algorithm, something that's runnable by a computer because the code on there unfortunately


Biology and Physics meet Computer Science | Learning Intelligence 7

isn't yet runnable on a computer in an IDE or something like that.


Plenty of learning today from reading chapters in here to going through three major classes


in university, artificial intelligence, nanodegree.


I even have to write down points that I wanted to talk about in this video clip because today


was a productive day.


I'm not sure what I did differently.


Maybe because in the morning rather than doing fluffing around, I just sort of got straight


into study, which is a note to self.


I should really do that in the future.


So I put the Pac-Man lab on pause for the time being because I realized that project


three is due in about 11 days and from the previous two projects, it's taken me a while


to actually understand them.


It takes me a lot longer to understand the projects than what the time limit sort of


on the Udacity suggested class.


Like I think for most of them, it's two to three hours and for me, I would spend a whole


day on one single section of the problem.


So I've been catching up on the three classes that I had to finish before starting project


three and good news, I'm up to project three now.


So that's my next major goal to do.


The labs, the optional labs at the end of the two classes that I completed or three


classes that I completed today, I'll push them aside for the time being unless they're


closely related to what I'm doing in the project because of course, projects are my major focus


and I can always come back to the optional labs at a later date.


So three classes I went through today were Constraint and Satisfaction, Logic and Reasoning.


Now logic and reasoning, it's quite confusing to me because I've never actually, I've never


gone over most of these topics here.


I have used technologies that use these three methods or methodologies of artificial intelligence


but I haven't learned about them in depth before.


So logic and reasoning was like a whole nother language.


So I've done Boolean expressions and whatnot but logic and reasoning takes that one step


further.


Well, in my opinion and there's different symbols, there's an upside down A which means


all of and sometimes the A doesn't appear and you just have to assume that the A is


the upside down A is there.


There's an upside down E and all this stuff, there's so much going on in logic and reasoning


but it does make sense.


So I don't fully understand it but the whole concept of it is starting to make sense to


me because once you develop, it's like once you get better at any skill, right?


You sort of, you develop a language in that and once you develop the language of and the


language and understanding of logic and reasoning, you can communicate better.


The way logic and reasoning is designed is so that it can be easily implemented into


computer programs and it can be easily shared among other people.


That's my understanding of it so far and then planning was the final class that I did today


which is a branch off of logic and reasoning.


These three techniques can strength satisfaction, logic and reasoning and planning.


What are some use cases for these three techniques?


Well if you imagine anything to do that has large scale planning and requires multiple


constraints say for example, if you're IBM and you're designing the most efficient computer


chip possible and you want to make sure that this wire goes underneath this wire and this


transistor is separate to this transistor, a human being could play around with that


chip design for endless hours on end and may not find the most efficient way and they may


eventually do it but imagine if you could get a computer to design a computer and essentially


that's what I've gathered is some of the use cases of this is one is designing computer


chips, the most efficient layout of the computer chip to make it faster so that you have optimal


power consumption, optimal power efficiency, optimal everything essentially and this not


only happens in computer chips, it happens in planning warehouses, planning schedules


and now what did I read about in here?


A cool thing, I'm really fascinated about genetic algorithms actually.


I think the reason why they're so appealing is because it's like that confirmation bias


almost or some sort of cognitive bias.


I've seen this before, I've seen these algorithms before somewhere.


That's how I evolved, that's how we evolved, maybe not entirely.


The algorithms aren't as good as natural selection yet I don't think or as good as evolution


yet.


But it's cool stuff.


One example, NASA used genetic algorithms to evolve the best antenna shape and I'll


put a picture here of something of what it looks like but it's not what you would think.


If you look at normal antennas, they're shaped pretty logically in my opinion.


They've got the major part and then that's the antennas I see like the ones you have


on the side of your house for TV but NASA, this one is a weird shape and apparently the


computer algorithm, the genetic algorithm decided that that or discovered that that


was the best shape for a space station antenna and it's been done more than once, several


times.


So I think that's enough for this week's video.


Thank you so much for watching.


If you have anything you want to see at all in a future video, leave a comment below or


hit me up on Twitter or something like that.


All my links will be in the description.


Otherwise, I've got to go run and it's burger night at my place so burgers for the boys.


A Lesson In Movement | Raw Training Australia

Woohoo! Empty gym!


Training at Raw Training Australia today with my mate Jay. I know where he is but we'll


see what he's got in store for me.


What are we training today brother? What are we training? I'm keen.


Bit of push pull, some jerk action, some weighted chins.


He's gonna teach me some things. I'm terrible with these type of exercises but I'm keen


to learn.


I'll get you good.


It's not too hard is it?


No.


I'm kidding.


What have we got here?


Let's start off with some mobility. Tabletop, band lat stretch, band tri stretch, some circulation


and movement, skip five minutes.


Can you skip?


I don't know if I can skip for five minutes. Nah, I can. I'll give it a go. I'll give


it a go.


Skin the cat, back the bridge, five, strength, split jerk. Oh, I'm keen. Overhead press,


weighted chins and then handstand work, five minutes. I'm keen brother, I'm keen. Check


out what my boy just gave me. I'm part of the crew now. Raw Training Australia.


That's it. If you're in Brunswick, come check this shit out.


See, I can do the normal one go.


The more you have to think about skipping, the harder it is. Just don't think when you're


doing it. One, two, one, two baby, one, two. See, I thought about it and then I failed.


Anyone who disses skipping, diss them. Skipping is fun and good for you.


What's this karate move you got here?


This is a move starting from some animal flow workout. I find I use it to open up my thoracic.


You're pumping yourself up, dropping the hand over, twisting and then putting all the pressure


and weight onto the arm that's positioned into the ground. You feel it really weapons


up that thoracic, pulling back down.


What is your thoracic?


They're all the muscles in your spinal cord, I guess, from just below your shoulder blades


up to the bottom of your neck.


Shoulder blade to the bottom of your neck.


A lot of the time they'll get quite tight because of the workout we do on the computers,


driving our cars.


Hunched over.


The lack of mobility and moving that we do in this today.


That's it, twist it right over. You feel how you can lock that shoulder in and then stretch


out that lower back just a season. Don't do what I did.


You want to have your toes, I prefer to have my toes pointed out maybe 10 o'clock to 2


o'clock. What you're going to do is you're going to pretend you're dipping your balls


in honey. Okay, so you're going to keep looking inside on, you're pushing your bum back, pushing


your knees out, dropping down a small amount. We're going to launch up to our toes, jumping,


pushing the weight up. The goal is to then land in a lunge position with your hand overhead.


The idea is you want to get under the bar as quick as you can. It's not necessarily


about throwing the weight and being like, yeah, fucking check it out. I'll do that all


the way from here. That being fast and getting under the bar as quick as possible. From here,


a little pop. See how I actually, I didn't go up, I launched, I went underneath the bar.


You're hopping underneath the weight. See the front rack, bum back, dipping the balls


in honey. You're going to jump up just before you land down. You go from here. Okay. See


there's not even a great deal of pushing the weight up but necessarily popping underneath


the weight. How do you come back? Drop it. See how you fell forward into the front of


your toes, a very small amount there? Yeah. You want to make sure all the weight's in


the heels so that you can use those hamstrings. It all comes from your glutes. A little more


jump. Well, let's go to a bar now. This is good. What is this called? Split jerk. Split


jerk. It's fun being bad at something. I've never done this before. You tell me. You got


the master here so I get in the class. I started on the dowel, just a wooden bar aka the broomstick.


Best piece of mobility equipment you can buy in my opinion. I'll agree. We're moving to


the bar now. Yep. Let's chuck you over here on the bar. Step underneath, step forward.


Pushing the hips back. That's it. See how your knees bent first? Yeah. The first initial


part we want to do, pushing your bum back. Hips first. Get those hamstrings engaged.


Power preparing the booty for action. Good. The other key thing when you're launching


forward, the pull power comes from the bum and the hamstrings. We've gone like this.


You have to reach extension before you jump underneath. So reach, push first. When you're


doing the clean, it comes off the hips and the thigh. When you're doing the jerk, you


want to drop down, hit the extension and push first. You have to stand up straight as you're


launching the weight up. Okay. Head up. Lunge that front foot forward. There you go. See


how nice and solid it felt? Yeah. So because I'm a complete noob at this, I'm going to


stay on the 20 kilo bar and just get the technique down pat. This is the first time I've done


this exercise. If you're starting out with a new exercise, you don't have to throw heaps


away on the bar. I could probably overhead press 60 plus kilos, but with this exercise,


I've never done it before. So I'm going to do at least three to four sets of just using


the bar and then maybe I'll add some weight later.


So we've got exercise two and three. We've got overhead press, eight sets of six reps,


supersetted with widened tune ups, eight sets of six reps.


So I'm going to do the first set of six reps.


5 Beginner Friendly Steps to Learn Machine Learning

Hello everyone, machine learning engineer Daniel Burke here.


Now someone sent me an email the other day saying I've learned some Python and I'd like to learn


machine learning but I'm not sure on where to go to next. It was a good question right, I had the


same question about two years ago when I first started learning machine learning. So I replied


to the email with some steps, the steps we're going to go through in this video but don't worry


we're going to start from scratch in this video. So even if you haven't learned Python, if you're


a complete beginner, steps in this video will be for you but even if you do have some experience


with machine learning keep watching because you'll probably still gain some value out of this video


and if there's anything that I've missed you can leave a comment below to help others.


Before we get into specifics let me explain to you my style of learning. Now I'm a very practical


person right which means I like to get hands-on before I'm to learn anything well and now machine


learning is one of those fields where you don't need to know the theory like the the probability


statistics and mathematics really in depth until you do. So if you want to get started learning


I have a bias towards learning code first because it's a practical approach right. You can write code,


you can see it running, you can learn major machine learning concepts as you're building them


and then when you hit a roadblock then that's when you can go back to the theory and go okay


I need to understand this the background behind this code to go further but to start off with


I always encourage people to use code first because it's practical you can learn the major


machine learning concepts as you go. So that's what these steps will be dedicated towards


learning machine learning with python code. Now for all the resources I mention in this video


there'll be links in the description so be sure to check it out there don't worry too much if you


don't pay attention through it everything will be below. Step one learn python, data science tools


and machine learning concepts. Spend a few months learning foundation python right just general


python because that's the code language that you're going to be using for machine learning


and at the same time start to look into different machine learning concepts because as you're


learning python code it'll be good to be aware of right so you're learning it in this in the space


of understanding machine learning. Don't worry too much if you're not implementing machine


learning at the start what you're trying to do is just get get used to typing python code. Now


while you're learning the code it'd be great if at the same time right you're doing all all these


three steps at once machine learning concepts, python and data science tools. Now the only two


tools you need to be aware of are jupyter notebooks and anaconda to begin with. Now where can you


learn this? Machine learning concepts you can use the elements of AI course right it's a free course


and go through an overview of all the the major concepts in machine learning and artificial


intelligence. For python code you could use the Coursera python for everybody specialization


or the learn python video on free code cams youtube and for jupyter and anaconda Corey Schaefer has


some amazing tutorials that you can go through and watch them an overview of each in in one video.


Step two learn data manipulation analysis and visualization with pandas matplotlib and numpy.


All right so once you've got some fundamental skills with python you'll be able to start to


use specialized data science and machine learning libraries and the three foundation ones to


manipulate and work with data because after all machine learning is getting insights out of data


are pandas numpy and matplotlib. Pandas is what you use to work with with structured data imagine


like an excel spreadsheet right rows and columns all filled with information pandas will help you


manipulate that change its change its style get rid of data that you don't want fill data that you


you might be missing etc etc numpy stands for numerical python it'll help you do numerical


operations on those rows and columns if you need to and matplotlib stands for math plotting library


now a big table of information right with all the numbers through it that's kind of hard for


humans to understand but we definitely understand a line going through a graph so you can look at


it and see see things visually we're very visual people visual creatures anyway that's that's


biology learning these three together will help set up a foundation for what's to come where can


you learn these well Coursera's data science no applied data science specialization with python


is a great start you could do the first three parts of the specialization to learn these three things


otherwise there's a great pandas tutorial by code basics for numpy you could use free codecamps


video on numpy and for matplotlib sentdex has a great series going through all the most useful


functions of matplotlib step three learn machine learning with scikit-learn finally why did it take


up to step three to start actually doing some machine learning because it's important to get


the fundamentals right if you have a good foundation i'm all about getting into things as


soon as possible but for something like machine learning you really do need a good set of


foundations to build upon that's why we went through the first two steps first now scikit-learn


is a great library it's done in python as well that's why we're learning python which has some


incredibly useful machine learning algorithms built in now what your focus will be with scikit-learn


is not to learn the algorithms from scratch it will be to learn how to apply the pre-built


versions of them to different kinds of machine learning problems so if you have a classification


problem what algorithm in scikit-learn should you apply to it if you have a regression problem


what algorithm that's already been built for you should you apply to it that's your goal with this


step learn how to apply pre-built machine learning algorithms to different problems now to learn


scikit-learn data school has a great playlist on youtube step four learn deep learning and neural


networks okay it's time to get deep deep learning that is no i'm kidding so deep learning works best


with unstructured data now remember that excel file we talked about before which is what you


use pandas for which is all like rows and columns everything's nice and neat you might have some


label columns at the top some rows oh you know this column's the house price you know this column


means bathrooms etc etc well deep learning works on the type of data that you may not have everything


in a in a beautiful row and column set up like images like videos like audio where there's some


structure right like pixel values and whatnot but it it's it varies pretty quickly right from frame


to frame this video has different different sets of data a different picture can be very different


from another one audio sequence can be a different language and a different person speaking so you can


imagine all the different types of structure so that's what deep learning and neural networks are


good for they're kind of called representation learning or feature learning what a neural network


is really good at is breaking something down into lots of small little pieces of data and then


combining it back up and finding the patterns in that so to learn deep learning and neural networks


you'll want to look at Andrew Ong's deep learning.ai specialization or the fast.ai curriculum


step five extracurriculum and books along the way as you go through the steps we've mentioned before


to solidify your own knowledge and to practice your communication skills it would be great to


share what you've learned through your own projects you could do this through the form of a blog post


on your own blog or medium and by sharing your code on github now an example of this was was for


me when i wanted to learn exploratory data analysis i wrote a blog post about it i wrote an article


an in-depth article that went through a project of me entering my first Kaggle competition the


Titanic Kaggle competition and wrote about each of the steps in exploratory data analysis right


so going through the data figuring out how to how to split it into train and test set then modeling


it then submitting code to github and sorry submitting code to Kaggle but i also linked in


that post my code on github and a video walking through the steps and it really helped me to


understand the steps in exploratory data analysis so if you want to understand something if you want


to learn to communicate something right it's one thing to have have it all private in your own


your own thoughts but until you can really explain it or share it with someone else that's gonna that's


gonna help help you understand something so that's why i've put in here as extra curriculum to really


solidify your knowledge practice using it and if you're after books to go along with the steps


above i've i've listed some of my favorites in a in a previous video but the two standouts that


probably align most to what we've talked about uh i think i've got it right here conveniently placed


book python for data analysis by wes mckinney this will help most with with step two right this


is using pandas and this is by uh one of the authors of pandas so you know it's a great book


the other one is i don't have it a physical copy i have it on kindle is hands-on machine learning


with scikit-learn and tensorflow by aurelion garron now beware at the time of recording this i believe


the second edition is on its way so be sure to check it out before you you buy it or or pre-order


it or something like that the third book is the mechanics of machine learning which as the time


of this video is also in progress but it's by terence pa and jeremy howard jeremy is the uh the


founder of fast.ai which is one of the courses you could use to use learn deep learning it's a great


book it's very hands-on which is which is what i like where can i learn all these skills well


as i've mentioned i've listed a couple of resources or we spoke about a couple of resources through


the video all the links will be in the description but of course there's plenty more if you do have


one you'd like to share leave a comment below so other people can see it um i've also put everything


we've talked about in a structured blog post so you have it all in one place you can bookmark that


and you can use that for later otherwise if you're looking for like a one-stop shop you can get


hands-on straight away i'm a big fan of data camp they have interactive code code modules you can


go through step by step it works for some people doesn't work for others but that's kind of the


best one-stop shop that i can think of how long should learning all of this take well the beautiful


thing is that because it's all online you can set your own pace which is why it's important i think


to to lay out a structure right you could teach it you could treat it sorry you could treat it as


your own university degree or your own two-year self-apprenticeship to learn machine learning


so what that what that would mean is take about six months per step or again because it's online


you could go faster if you wanted to but remember learning anything new especially machine learning


takes time right so be patient with your results day to day it'll probably seem like you're not


learning anything you're not even retaining it you might even think i'm going backwards


like day-to-day progress might not be much but don't compare yourself day-to-day compare yourself


year on year or in six month blocks something like that remember learning anything new takes


time what about statistics what about probability what about math now you will learn some of these


along the way as you go get your code running first when you hit a roadblock that your foundation


skills right of learning code first are preventing you from moving forward then dive in to the to


the fundamentals of what you need the math probability statistics that that will push you


forward trying to learn all of the math statistics and probability of machine learning before you


start getting into to to building machine learning projects is like trying to boil the ocean what


about certifications now certifications are nice but you're not after them all right some workplaces


require certifications if that's the case look into that you might need to take care of that


but otherwise if they don't share your certify your knowledge right certify your skills through


your own projects which is step five remember extracurriculum and that's it all right if you're


serious about learning machine learning you can treat the steps that we've gone through in this


video as a as a rough outline and don't forget there's an article with with them all in one place


so you can quickly go through them if you need to chop and change some of them you're like oh i've


done this step one and it doesn't really suit me to go to step two that's fine i've kind of put them


together just as a as a loose outline something that i would would have liked to have had when i


first started learning machine learning if you have any questions or think i've missed out on


something leave a comment below so that other people could see as always keep learning keep


creating we'll see you in the next video step one learn python data science to five four three two


Full body workout while my machine learning model trains (join in)

Can you hear the fans on this thing?


It's pumping.


I'm training two models at the moment.


We've got one here on CoLab, one on JupiterLab.


What I figured is that it's day 11 for Reps for Rona, y'all thought, and it's also Friday.


You know what that means?


So while my model's training, my body has to be moving.


While my model is moving, my body has to be moving.


So that's what we're going to do today.


We're going to be training.


I'm going to monitor this training because I've got a couple of experiments running,


and we also need to do Reps for Rona day 11, of course.


We need to get our bodies moving, so let's do that.


We've got something special, a nice surprise.


Other than monitoring the model training, have a look at this.


Look at this.


What's going on here?


Look how many iterations.


I'm going this for 20,000.


We're almost at halfway, well, 5,000 iterations.


Look at this lost curve.


See, the ideal position for that lost curve is to go down, which slowly it's trending


down, and I want this one to keep going up like it is.


There we go.


So that's average precision.


This is part of my Airbnb series.


As long as these two curves go down and that one goes up, average precision, we are good.


So while my model is making gains, let's make some bodily gains.


We have... We're going to mix it up today because, as I said, it's Friday, and you know


what that means.


So let's do it, but I'm going to keep the mix up a little bit of a surprise.


See if you can guess it as we go through.


And because it is day 11 of reps for Rona, the first video, we did a nice and simple


body weight workout.


What we're going to do is replicate that.


We're going back to basics.


That can be the theme for today, back to basics.


Sometimes you try new things.


Other times, you get back to basics.


Let's do it.


I've been sitting down for way too long today.


I'm excited to move.


We've got push-ups, kick sits, mountain climbers, lunges, body weight squats.


Nice and simple.


Let's get a pump.


Starting your time in our company.


Get ready.


I'm ready.


We've got push-ups first.


Remember, we just go for the 30 seconds.


We keep moving.


We're doing reps for Rona.


Keep going, push-ups.


Let's go.


Oh, yeah.


Yep.


Keep going.


Rest.


That felt like about a million.


Fire out.


All right, push-ups.


Dismount.


Push-ups.


One set.


We've got kick sits, but next, I've got a ghost behind my door.


How's the model going?


You know, I think it can get about 100 iterations per minute on the... I'm running on a Tesla


P4 GPU.


I believe Colab is using the same GPU as my Google Cloud platform.


Colab is free, Google Cloud is paid for.


We've got kick sits.


Exercise.


Down on the ground, my hips are going... oh, yeah.


I thought so.


My hips are nice and tight.


So someone left a comment on a previous video, believed it was Hamad. Shout out to Hamad.


Asked why does one of your hands come off if you're doing this movement? And


that's if you have tight hips or lower back. So my hands used to, one of them


used to come off the ground while doing this exercise. And so after holding in


this position for a long enough time, your hips and lower back start to loosen


out. You can also do hold a lunge position. What happens when you sit


down for too long, like me, training these models, everything gets all tight.


And so you need to counteract it with movement. With movements that


stretch out your hips, movements that stretch out your glutes, movements that


stretch out your lower back. Now we have mountain climbers. These are also


great. So in a plank position, one leg up, back, up, back. That's it. We're going


just nice and calm for this first iteration. We treat the first round as


our warm-up round. That's it. Keep going. Up. Beautiful. That's it. Right up. Keep it


going. Nice. And so I heard some news that the US, I've tried to stay away from


watching too much news about coronavirus and that sort of stuff, like little bits


and pieces, because frankly it takes up too much of your headspace. We discussed


in previous reps for Rona Day that fear is the mind killer. And I saw that Trump,


President Trump said that the US wants to be open for business on April 15th,


which is Easter. And I will come back to that in a second. We have lunges. So


let's go. We're going to alternate right, left. How's the model doing? Forgot to


check that. See this is what you have to do when you're a doctor of


machine learning. You have to figure out what to fill your time with


whilst your machine learning models are training. Movement is one of the best


options of course. Wonderful. So where are we at? Oh we're at nearly at 5,000


iterations. It's just saved a checkpoint. So come over here. We've got, see there,


saving checkpoint. So every 5,000 iterations we save a checkpoint. Curve,


lost curve, still going down. Average precision, still going up. Tick tick. One


of these models I'm training the Tektron 2 from scratch and the other one I'm


using transfer learning. Now we have bodyweight squats. I completely forgot to


talk about President Trump wanting to open the US back up on April 15th, but


that's going to be, it sounds like a fairly optimistic date. According to what?


Keep going with the bodyweight squats. Don't just listen to me. You got to move.


Let's go. Keep going. Feeling good. That feels so much better. So much better to


get up and start moving. So sounds like a fairly optimistic date. Easter, but I


think it's a, it's a good idea right? You'll be, a lot of experts will be


saying, oh no you shouldn't have that date. You should, it's gonna, it's gonna


peak and all that stuff, but have you seen what people are capable of when you give


them a deadline? I wouldn't, I wouldn't like, I would hate deadlines more if they


didn't work. So now we are back. Push-ups. Let's go. 30 seconds. We're nice and slow


through this set because we went pretty fast on the first one and that felt like


we did about a million. Come on. Hold it. Up, down, up, down, up, down. Hold. Up, down, up,


down, up, down, up. Press. Wonderful. Come back over here. You know I've got this set up to


evaluate itself every 2,000 iterations. As you can hear my laptop is going. Let's


pop back over to Jupiter notebook. Oh there we go. So this one has an average


precision of 23.7 and this one, my other one, is not even past one yet. So training


from scratch is not going as well as transfer learning. We have kinksits


again. Come on, let's hit the deck. Might go a little bit faster this time. So come through,


slide your foot through. That's it. Keep it up. Well done. Let's keep moving.


That's it. Really getting a sweat up now. Especially wearing this hoodie. You know


usually I do these in the morning but I actually did some concreting today. So


with my little brother, my little brothers. Did some physical labor and I found out


how weak my hands are. Have a look at that. See that's a bruise there. I've got


two concrete pillars and jammed my finger in there. You know what? I actually


enjoyed that. Not the pain. That sucked a little bit but we are back to mountain


climbers. But getting outside and using my hands. You know they've gotten weak


from being inside too much using a computer. Now here we're swapping. Let's


go left and right. Right, left, right, left. That's it. Swap. Keep it up. Let's go.


We're doing restaurant. Our models are training. Our models are moving. Our bodies


are moving. You know coming back to that deadline. Sometimes you have to go


against the opinion of experts. I mean imagine how many business deals Trump


has been in where experts are saying one thing and he's had to go. Nope. We're


gonna have to do this. Now I'm not saying that everything will be fixed by April


15 but I think it's good to put in the minds of people go oh I have a


date to work towards. Let's go lunges. We'll go backwards this time. Forward,


backwards, forward, backwards. That's it. We'll go right, left. I need to get the


opposite arm forward. Keep moving. How many times have you had something where


you had a deadline and you thought you couldn't make it but then miraculously


I believe it's Parkinson's law or something like that. Miraculously you


come to it and you everything sort of falls into place. Look at this project


I'm working on. My Airbnb project. It's day 32 out of 42. I think it's 33 actually


so I need to fix this up. We're up to week 5. That's what you see running over


here. Oh I believe it might be bodyweight squats.


I'll squat here and sometimes you'll be working on projects that are just


open-ended and they just get too big, too long and it just gets filled with too


much stuff. The fact that I set this up as a six-week project this means I


can't say yes to everything. Keep moving by the way. I want to keep doing these


squats. No sitting around just watching these videos in bed. Rest. Wonderful and


now for the surprise. Oh one last check of the model. Total loss is still going


down. Oh we're nearly at point five. We're doing two rounds today. Remember


how I said it's Friday and you know what that means? We're going for 20 minutes


today. Usually we go for 10 minutes over the past 10 days but we're mixing it up.


We're going back to basics but then adding in a little bit of sugar and


spice. So what are we back to? We're back to the start. Push-ups. Let's go fast


again this time. How many are you gonna get?


10 yeah


20. That was like 25 or something. Really getting the sweats up now. That feels good. But yeah I was outside


today. Surprise surprise. Moving around concrete.


Wheelbarrows full of concrete. That heavy ass. You sort of get there and you got


these, got this like metal grate where all the concrete goes and it's like held


up by these plastic lids and you get there and you're pushing the concrete and you sort of


push it forward. Whole thing nearly takes you over. We got back to kick sits.


Let's go nice and slow this round. We go fast slow. That's it. Just focus on


the movement. Kick through. Check your machine learning model. Beautiful. And then


we check again. Not much progress. Come back through. Wonderful. Keep it up. Push


through. Now remember we're not trying to really really overexert ourselves so if


at any point this is too much take a breather. Chill out. Extend your rest from


30 seconds to a minute or something like that. Catch your breath. You know I know


this isn't going to change very much but there's something in me that's just like


oh better check that. Oh it's gone up a hundred iterations. Better do something else.


Oh it's gone up a hundred iterations. Mountain climbers. Here we go. Up, back.


We're going nice and fast this time. Nice and fast. Keep it up. That's it.


Techniques gone out the window.


Getting carpet burn on the soles of my feet. That's some good movement. That's some real


good movement. Under here. Under here is just a layer of sweat. That's what we want.


We want to get that sweat because that's when it starts to feel good. Okay all right.


We should hit an evaluation epoch soon. Nearly at 15,000 on GCP. Lunges baby.


Let's go. We're going forward again. 20 minutes. Model moving. Body moving. Simple.


What do you do while your machine learning models are training? Leave a


comment below. That way we can collectively help each other while the


epochs are running. We can find ways to occupy our time. But what are you doing


right now? I heard you know I heard as well. I see I try to avoid the news as


much as possible but you get you end up you end up hearing things inevitably.


The entirety of India. Shut down. Someone's here and the guard dogs are doing their job.


But that's that's a lot of people. 1.4 billion or 1.3 billion completely


locked down. Bodyweight squats you know the drill. Let's go. Nice and fast.


Prison style. Hands behind your head. Down. Up. Up. Up. Let's go. 30 seconds on the


clock. Probably a little bit less now. Keep moving. This is a surprise attack.


We're in uncharted waters. We just did a round of uncharted waters. Here we go.


We've done. We've actually been through everything three sets. Look at that. And


by the time this next one's over we should be up to another evaluation.


Oh no. Maybe not. I only said it every 5,000 I think. Or maybe no every 2,000.


That's correct. What are we back to? Push ups. We got nice and slow this time. Down.


Hold for two. One. Two. Up. Down for two. One. Two. Up. Hold. Down for two. One.


Two. Up. Hold. Down for two. One. Two. Up. Hold. One. Two. Down. Hold. Jump forward. Look at that.


Officially in the last loop. Oh did you see that? Check that out. Dark mode.


There are little things in life. Dark mode has just automatically activated. See?


Friday 6 o 2 p.m. You know I told you it's Friday and you know what that


means. Let's go. Kick sits. Boom. Nice and fast. Keep it up. Shoot through. If your


form's going to too much shit, slow down. Like mine, it's going to shit. So I'm


gonna slow down. Come back. Kick through. That's it. I'm not trying to buckle myself.


Just trying to move.


Wonderful. BEA. Beautiful. You know I've got a rule as well. I try to every day


before I eat. Someone, actually a family friend called me up and asked how do I


lose weight. I've got a rule. I try every day before I eat at all. I do some sort of


movement. Whatever that may be. Whether it's a session like this. I've already


eaten today. Or some kind of maybe a long walk. Let's go. Mountain climbers.


Boom. Up. Nice and slow this set. Swap. Right. Left. Right. Left. That's it. Keep it up.


Watching you. Come on. Push it through. Wonder how my model's doing. Are we up to


the evaluation loop yet? Let's check it out. That's what we have to look forward to


while we're doing our reps for Rona. This is a really important point. There we go.


See that inference happening? So what I've done is I've got a whole data set


of 35,000 images to replicate Airbnb's amenity detection. Of course I don't


have access to their public images but I'm using 35,000 open images. But I'm


doing experiments, these experiments here, on a 10% subset of that data set.


So now we are up to lunges. Let's do it. Come through. The inference wasn't


done just yet. So we'll check the results. See how much it improved. Keep it up.


That's it. Nice. Let's go lunges. Come on. Mix it up this time. Come on back. And now


forward. And now back. I'm even confusing myself here. Where do my arms go?


Alrighty. There we go. We've got some average precision metrics. Come and look at that.


3.6. So we wait for weights and biases. If you're not using weights and biases


to track your... it's like tracking your workouts. One session you might do a


certain amount of weight and it felt great so you need to track that. So the


next one you can work on that. Look at that. From in the last 2,000 iterations,


look at that line. Look at that spike. Oh, we're back on. We're working. But the spike,


we just went up average precision threefold. And now lost curves are both


going down. Good. This will probably run for another few more hours. I mean we


could keep moving all night if you want to. Keep dancing. Let's go. Last set, best set.


Look at that. Come here. We're going to wait for that timer to go out. This is what we've


done. This is the Tabata app on the App Store. I've had a lot of people asking that. It's just


T-A-B-A-T-A on the App Store. It's fairly... it does what it needs to do. If I was going to build


a time wrap, it'd probably have a few more usability fixes but that does what it needs


to do. It doesn't let perfection get in the way of progress. So where do we finish up?


Look at that. Victory. Is it going to tell us what we did? Done. Great workout. You spent 20


minutes, seven seconds exercising. Have a go at that. You should be proud of yourself if you've


been moving. Now we had a little bit of modeling and a little bit of moving. How was that for day


11 reps for Rhona? Maybe this is a series going forward. Every time I train a machine learning


model, we monitor the training progress whilst doing some sort of movement session.


And so what did we do? We did push-ups, kick sits, mountain climbers, lunges, bodyweight squats,


four times through, 30 seconds on, 30 seconds off. One time we'll do it fast and another time


we'll do it slow. So thank you all for tuning in. Stay tuned for an update video when I figure out


which way I'm going to go. Am I going to train a model from scratch or am I going to use transfer


learning? I haven't quite figured that out yet. We're at the tail end of week five. I would have


liked to have had modeling finished by now, but the good news is that even if the project doesn't


go through, we can still keep moving. So wipe that sweat off your nose. Hopefully it's there.


Oh, actually don't do that. Don't touch your face. Stay indoors, keep moving, stay alert,


Text/Profanity Checker in Python

Alright, what's going on guys? Daniel Burke here, welcome back to another episode of me


Text/Profanity Checker in Python

going through some programming. Just to let you know, I'm learning how to code in Python


Text/Profanity Checker in Python

using Udacity and another of our other resources. And I'm just going to take you through one


Text/Profanity Checker in Python

of the projects I've just done in programming foundations with Python. Essentially, this


Text/Profanity Checker in Python

is a profanity editor. What does that mean? Let me just start off. Actually, this is all


Text/Profanity Checker in Python

of this that I'm doing is available on Udacity for free. It's the courses you can see it


Text/Profanity Checker in Python

there called programming foundations with Python. If you just Google Udacity programming


Text/Profanity Checker in Python

foundations with Python, you'll be able to find it there. Sign up, check it out if you


Text/Profanity Checker in Python

want if you're interested in learning Python, one of the most popular languages on the web.


Text/Profanity Checker in Python

But enough from that. And just remember, I'm no expert, I'm learning as well. I'm making


Text/Profanity Checker in Python

these videos to practice and cement my learning. So what was the goal of this? This project


Text/Profanity Checker in Python

through Udacity. It was essentially take a piece of text like this here, it could be


Text/Profanity Checker in Python

any text that you want. It could be something as simple as Hello, my name is Daniel. Right,


Text/Profanity Checker in Python

and it's the goal was to scan the text using Python, and then determine whether there was


Text/Profanity Checker in Python

any profanities within that text. And now what are profanities? Well, swear words, essentially,


Text/Profanity Checker in Python

you know what they are, you can, you can Google them or, or if you've watched YouTube for


Text/Profanity Checker in Python

long enough, I'm sure you'd be able to find them. And so here's my code here, from the,


Text/Profanity Checker in Python

I had a lot of help from the Udacity videos. But essentially, what I'm going to do is I'll


Text/Profanity Checker in Python

show you an example of me scanning through this particular text here is just a whole


Text/Profanity Checker in Python

bunch of movie quotes, and then show you what what my code outputs. And then I'll go through


Text/Profanity Checker in Python

each of these things here step by step. So I can submit my knowledge. And hopefully you


Text/Profanity Checker in Python

guys can learn something too. So let's run this. We go run. Beautiful. So bring over


Text/Profanity Checker in Python

the results here in the shell. So this document has no curse words, right? So it's saying


Text/Profanity Checker in Python

that this piece of text, this group of text here has no curse words. And that you can


Text/Profanity Checker in Python

see here, this document has no curse words that has printed out to here. So how does


Text/Profanity Checker in Python

this all work? Let's go right from the top. So to start with, we're importing URL lib


Text/Profanity Checker in Python

or URL lib. Now, what does that mean? Well, we're importing a library called URL lib.


Text/Profanity Checker in Python

That means if someone's coded a whole bunch of things in URL lib before, we don't have


Text/Profanity Checker in Python

to recode them in our file, we can use them and I'll refer to where we use URL lib later


Text/Profanity Checker in Python

on in the code. But what are we doing here? First of all, we're defining a function. So


Text/Profanity Checker in Python

if you break this problem down, you would have two main steps, you would have the step


Text/Profanity Checker in Python

to read the actual text in the file. And then another step will be to check whether any


Text/Profanity Checker in Python

of the text, any of the words in that text is a is a curse word or is is profanity. So


Text/Profanity Checker in Python

the first function, we're going to break it down, what function one, read text function


Text/Profanity Checker in Python

to check whether there's a parameter of profanity, sorry. So read text. So we're going to define


Text/Profanity Checker in Python

a variable here called quotes. And now we're going to use the Python keyword open. If you're


Text/Profanity Checker in Python

not sure what that is, you can Google Python keyword open and it will show you what it


Text/Profanity Checker in Python

does. Essentially, you throw in a path name for a file that exists on your computer. And


Text/Profanity Checker in Python

it will open that file. So this particular path uses slash Daniel slash desktop slash


Text/Profanity Checker in Python

movie quotes dot text is where this file lives on my computer. Now, just a quick note, if


Text/Profanity Checker in Python

you're making a text file in on Mac, and you're using text edit, the default saving type is


Text/Profanity Checker in Python

dot RF dot t r dot r t f, otherwise known as rich text file, but you can change it to


Text/Profanity Checker in Python

top dot txt by going into format. And then if if it was already RTF, this will be changed


Text/Profanity Checker in Python

to normal text file, which will give you the dot txt extension. That's I ran into that


Text/Profanity Checker in Python

that trouble doing the doing the project on Udacity on here. This code won't work properly


Text/Profanity Checker in Python

if you've got an RTF file. So make sure it's a t dot txt file. And then what do we do next?


Text/Profanity Checker in Python

So we're defining another variable here called contents of file equals quotes dot read. So


Text/Profanity Checker in Python

we've defined a variable here quotes equals opening this folder. And then we're defining


Text/Profanity Checker in Python

another variable called the context of the file is quote. So this, this folder, this


Text/Profanity Checker in Python

file that we've opened dot read, and now dot read is another function built into the Python


Text/Profanity Checker in Python

library that allows us to read the text within a file. So we've opened the file, and now


Text/Profanity Checker in Python

we're reading the file within the read text function. And then I've commented out print


Text/Profanity Checker in Python

contents of file here. But essentially, what that will do is print out the contents of


Text/Profanity Checker in Python

this file in the shell if we wanted it to, but we're not going to do that for now, we'll


Text/Profanity Checker in Python

go over, we'll go over the crux of the problem first. And so then we're going to close quotes.


Text/Profanity Checker in Python

So we're closing this file, we don't really need it, it won't actually close, but it's


Text/Profanity Checker in Python

just telling Python, get rid of that file out of memory, we don't need it anymore. And


Text/Profanity Checker in Python

so we've saved what we need in the variable contents of file. And so then we've got the


Text/Profanity Checker in Python

function we're calling check profanity at the bottom of read text, because read text


Text/Profanity Checker in Python

is the final function that we call. So we're going check, check profanity, and we're throwing


Text/Profanity Checker in Python

it, we're giving it the argument contents of file. So whatever's saved in here is going


Text/Profanity Checker in Python

to go into the function, check profanity. So the next step is defining check profanity.


Text/Profanity Checker in Python

Now we define check, check profanity, and then it's got a parameter here called text


Text/Profanity Checker in Python

to check. And now remember, up here, we've already passed a contents of file. So what


Text/Profanity Checker in Python

happens here, we're defining a variable called connection. And connection equals URL lib


Text/Profanity Checker in Python

dot URL open, then a web address. And on the end of that web address is text to check.


Text/Profanity Checker in Python

And remember, you can you can assume here that contents of file is the same as text


Text/Profanity Checker in Python

to check. So we're opening this web address with text to check on the end of it. So what


Text/Profanity Checker in Python

is this web address? Well, I've got it up here. And it's a web address that Udacity


Text/Profanity Checker in Python

have made to give a true or false reading. If you put in a profanity in this final word.


Text/Profanity Checker in Python

So if you see how this section of the web page, this first section is exactly the same


Text/Profanity Checker in Python

as this here. And then you add the text to check, which will end up on the end here.


Text/Profanity Checker in Python

Now I've got test there. And so if I refresh this page, test is not a profanity. So it


Text/Profanity Checker in Python

will come up false. But if I put in a profanity word, so let's go here. Refresh the page.


Text/Profanity Checker in Python

Oh, I did it wrong. Enter. True. Right. So the word shit is a profanity. So it returns


Text/Profanity Checker in Python

true. And then you can use your wild imagination here and type in any Oh, you can see what


Text/Profanity Checker in Python

I've I've tried here in my history. There we go. Damn is not a profanity according to


Text/Profanity Checker in Python

this app. So what could we use a profanity checker for? Well, if you're sending an email


Text/Profanity Checker in Python

and you're sending it to someone sensitive, imagine if we had this text file, and we accidentally


Text/Profanity Checker in Python

typed that rather than the actual quote, the actual movie title. So this won't pick up


Text/Profanity Checker in Python

in a text in a auto auto spelling correct, because that word is spelled correctly. And


Text/Profanity Checker in Python

the O and the I keys are right next to each other. So that could easily happen. So if


Text/Profanity Checker in Python

we've got a shot in the dark, it could easily be a shit in the dark. We don't want that


Text/Profanity Checker in Python

to happen. So this is why we're making this checker. So where were we we've got we've


Text/Profanity Checker in Python

defined the variable connection we've opened and it's going to open up this website here.


Text/Profanity Checker in Python

Now this is where URL lib comes into into play. So remember at the start we imported


Text/Profanity Checker in Python

URL lib importing URL lib allows us to use this function here URL open. So URL open is


Text/Profanity Checker in Python

an instance that's a part of the class URL lib. But we'll get on to more about classes


Text/Profanity Checker in Python

and functions and whatnot down the track. I'm just going to literally run through what


Text/Profanity Checker in Python

this code does step by step. So URL open will open this web page and pass in the contents


Text/Profanity Checker in Python

of the file. So it's going to pass in literally every word that's in this text onto the end


Text/Profanity Checker in Python

of this URL here. And then we define another variable here, output equals connection dot


Text/Profanity Checker in Python

read. So what does this mean? So that means before we use the read read function up here


Text/Profanity Checker in Python

to read the quotes. And so output will make it a lot easier in our shell, rather than


Text/Profanity Checker in Python

it outputting a whole bunch of just true or false, we want it to, to output something


Text/Profanity Checker in Python

something a bit more text friendly. So as if let me just give you an example. This is


Text/Profanity Checker in Python

the output we're talking about here. So connection is going to give us the result of true or


Text/Profanity Checker in Python

false and output will read this connection. So false dot read. So now output will equal


Text/Profanity Checker in Python

false in this case, but if we type in a swear word, profanity, the output will equal true.


Text/Profanity Checker in Python

And then we close connection because we don't need it anymore. So it's not actually going


Text/Profanity Checker in Python

to close the web page, but we just close it because we're telling Python we don't actually


Text/Profanity Checker in Python

need this in working memory anymore because we have our output variable saved as true


Text/Profanity Checker in Python

or false depending on whether or not there's a profanity in these words. And so what's


Text/Profanity Checker in Python

happening next, we've got connection dot close, we've gone over that and then if true in output,


Text/Profanity Checker in Python

print profanity alert. So it's saying if this value is true, it's going to print profanity


Text/Profanity Checker in Python

alert to the to the console as an output. But if it's false print this document has


Text/Profanity Checker in Python

no curse words. Else print could not scan the document properly. So we're doing an if


Text/Profanity Checker in Python

statement here. So if true, print this if else if false print this else print this.


Text/Profanity Checker in Python

Right. So now read this text. There's no swear words in there. Spoiler alert. So now we're


Text/Profanity Checker in Python

going to run this and see what happens. This document has no curse words just as we expected.


Text/Profanity Checker in Python

So what's happening behind the scenes? Well, let's go from the top. So quotes, the variable


Text/Profanity Checker in Python

is opening this file, and then contents of file is reading quotes, the variable, it's


Text/Profanity Checker in Python

saving it to contents of file. And then we're running check profanity, which is this function


Text/Profanity Checker in Python

here on contents of file, which is what's in here. And then we're checking the text


Text/Profanity Checker in Python

in here via this webpage. So behind the scenes, this is what this is doing here is actually


Text/Profanity Checker in Python

going to this webpage, typing in all of this text, and putting it at the end of this URL.


Text/Profanity Checker in Python

So our browser is smart enough to do that for us to put all these weird symbols here


Text/Profanity Checker in Python

like a space is actually equal to percent 20 percent to zero. And so false because there's


Text/Profanity Checker in Python

no swear word in there. And then the output is reading output variable will get given


Text/Profanity Checker in Python

a true or false value depending on if there's a profanity in here. And then if true, we're


Text/Profanity Checker in Python

printing profanity alert, but if false, we're printing where to go. This document has no


Text/Profanity Checker in Python

curse words. Now let's change this one letter is a moment of truth. Let's see if it's works.


Text/Profanity Checker in Python

So now there's there's definitely a profanity in this in this piece of text here. So what


Text/Profanity Checker in Python

should it do? I'll give you a second to think about that. So if a profanity is present,


Text/Profanity Checker in Python

that means this will be true, right? Because if we, we completely delete everything off


Text/Profanity Checker in Python

here. And we go back to type in this, it's going to give true. So if true, profanity


Text/Profanity Checker in Python

alert, and that words in there. So hopefully this works because it's going to pass all


Text/Profanity Checker in Python

this text on to the end of that URL. And then the output is going to define what gets printed


Text/Profanity Checker in Python

in the Python shell. So moment of truth, save this file, save this file, run profanity alert.


Text/Profanity Checker in Python

Excellent. So that has essentially alerted us that we've got a profanity in our text.


Text/Profanity Checker in Python

And we shouldn't be really emailing that to our boss if we had to. Oh, and what's this


Text/Profanity Checker in Python

little thing at the bottom here? Well, this is read text, it's calling the function that


Text/Profanity Checker in Python

we defined up here. And remember, this is two step problem, we have to read the text


Text/Profanity Checker in Python

from here, and then decide whether there's a profanity in there and print something to


Text/Profanity Checker in Python

the to the console. And then we call read text, we only have to call one of the functions


Text/Profanity Checker in Python

because check profanity is being called in the within the read text function. And what


Text/Profanity Checker in Python

do I mean by called? Well, called is just another word for being run. So well, that's


Text/Profanity Checker in Python

that's how I define it. But that is essentially how the profanity editor works from the Udacity


Text/Profanity Checker in Python

Python foundations course. And now, don't get me wrong, I'm no expert on on Python,


Text/Profanity Checker in Python

but I'm just sharing with you what I've learned and take everything that I've said with a


Text/Profanity Checker in Python

grain of salt. If you if you're finding that what I said wasn't making sense, I would highly


Text/Profanity Checker in Python

recommend signing up to the Udacity programming foundations with Python and running through


Text/Profanity Checker in Python

the examples yourself because they do a lot better job at explaining it than I do. But


Text/Profanity Checker in Python

I'm having fun learning some Python, I'm going to continue making these videos every time


Text/Profanity Checker in Python

I learn something new. So hopefully you're getting some value out of them. If not, I'm


Text/Profanity Checker in Python

getting a lot of value out of them because it's cementing my knowledge. I couldn't program


Text/Profanity Checker in Python

anything in Python about a week ago until I started started this course. So we'll see


Text/Profanity Checker in Python

how it goes. I'm signed up to a deep learning course on Udacity as well. So I'll be making


Text/Profanity Checker in Python

videos about that too. But whatever you're doing, I hope you're having a great day. And


Text/Profanity Checker in Python

I'll if you got any questions, leave it in the comments. I'll try and help you out as


Text/Profanity Checker in Python


These books will help you learn machine learning

Reading is learning and as you'll see in this video I've put together some of my


favorite books that have helped me learn machine learning and it's by all means


not an exhaustive list right I've missed out plenty of things so if if you do


watch the video and you think of me something leave a comment below so that


someone else can benefit from it but otherwise my style of learning is code


and concepts first right so I've listed the books in this video in order of


approachability alright so I learn the code and concepts first and then fill in


the gaps with math where needed alright now you know that let's get started


number one machine learning for humans by Vishal Mani and Samir Sabri now


machine learning is broad right there's algorithms for this there's data for


this there's different ways to manipulate data for this algorithm


there's problems that you'll have collecting data etc etc we know this


right there's supervised learning unsupervised learning deep learning and


there's all different ways to work about them but somehow Vishal and Samir have


managed to compulate a beautiful collection of explanations on all of the


main topics in machine learning right right from supervised learning


unsupervised learning reinforcement learning deep learning neural networks


it's in there all within 96 pages right and you can read this on online for free


I don't have a full PDF but I've read it on medium as a series and both the PDF


article now why is it important well if you're new to machine learning as I said


it'll introduce you conceptually to some of the main topics in machine learning


but if you're already in machine learning if you're already a practitioner it will


give you an idea of a way to communicate a complex topic in a way that people can


understand right because even if you are the smartest person in the room if you


can't communicate what you know how helpful is that knowledge to someone


else number two Python for data analysis by


where's McKinney now this one I do have a physical copy of why well because it's


worth it now most of my time as a machine learning engineer is spent


manipulating data analyzing it and getting it ready for modeling and 99% of


the time I do that with pandas right so performing data analysis with pandas and


so what is this well this is Python for data analysis data wrangling with pandas


numpy and I Python which is Jupiter notebooks now but the important thing is


that it's the author is where's McKinney right who's one of the creators of pandas


which is the library I most use for manipulating and wrangling data so if


you want to get into data science or machine learning you have to know how to


manipulate data and this book will show you how okay number three so once you've


read Python for data analysis you've got comfortable wrangling data with pandas


you'll probably want to model it and how do you do that you use machine learning


algorithms to find patterns out of data and now I don't have a physical copy of


this book but I I do have it on the Kindle and you can't really even see it


there so I'm not sure why I'm holding up the Kindle I'll just put a nice photo of


it here it's hands-on machine learning with scikit-learn and tensorflow by


Aurelian Goran and now I got it on the Kindle right because I was reading it to


and from work on the train I wanted something that I could read just just go


and update myself on on the best best machine learning techniques while I was


going to work to use them the best thing about this book is that it will give you


hands-on work through problems aren't using scikit-learn and tensorflow or


scikit-learn is also called sklearn which are two of the most useful machine


learning libraries if not that two most useful machine learning libraries out


there and that's really important like for me I learn best by by seeing someone


else do something so having those examples seeing how they're done I can


go okay this is how it's done for this problem I can come across my own problem


compare the two and then adjust where it's needed so if you if you take this


book right Python for data analysis the hands-on machine learning book with


scikit-learn and tensorflow these two together made up 80% of the things that


I would do on a day-to-day basis as a machine learning engineer so they're


definitely worth your time number four grokking deep learning by Andrew Trask


now again I don't have a physical copy of this one because when I first


encountered it it wasn't fully out yet so way back when when I was doing the


deep learning nano degree by Udacity Andrew Trask was one of the guest


lecturers and he was talking about LSTM's I think or language modeling


something like that I can't remember the exact topic but what I do remember is


his way of explaining things was very akin to how I like to learn things so


using analogies like hyper parameters are like knobs on an oven which you can


adjust those sort of things right I really that's those those click in my


brain and so when I heard that he was authoring a book grokking deep learning


I jumped on it and so I bought it at the time there was only five chapters I


remember sitting on my couch reading through them right step by step and now


up until that point for neural networks I'd only ever used frameworks right but


Trask insisted that in order to understand them deeply it was best to


code them from scratch using NumPy and so I kind of disagreed right because I


found the frameworks easier again me avoiding the hard thing to do but I


followed trust lead code on them from scratch from NumPy and gained a much


better understanding than I did without so if you prefer a ground-up learning


approach for deep learning grokking deep learning by Andrew Trask is for you


wildcard time now this one doesn't have an actual number because it's not fully


out yet that's a beautiful thing about open source right is that people can


release things as they're being built so you can you can get value from them


straightaway now this one is the mechanics of machine learning by Jeremy


Howard and Terence Parr you can read it online for free but remember at the time


of recording it's not fully done yet but reason being Jeremy Howard founder of


fast AI I absorb all of his teachings to do with machine learning right I really


I really align with his style of teaching so I know it's in good hands if


it's got his name on it and Terence Parr is a professor of computer science at


the same University at where Jeremy teaches at and after briefly going


through some of the chapters on the book it's an incredible resource to learn


machine learning so check it out the mechanics of machine learning number five


the hundred page machine learning book by Andre Burkhoff now this one I do have


a physical copy why because it's the start here and continue here of machine


learning and something that you should have on your coffee table right so you


can pick it up someone can ask you about machine learning ask are the robots


really going to take over and you can say no I've read this book and I know


what machine learning actually is all right and so this is the book that I


wish that I had when I started learning machine learning I probably wouldn't


have got a lot of the concepts in here when I first started right because it is


that little bit little bit advanced and even now into it two years there are


some things that I don't understand but that's that's alright it's only a hundred


or so pages you can read it in about a day I took longer than that I did make a


full review video on this by the way so if you want more details on it check out


there I'll put that in the description it covers end-to-end the broad spectrum


of the most useful things that you should know about machine learning and


explains them in a simple easy to understand way number six now this is


the last one but by all means again this is not an exhaustive list but I'm just


sharing you some of my favorites the last one is deep learning by Ian


Goodfellow, Yoshua Bengio and Aaron Corville all right now if you haven't


heard of Ian Goodfellow or Yoshua Bengio or Aaron Corville you could pretty much


say that they're they're some of the founding figures of deep learning as a


whole and now when I first encountered this video this this this book two years


ago it scared me right because you can read it again online for free but I


started going through the pages and I read I read I saw the big paragraphs of


text I saw the the equations the math equations and I'm like now I'm sticking


to the code and concepts but then I realized in order for me to take it to


the next level I need to I've kind of learned from a top-down approach


remember but now I need to go back and fill in the gaps from a bottom-up


approach and now this is this is where this book is I plan on using this book


for now I'm most excited for the for the chapters on math right because in the


end code frameworks languages etc will change but math stays the same over the


long term and that's what I'm going for right I want knowledge over the long


term my mistake in the beginning was being scared of books that were too hard


for me when really in order to keep learning I have to remind myself Daniel


you should be reading at all times a book that is slightly too hard for you


to read because why eventually that book won't be too hard for you to read and


then you find another one that's slightly too hard to read so with that


I hope you check out at least one of these books or or if not more there'll


be there'll be links for all of them that are in the description so you can


check them out there I hope they bring you as much value as they've brought me


and remember at $30 or whatever the price of a book is or even free for some


of them even if they give you one concept that you use for a number of


years that's money well spent if you have any other books that you think I've


missed leave a comment below so that others can see and so so I can see as


well otherwise keep learning remember learning is reading so keep reading and


FALLING IS FUN | Functional Friday 4

Back at Raw, another workout, functional Friday. I'm a bit sore so today's workout is going to be


FALLING IS FUN | Functional Friday 4

all about mobility. We've been needing work on that in the past 12 months or so. So Jay, my boy,


FALLING IS FUN | Functional Friday 4

what do we got today? We got, we got, starting off with a bit of weight and mobility. So


FALLING IS FUN | Functional Friday 4

some Jefferson curls, some weighted pancakes, some windmills, get the shoulders prepped up,


FALLING IS FUN | Functional Friday 4

bulletproof them, kind of work on a bit of flow, some animal flow stuff, just to loosen everything


FALLING IS FUN | Functional Friday 4

up and prime the joints and a bit more. And then a bit of strength work, some block cleans,


FALLING IS FUN | Functional Friday 4

deadlift, broad jump, finishing it off with some parallel bar work at the end.


FALLING IS FUN | Functional Friday 4

Sounds awesome. Woo! Easy, all right. First exercise, Jefferson curl, one of the best ones


FALLING IS FUN | Functional Friday 4

to warm up the spine. Idea is to go as slow as possible, collapse, collapse each one of


FALLING IS FUN | Functional Friday 4

the vertebrae one at a time. So you'll see us doing it.


FALLING IS FUN | Functional Friday 4

So we've got the rounded pancake, weighted pancake, sorry.


FALLING IS FUN | Functional Friday 4

So how's this different to, so what's the normal pancake? You just don't have the weight there.


FALLING IS FUN | Functional Friday 4

Yeah, so you basically, you know, you can do a whole bunch of different ways where you


FALLING IS FUN | Functional Friday 4

creep down and go to the side and you creep yourself across the floor.


FALLING IS FUN | Functional Friday 4

The weight really is just good for pulling yourself down into a position.


FALLING IS FUN | Functional Friday 4

What are you working on stretching here? Abductors. Abductors? That's the one, thank you.


FALLING IS FUN | Functional Friday 4

Oh yeah, a lot of the time when you squat, you know, you'll contract immediately.


FALLING IS FUN | Functional Friday 4

So if you're fighting whenever you're squatting and you're dropping in and your knees coming in


FALLING IS FUN | Functional Friday 4

as you're trying to push out, the abductors are too tight. So you need to loosen them up


FALLING IS FUN | Functional Friday 4

so your glutes can actually fire off properly. I have that exact issue.


FALLING IS FUN | Functional Friday 4

Now we've got windmills, that stretch, that pancake, weighted pancake was one of the,


FALLING IS FUN | Functional Friday 4

I don't know, most painful stretches I've ever done. I was going white in the face a bit there,


FALLING IS FUN | Functional Friday 4

but nonetheless, I feel amazing now. What do we got now, brother? Windmills. Windmills. So the


FALLING IS FUN | Functional Friday 4

idea is to get kettlebell weight or whatever weight in your hand, lock your shoulder out,


FALLING IS FUN | Functional Friday 4

really make sure that you're engaged and get it flat. You don't want to be sitting forward and


FALLING IS FUN | Functional Friday 4

just activating the front of your delts. So you really want to get that shoulder locked in.


FALLING IS FUN | Functional Friday 4

You're going to point your toes to the side and we're just going to slide down the leg,


FALLING IS FUN | Functional Friday 4

rotating the arm through all of its motion, not all points, all the way back out,


FALLING IS FUN | Functional Friday 4

rotating the arm. What we're doing here is we're just really stabilizing the whole shoulder joint.


FALLING IS FUN | Functional Friday 4

You know, it's the most complex joint in the body and it just wreaks havoc whenever you're not


FALLING IS FUN | Functional Friday 4

looking after it on all your lifts. So now last bit of mobility before we get into the strength


FALLING IS FUN | Functional Friday 4

workout, we got a bit of animal flow for how long? We got 10 minutes of animal flow. So what would


FALLING IS FUN | Functional Friday 4

you describe animal flow as, Jay? To me, we're just going to freestyle. A little bit of capoeira,


FALLING IS FUN | Functional Friday 4

a bit of handstands, some cartwheels, move, hop down into some compromising positions and


FALLING IS FUN | Functional Friday 4

just moving, right? Just moving around. That's it. That's the good fun about it. Animal flow is,


FALLING IS FUN | Functional Friday 4

it looks simple, but I assure you it's incredibly difficult.


FALLING IS FUN | Functional Friday 4

So


FALLING IS FUN | Functional Friday 4

if you've ever seen Edo Portail before, you'd be aware of the Lizard Crawl. So that's what


FALLING IS FUN | Functional Friday 4

we've been trying to do just recently is master the Lizard Crawl and it looks a lot easier and it


FALLING IS FUN | Functional Friday 4

looks weird. We've never seen it before, but it's a phenomenal all body workout. My shoulders are


FALLING IS FUN | Functional Friday 4

on fire, my arms are on fire, my abs are on fire and this guy, Edo, if you've never seen him before,


FALLING IS FUN | Functional Friday 4

check him out. Edo, I-D-O-P-O-R-T-A-L, Portal. He does it with so much better flow than we do,


FALLING IS FUN | Functional Friday 4

but nonetheless, it's still good fun. What do we got now? We got block cleans, blocking,


FALLING IS FUN | Functional Friday 4

we're on the strength part of the workout. So block cleans, deadlift, broad jump, five rounds.


FALLING IS FUN | Functional Friday 4

Oh, gymnastic strength. Yeah. A bit of contrast training, some block cleans and then some parallel


FALLING IS FUN | Functional Friday 4

bar work. Good times. So the cleans are another exercise I've never done before


FALLING IS FUN | Functional Friday 4

and my technique is horrible because I'm sort of from a weightlifting background or bodybuilding


FALLING IS FUN | Functional Friday 4

background. I just sort of brute force the weight up every time and from what I've been told,


FALLING IS FUN | Functional Friday 4

essentially you're supposed to pick the weight up, bounce it off your thighs and then with the


FALLING IS FUN | Functional Friday 4

momentum, bring it up and catch it underneath. And I'm just essentially, I'll show you.


FALLING IS FUN | Functional Friday 4

I've just been kind of bouncing it, curling it up to my shoulders, but ideally you want to


FALLING IS FUN | Functional Friday 4

pop up and then catch and don't quote me on form or technique. Make sure you research this if you,


FALLING IS FUN | Functional Friday 4

I don't know, if you want to learn more about them, but I'm slowly learning from the master over here.


FALLING IS FUN | Functional Friday 4

You're teaching me. The student over here. The student. Yeah, we're all learning. We're all


FALLING IS FUN | Functional Friday 4

learning, but it's good fun. And as you can see, I got red marks on my shoulders already.


FALLING IS FUN | Functional Friday 4

So we got a bit distracted. Someone came to the gym and just started talking to us. It's


FALLING IS FUN | Functional Friday 4

awesome bloke, but nonetheless, we're going to skip deadlifts because we're a bit cold now


FALLING IS FUN | Functional Friday 4

and skip the broad jumps. Just go straight into the gymnastic strength training.


FALLING IS FUN | Functional Friday 4

What are we going to do on the gymnastics strength training? At the moment, I'm working from L-sit to


FALLING IS FUN | Functional Friday 4

handstands. So I'm just going to go over a few drills I've been going over. Parallel bars?


FALLING IS FUN | Functional Friday 4

Parallel bars, yep. Some handstands. Let's do it. Let's do it.


FALLING IS FUN | Functional Friday 4

So what's the goal here? L-sit into handstand.


FALLING IS FUN | Functional Friday 4

So close brother, so close. So how were you practicing to start off with? What were the


FALLING IS FUN | Functional Friday 4

movements you were doing before you could do the handstand? So I got nailed just getting up into


FALLING IS FUN | Functional Friday 4

a handstand on the parallel bars first. Yeah. So that was just, just andracing in. And then


FALLING IS FUN | Functional Friday 4

just getting comfortable so that you can hold yourself into the position. While practicing that,


FALLING IS FUN | Functional Friday 4

we were doing a little bit of L-sit variation. So, you know, started out here and bring the knees out


FALLING IS FUN | Functional Friday 4

and then bring the feet out. Then sort of mix the two movements in together. So I start here,


FALLING IS FUN | Functional Friday 4

go back here, back to the L. Just rotating back and forth. That's what I need to work on.


FALLING IS FUN | Functional Friday 4

Just that rotation.


FALLING IS FUN | Functional Friday 4

My turn.


FALLING IS FUN | Functional Friday 4

Just finished up a session at Raw Training Australia with my boy Jay.


FALLING IS FUN | Functional Friday 4

We didn't go too heavy this time, bro. No, we went a little bit light today.


FALLING IS FUN | Functional Friday 4

It's just more mobility. My core is on fire. Yeah. I'm going to be sore tomorrow.


FALLING IS FUN | Functional Friday 4

How you feeling? Fresh as. So we're going to rock and roll. I'm a bit sore from a couple of days


FALLING IS FUN | Functional Friday 4

ago, but today was a really good session just focusing on mobility. But sometimes you just


FALLING IS FUN | Functional Friday 4

got to do that. Recognize when you're sore. Recognize what you're going to train. Listen


FALLING IS FUN | Functional Friday 4

to your body. Don't go too hard. Otherwise you get injured. Being injured sucks. Am I right?


FALLING IS FUN | Functional Friday 4


The curse of dimensionality. Or is it a blessing?

Hello there!


In this video, we will explain why many dimensions can be both a blessing and a curse when doing


machine learning.


So it's Ms. Coffee Bean here with a video about some important basics in machine learning.


First we have to explain what are dimensions and why do we need them in machine learning.


The number of dimensions describes the number of coordinates we have to give in order to


fully localize a point.


For example, we have one dimension if we only need one coordinate to find the point, like


this point on this line.


For two dimensions, we already need two numbers to localize one point.


Three dimensions are very normal to us because space around us is three-dimensional and to


localize an object we have to specify three points.


But the spatial dimensionality in particular does not limit dimensionality in general.


Dimensions can be anything, especially in machine learning problems.


For example, to characterize a data point, like a rocket, we might need a dimension where


we specify its speed.


We can specify the rocket's weight in a second dimension, its height in a third dimension


and so on.


In some machine learning contexts, it is quite common to use the word dimension as a synonym


for feature.


And theoretically, nothing stops us to increase the number of dimensions or features to infinity.


We take whatever we can to characterize a point, because these features can be then


used by a machine learning algorithm to differentiate points one from another.


But what do high-dimensional spaces bring with them?


A large number of dimensions can be a blessing.


Think about problems that are not linearly separable in low dimensions, so cases in


which we cannot just draw a line to separate our data with, for example, linear regression.


But certain mappings to higher dimensions can make data linearly separable, so very


easy to solve.


However helpful in these cases, many dimensions can quickly become a curse.


To visualize, we have two points, A and B, in only one dimension.


To compute the distance between the two, we just have to compute the difference between


their coordinates.


Done.


In two dimensions, the distance computations become a little more difficult, since we now


have more coordinates for our points.


And if we assume that the points lie on the diagonal of a cube, forming a 45-degree angle


with both axes, then the distance simplifies a little.


It becomes the difference between the coordinates in one of the dimensions, say x, multiplied


by the square root of 2.


And 2 happens to be the number of dimensions.


We observe the same pattern in one dimension, where the difference of the coordinates is


virtually multiplied by square root of 1, which is again our number of dimensions.


The same holds also for three dimensions, where the distance between diagonal corner


points of the cube scales with square root of 3.


And here we generalize without delivering the proof now that the distance between such


points A and B is proportional to the square root of the number of dimensions d.


What does this mean?


Well, it means that in a high number of dimensions, there is a lot of space to begin with, so


we have a lot of new places to put points into, since the distance between them is bigger


with each added dimension.


When distances increase, it also means that the overall volume of the space increases,


so that we need many more points to sample the space with the same density.


For one dimension, it might be enough to sample only two points, but in two dimensions, one


needs four points to fill a grid with the same interval, and already eight points in


three dimensions.


To be precise, with each dimension, we need exponentially more points to populate a space.


Oh no, what do we do?


High-dimensional spaces are like a two-sided coin.


On the one side, high dimensions are a blessing for linear separability.


On the other side of the coin, there is a curse.


Many dimensions increase the overall volume of the space, so points and machine learning


optimizers can get lost inside the void of high dimensionality.


So what do we do?


There are ways to decrease dimensionality, and in the next video, we will explain how


dimensionality reduction works.


Can't wait!


Are you still here?


Go!


And do not let the dark side of dimensionality take over.


See you!


Attention, please

What's going on y'all? It is Friday the 23rd of June. I had to look at that. I should really get the date down pat before I start recording these things. I've done it in my VLOG so many times.


Attention, please

So, what are we doing? What is it? What's special about today? Well, of course today is 5 minutes of fire!


Attention, please

Fridays, FFF, because everyone loves Fridays. Fridays are fun. I'm having a hermit. I'm a hermit at the moment. I'm having a night in.


Attention, please

So, I'm going to do a podcast. Then I'm going to go cook some food for my family. And I'm going to read a book tonight.


Attention, please

I think that's actually this camera that I'm using, my iPhone camera, is propped up on the book that I'm going to read.


Attention, please

Tools of Titans by Tim Ferriss. Very, very good book. I highly recommend it. I'll put a link in the description if you want to check it out.


Attention, please

Certainly a lot of life changing stuff in there for me.


Attention, please

But, what's today's topic? Well, I recently did a post about awareness. Oh no, sorry, not even awareness. Attention. Another A word. How do I get those mixed up?


Attention, please

So, that's the topic of today's episode. 5 minutes of fire on attention. And now, let's get straight into it.


Attention, please

So, we'll get the timer going. Podcast, obviously you can't see this, but YouTube, check it out. Timer is go.


Attention, please

So, why did I do a post on attention? Why did I write about attention? Well, I've been thinking about it more and more lately.


Attention, please

I've been saying it to a lot of friends. The 5 chimps rule. And if you've never heard of it, I'll explain it to you quickly.


Attention, please

Essentially, the 5 chimps rule is an experiment done on monkeys that sort of discovered the psychology of stuff and whatnot. Yada, yada, yada.


Attention, please

Essentially, human beings are still monkeys. We all know that. Unless you're sort of non-evolutionary theory.


Attention, please

And what they concluded is that you're the average of the 5 people you spend the most time with. And I totally agree with that.


Attention, please

So, you look at any circle, right? Look at your own friendship circle. Have you got people around you that sort of do one thing, do this, do that?


Attention, please

You're going to feed off them no matter what happens. The more time you spend around them, the more you're going to become them.


Attention, please

You know how you have that best friend who you're completely alike, right? You get each other, you understand each other.


Attention, please

That's because you're growing together, you're understanding each other. And then you look at another relationship and you're like,


Attention, please

wow, I could never have that sort of relationship with that person. And so, I think that rule doesn't just apply to people that you spend the time with.


Attention, please

To me, it applies to everything. So, you've got to be very careful where you spend your attention, right?


Attention, please

Because attention is essentially our most valuable commodity. It's time. Attention equals time.


Attention, please

And time is virtually all we ever have on this earth. Even though time is sort of relative and may not even exist at all if you read the Einstein's theories and whatnot.


Attention, please

But attention equals time and time is everything that we ever have. None of this, all these materials that we have, whatever, they don't really matter, right?


Attention, please

It's the time that we spend here, the time that we have with others, the time that we work on things and stuff like that. That's what matters.


Attention, please

So, what's important about it? Well, because attention equals time, you should be very careful where you spend your attention.


Attention, please

I've been thinking this in my own point of view. Where am I giving too much attention? Is it making these YouTube videos?


Attention, please

Should I be paying more attention to writing? Think about it. The average of the five people you spend the most time with.


Attention, please

Also the average of the websites you visit the most, average of the five foods you eat the most, average of the five TV shows you eat the most.


Attention, please

Sorry, you watch the most, you don't eat TV shows unless you're sort of a Netflix fiend or something like that.


Attention, please

And here's an experiment actually. Like, if you go to YouTube and you watch something slightly different to what you usually watch.


Attention, please

Like, say for example, I go on and I watch, I don't know, how to cook a vegetarian meal. I don't usually watch videos on YouTube like that. I usually just Google recipes.


Attention, please

The next time I go to YouTube, my front page and suggestions will all be what to watch and it will have something to do with vegetarian there.


Attention, please

So, coming back to the law of five and the law of sort of the five things you do the most, the five websites you visit the most, I watch a lot of YouTube videos for education purposes mostly.


Attention, please

But now I find every time I watch something new, the front page of YouTube is completely different.


Attention, please

It's sort of pushing me towards learning more about stuff that I'm learning, which is artificial intelligence and machine learning and programming and whatnot.


Attention, please

And I'm okay with that. But think about, scale that out to everything. That's Google, that's Facebook, that's every website you visit is going to try and push you towards the algorithm gets very, has one task.


Attention, please

The algorithm that runs these things have one task and that's getting to know you. So they'll push you towards something.


Attention, please

You're going to be very careful and I'm going to be careful like where you sort of spend your time because it's just going to add up and add up and add up.


Attention, please

And eventually you may be doing something that you didn't even know that sort of how you got there, right?


Attention, please

That's where you get to that stage on YouTube is like, how did I get this place on YouTube?


Attention, please

Algorithms are also controlling your attention. And so what is, how can I wrap this all up?


Attention, please

Well, essentially the message is be careful with your attention and just be more conscious of where you're spending your time.


Attention, please

More so than for whoever's listening, whoever's watching, whatnot. This message is trying to, I'm speaking this message so I recognise it, right?


Attention, please

I've got a list of things on my whiteboard that say yes to do this and no to do that, right?


Attention, please

No is everything apart from what's on the yes list and the yes list is about six different things.


Attention, please

That was in my previous V-log. And so I'm trying to focus my attention on a small, small group of things and this time is about to go off.


Attention, please

So that's five minutes of fire. Five minutes of fire done.


Attention, please

But just to wrap it up, if we were all this careful, I don't know, with our attention as we were our money, what would change?


Attention, please

How would things look different? So think about this in your life.


Attention, please

You've got someone who's negative and you're sort of feeding off those in your life and you're feeding off those vibes and you're finding it's having a negative effect on you.


Attention, please

Or if you're someone who's, I don't know, visits certain websites and you're finding it's changing your day to day life.


Attention, please

Like for me, here's an example. Here's a completely honest example. When I first got a smartphone, iPhone, I was going through puberty and eureka.


Attention, please

I had access to the internet, which meant porn, which meant watching porn videos on my iPhone all the time, like every day.


Attention, please

I'm not even kidding. I'm being completely honest right now. And what happened from there?


Attention, please

My brain was rewired to have a completely different perception to what sex actually was.


Attention, please

So this is before I had had sex with any other human being. I was watching these videos and it was creating this image, this mindset, this idea in my head of what it was like.


Attention, please

And it was completely different to how it is in real life.


Attention, please

And so I think that's just one example of where I was spending so much time on these websites and it was completely changing my point of view in real life.


Attention, please

And I think that can be applied to, of course, the internet. So be careful where you spend your time on the internet, how your attention is sort of moulded by these algorithms now that sort of suggest these videos and these posts and whatnot that you should look at.


Attention, please

So just be careful of that because you could end up like I was and just have a completely different view of the world of how it actually is.


Attention, please

And sort of if you are hanging around people or an environment that is not where you want to be and not where you want to go in life and whatnot, well then change it.


Attention, please

Change the environment. Be careful where you spend your attention.


Attention, please

And this is most of all, as I said, this is a note to self. So I'm 23, I don't know half of anything. I don't know what I know actually.


Attention, please

I'm just sort of, I'm standing on the shoulders of giants with everything I say here.


Attention, please

But yeah, that's the challenge today guys actually. Think about where you're spending your attention. Is there a website that you could stop going to? Is there a person you could stop talking to?


Attention, please

I know it sounds dramatic but hey, if you want to live your life that's how you got to do it. You got to cut them off.


Attention, please

Is there a food you could stop eating that's really damaging your health? We all know how to eat well.


Attention, please

All you have to do is sort of eat food, mostly plants, not too much.


Attention, please

But that's 5 minutes of fire. I know it's gone over time but thank you so much for listening. Thank you so much for watching.


Attention, please

And have an awesome day. Call someone and tell them you love them. And we'll catch you next week, next Friday, episode 105.


Attention, please


I Want To Help 100,000 People Workout | AI Powered Personal Trainer

Yo, what's going on everyone welcome back I believe it is day 96 of the Daniel Burke show


Thank you so much for tuning in. Thank you so much for listening


What I've been up to today. Well, if you listen to yesterday's episode or watch yesterday's video


First of all my car broke down while I was driving and recording the video


I shouldn't have been recording while I was driving so don't take that as a as a good thing to do


I shouldn't have been doing that


but my car ran out of fuel and


Because I was so distracted by doing a video. I really do need a self-driving car


So maybe that's something I could I could get a job in and start working towards but nonetheless


Over the last two days. I've been


Bit of a hermit. I've put my head down. I built a personal trainer messenger bot. So what the hell is that?


Well yesterday I talked about


messenger bots, but today I


Finished it off. It's now a fully functioning working product


and


it I did it with a lot of help you can go to chat fuel calm and


Just start building your own body


It'll take about seven minutes and you can be a really basic one mines mines probably got a hundred steps or so


hundred different things that you can do with it and


And it took me I would say probably about


20 hours total maybe maybe a bit less probably about 15 hours


I did spend a lot of today on I'm only just wrapping up. I've got a I've got a switch off tonight


Otherwise, I'll be up all night. Keep going on it


I'm gonna fix it up during the week and make it a bit better and implement new features and stuff like that. So


Hopefully can can help some people work out. What's my goal with it? I want to help 100,000 people work out


With messenger bot so far. It's been out for about two and a half hours


And I've got a dozen people messaging it getting some great feedback


When I really start to notice it will be


When I start to get recurring users, I won't I won't classify someone as messaging at once as helping them work out


If they've recurring messages say two or three times


With with with specific messages


Then I'll count like with a with a workout message if they've continually done workout messages


Then I'll then I'll count that as an active user. And so that's my goal


I want to help a hundred thousand people work out and I'm slowly working out how to do it


The way I figured I've got to I've got to build this for one individual person and then work out how to scale it


So right now I would say my target customer for this even though it's free. My target customer is


Someone who doesn't know too much about working out and


They might be just new to the gym. They don't know what their workout plan is and


They don't know the main exercises so it can ask the bot the exercises and within the next week or so


Maybe maybe two weeks. I don't know probably less than a week. Actually. I'll try and implement


Some new features to send reminders of when someone should be working out or something like that


And I've got I've got to work out how to do that first, but this is all a process


And by the way, what I'm doing is nothing nothing special by the way, it's anyone can do it right now