourselves of what our data looks like because whenever our models


Predictions aren't working very well


We can evaluate our model with by looking at the evaluation metrics or we can adhere to our model of visualize visualize visualize


so our data is


Zero get the zeroth axis of X and the first axis of X and we'll color it with Y and


we'll choose a color set up as


plot color map


red yellow blue


So we're just reminding ourselves of what our data looks like all right wonderful and now we've got model 4


Which is a trained model?


Albeit looks like it's not performing very well. Let's check out its predictions anyway check the decision boundary


For our latest model


so


This is why we created a handy function before


Our beautiful plot decision boundary function where we pass it our trained model this can be model 4 will pass it our features


X and


Y what does it look like where's our models decision battery? Oh my goodness?


It's all over the shop


so our model if


blue is the blue class and


Red is the red class. I mean reds right up here yellow is the crossover because we've set our color map up as so


Our model is basically going you know what anything in this yellow could be blue or red


Which is basically why our models accuracy is below guessing


Hmm now what can we do?


It looks like our models still predicting straight lines


So what we might try is in the next video or actually if you want to jump ahead


Reset the TensorFlow playground and try playing around with activation here. What happens if you set it to a different value?


See what happens and we'll go through that in the next video


In the last video we had to play around with the TensorFlow playground and we saw that


Setting the activation function with one hidden neuron one hidden layer learning rate of 0.001


Didn't result in a very good separation of the orange and blue dots similar to to the neural network that we built


For our latest neural network that we built


So there's model 4 and model 3


We just copied what we've got here now


I've hope you've thought about this question here


What could you draw if you had an unlimited amount of straight linear and non straight nonlinear lines?


So remember our data is that we're trying to model for classification is nonlinear


So it's not possible to model with just straight lines alone


If we come back here, we've spoken about nonlinear


Right now our activation is linear. How about


If we change this activation function to something that is not linear


So it could be any of these three values, even though we haven't we haven't seen what these are


Let's just change it to let's try the top one


Relu okay, and let's see what happens if we just run this


Hmm still not getting a very good value


Wonder if we just keep it training for longer


Well, we're almost at a thousand epochs now and it's still not improving


but


Before we explore different ones here. Let's see how we might use the relu


Activation in our own neural network. So let's come back to our notebook


Let's just see if we can replicate we'll take another practice and replicate this neural network here


with tensor flow code


so


First of all actually we can use the neural network to replicate the neural network


So first of all, actually we'll put in a note here. Let's try build our


first


neural network with a


nonlinear activation function and


Now when I say nonlinear, I really mean just anything that is not linear


So we could choose linear it didn't perform very well


But if we wanted to choose any of these which are not linear, we're going to start with relu which is very common as


You'll see in your deep learning journey. So set random seed


TF random


Set seed 42 now we're going to create a model or we'll put number one here


So just remind ourselves create a model with a nonlinear. So just anything but linear


activation so we're going model 5


equals


TFKeras


sequential


And then we go back here TFKeras


layers


Dense and we'll just keep it the same as what we did before we'll set activation equal to you see here by default. It's none


So this is where we're going to set it to nonlinear and we could do relu like this or we could do TFKeras


dot activations dot relu


Wonderful. Now two is we're going to compile the model just exactly as we did before


model 5 dot compile


Loss equals what problem are we working on TFKeras?


losses


We're going to use binary cross entropy and what optimizer have we been using the atom optimizer?


Optimizes dot atom. We're just setting the LR to what we have in the tensorflow playground


dot zero zero one


just the same there and


Then the metrics is going to be


Accuracy now


number three


Let's hit the model


Model 5 it's exciting our first model with a nonlinear


Activation. Oh, we said we were going to start developing the habit of saving the history


XY epochs equals a hundred. Let's see what happens. Oh


What did we mess up here


TFKeras


dot


optimizers a typo as usual


Alright wonderful


So we keep going we're using a nonlinear activation function remember in this case nonlinear is just anything but linear and


Again, our model ends up performing


basically worse than guessing


Hmm, so it's still not learning. What if we come back to our keynote?


Now we go back to the slide of where we looked at improving a model. So so far. This is what we've tried


We've tried adding layers


We've tried increasing the number of hidden units and we've tried changing the activation function as well as the optimization function far out


We've tried a fair few things


But we've only just tried changing the activation function. We haven't done that in


conjunction with these two


so how about we try that what if we increase the number of neurons and layers and


Change the activation function. I've got an idea we can try that


in TensorFlow playground first


so before the next video go back to the TensorFlow playground and


Try increasing the number of hidden layers the number of neurons


Keep the activation and the learning rate the same and everything else the same


But just increase the number of hidden layers to whatever you want. I think there's a maximum of six. That's alright


whatever you want and increase the number of hidden neurons to whatever you want and then


Run it and see what happens and if you really wanted to if your neural network works, or if it doesn't


Reproduce it in TensorFlow code and I'll see you in the next video where we're going to do everything. We just talked about


Welcome back. How'd you go?


Did you manage to to get the TensorFlow playground to find the patterns or distinguish orange from blue dots?


Did you get the test loss to decrease the training loss to decrease?


Did you increase the number of hidden layers or the number of hidden units? I hope you did


But if not, that's what we're going to go through in this video now we've discussed


That we've tried to improve our model and we tried adding layers. We've tried to increase the number of hidden units


We've even tried to add a nonlinear activation function because the data we're working with is nonlinear


We've even tried changing the optimization function, but we haven't done all of these in conjunction with each other


so let's have a play around with the TensorFlow playground and see if we can


Adjust those parameters or hyper parameters. See if we can get this


to distinguish patterns between orange and blue dots


So I'm going to add another hidden layer and I'm going to increase this


To let's say four neurons. Yeah, that's a good number and I'll do the same for this layer


All right, I'm keeping everything the same. So just increase the hidden layer increase the number of neurons learning rate the same


Activation is the same. It's nonlinear. So at the moment, it's relu and


Let's press play and see what happens. Oh


Whoa, what do we got here?


That's a good sign. The test loss is going down and it's continuing to go down


Oh


Wow, all right, we're nearly at a thousand epochs it's still going down. Let's just wait here for a second


Is it gonna keep going down? Oh, look at this


So it's starting to be able to distinguish orange dots from blue dots


Now once it hits 2000, I think we'll stop there you could leave yours running for longer and see what happens


But how cool is that?


so just by increasing the number of hidden layers adding a few more hidden neurons and


Changing it to a nonlinear activation function. We get much better results now


Again, some great practice. Let's replicate this neural network in TensorFlow code. So we'll come back to our notebook and


Time to replicate the multi layer neural network from


TensorFlow playground in code


And we'll set the random seed is going to be tf random dot set seed


42 and


What do we have to do for our model?


well, we had two hidden layers with four hidden neurons each and then we had an output and


We have a relu activation function a learning rate of zero point zero


Zero one. All right, that seems pretty straightforward. So create the model


What number are we up to? I believe up to model six equals TF carers sequential


Beautiful now we need two hidden layers here TF carers layers dense with how many hidden units four or six?


four and then activation


equals relu


Wonderful. I'll just come to the end of this and then we'll create another one TF carers layers dense


forgot the essay


for hidden units and


activation equals relu


Wonderful. Now we'll go step two compile the model


Model six dot compile just exactly the same as we've done before


Again getting lots of practice here loss can be we'll use a string notation for for this model


cross entropy binary cross entropy that is


optimizer equals TF carers


dot optimizers dot atom and the LR is zero point zero zero one which is


Adams default learning rate so we don't actually have to put that one there and then finally we're going to set the metrics metrics equals


accuracy and


let's


fit the model


So we're saving our results to the history variable


Model six dot fit on


XY just the same number of epochs that we fit before. All right. Well, let's see what happens here


All right, do we get any improvements Oh


Looks like our models performing even worse than guessing


So below what if we evaluate our model far out? What's the difference here?


evaluate the model


model six dot


evaluate and


We'll go X and Y


Hmm, why is our model performing far worse than what we like this model trained our 2000 epochs


Maybe we need to train our model for longer. What if we did that?


What if we come up here make it 250 epochs? Let's try that


Oh, whoa, our metrics are jumping all over the place


So it definitely seems like our model is still basically guessing


What if we added you know what? I might think it think it might be


You can't see it in this neural network playground, but I think


It doesn't make sense the fact that we're dealing with two or features and we're dealing with a binary classification problem and


Our the last layer has four hidden neurons instead of the same or instead of one for binary classification


so let's see what happens if we just


TF carers layers dense because we want our output to be one or the other right not four different options


We want it to be red dot or blue dot not four. So let's see what happens here accuracy


Still 50%


Wow, even after 200 or close to 250 it's about to be 250 epochs are still getting 50%


Hmm. What if we evaluate that?


What's going on? What should we do when we're not sure what our?


Model is doing based on the evaluation metrics we should


Visualize visualize visualize so how do our model?


Predictions look


So remember we've got our handy plot decision boundary function. I'm going to pass in model 6 X and Y


What type of decision boundary is our model creating? Oh my goodness


All right, so it's starting to realize that red might be towards the outside


But it's still operating with it looks like straight lines


Hmm what gives


Our model looks like it's the exact one in the tensorflow playground, too. I


mean ideally


Our yellow line would go inside or in between the red and blue circle


All right. Let's model this circle once and for all well. We're going to build one more model I


Promise, but actually we're going to build plenty more models throughout the course, but for this circle


I mean we've done enough times. It's time to reveal the missing piece here. It is if we come back to our keynote


We've looked at improving our model


We've added we've altered the activation functions in the hidden layers


But we haven't changed the activation function in the output layer


So we've set up relu relu here now. What if we came back to our we looked at this right at the start


I mean we could have come back to this to begin with


But I wanted to go through the concept of figuring things out. Let's go back to our architecture of a classification model


the typical architecture that is


So if we have a look what are we dealing with we're dealing with binary classification. We've got hidden layers


Well, we've got two at the moment. We've got neurons per hidden layer generally 10 to 100, but we've seen on tensorflow playground that


Four is enough for this type of data set so we'll stick with that


Output layer shape is one we've set that up hidden activation is usually relu okay. We've set that one up


output activation


sigmoid


So in our demo model here. We also have an activation on our


Output layer, but on the current neural network that we're working with come back model six


We don't have any activation here and remember


For a dense layer the activation is by default none


Hmm, so what should we do?


If in doubt we could refer to our little table that we have here


Sigmoid or we could search something like this we could go what?


activation function to use for binary classification


Now maybe we go here


What activation function for the output layer?


So regression linear softmax


Simple sigmoid works too, but softmax works better. Okay, we could dig through this information here. We could go back for another one


Here we go the output layer contains a single neuron in order to make predictions it uses


This is binary classification. That's what we're after the sigmoid activation function all right


That must be the missing piece that this tensorflow program doesn't show us it doesn't show us the output layer


But that's all right. We've got tensorflow code


So I want to issue you another challenge


If you want to model this circle once and for all create a model like model six


But for the output layer add in a sigmoid activation function


I'll let you do your research and give that a go


But otherwise we're going to model this circle once and for all by introducing the output layer activation function


in the next video


Welcome back in the last video we discussed the