Finishing the Treehouse Python Track | 100 Days of Code 13

Key Takeaways

yo Dave 60 to 100 days of code series I was learning about Python today I woke up this morning and realized that I was flirting with sending the day with my brother I forgot that's completely my fault I should more on top of these things that's all right I helped him build resume I hope to apply for some jobs because he's in this final year of high school and he's starting to get into the the real world the real world kind of things and I was just hoping he was learns and things like Gooding a resume and applying for jobs and stuff they I think they should they should really be teaching this stuff in school but well you do what you can but otherwise yeah I was learning a bit of Python only a little bit though I didn't spend as much time studying today as I would have liked but that's okay we'll be back into it tomorrow I fixed up a whole bunch of email stuff replying to some questions I got on Quora you can check out my Korra profile in the description actually I write a lot there about fitness and nutrition just stuff that I've learned I don't really write anything that I haven't tried myself so it's just me relaying my experience I also donated blood if you can't see because my arm is in a sleeve right now but I've got other bandages around mom never donated blood before I highly recommend it you'll take five ten minutes of your time depending on where you donating it and you have the potential to save three lives well at least that's the case in my country as for tomorrow it's going to be all about piping again as I said I finished the machine learning course on Coursera and I'm really trying to get on top of Python syntax because Python is the language of machine learning of data science and whatnot and yeah I also updated my medium series it's completely up to date now up to date 62 so if you check it out that will be in description as well but we'll check in tomorrow more place and study I'm going to continue the track that I'm doing on treehouse I'll try and finish it tomorrow so then I can move on to something new so kind of learning about databases in Python I finished the Python regular expressions track I'm sorry cause the piping regular expressions course on tree house today which is a part of the Python track that I'm doing and this is the second last module of the Python track it's done emotion in Python and I'm extremely excited because I can see where this is going - this is study I'm stunned to learn about SQL which is sequel or something like that which is a database in language which eventually can be used to create databases and the database is to me I just sort of thought other quick analogy is essentially just a table right oh I didn't think of it I learnt it and I've always sort of it as far complex than just that rather than just being something simple like a table and the database can be like a group of usernames and passwords of people who are using your app contact details names you name it everything can go into a database of sorts and the reason why I'm excited is because I can see myself using this sort of technology in the future along with the other stuff that I'm learning and so once you sort of get on that path it's a long way but once you sort of hit these little milestones of learning where you can apply this stuff then it's really fun by the way it's day 63 I've been doing Python all day and at the finish six Pomodoro's and then going to wrap it up actually after I finish this clip and head off to jujitsu so my second night doing it the first night was amazing got a few cuts and scratches over my body but it was good fun highly recommend anyone trying it trying to get out if you've thought about it before Brazilian Jujitsu find a local place to do it it's really cool but we'll catch tomorrow I'm going to be working on some own projects any gym and potentially move more but most likely just image you see then the article is officially live how I'm learning deep learning in 2017 part 3 got part 1 and 2 legs in it's a fairly extensive post about 20 minute read or so but you can get most of out of it like most of everything done in this level 10-second summary I've done here what is up y'all stay 64 is 100 days of code series I supposed to say writing that article as well as working on my startup with my little brother that was some good fun and then I just recorded a podcast which is getting uploaded now so I'll link everything I just chatted about in the description otherwise tomorrow I'm going to be planning out the next week of studying I'm going to be learning more piping more the deep learning course and then of course more reading and more and more others they're more parsing always more piping more preparing for what's next after the Udacity deep learning nanodegree I think I'm going to enroll in the AI nano degree through Udacity because they get entry into that but everything's still up near at the moment I need to make some concrete steps to go in the future but everything will be linked in the Trello board that I'm studying deep learning on I'll put that in the description as well as well as you'll be hit up with these logs every week or so and the medium series will keep going until the end of a hundred days and after that I'll finish the hundred days of code series and I'm thinking way too ahead fire in the future but tomorrow bit more learning and yeah we'll see in the next few Clips I just finished updating my hundred days of code series on medium.com you can check it out I'll make sure the link is in the description it's a little I write about a one-minute blog post maybe less every day just to summarize what I've learned mostly to sort of resubmitted in my own head as well as I'm going to just pull out to the world so if one if someone ever wants to know how I went about learning all this stuff and it's going to record there for it but otherwise what have I been learning about today semi-supervised learning check it out so semi-supervised learning is one of the newest implementations in the world of deep learning in terms of using a small amount of labeled data to to solve bigger problems right so in terms of the what's it called the house number data sets an SV HN data set you can use semi-supervised learning to learn how to label house data sorry house numbers with only a small amount of them actually labeled so think about it if you're a human being if we go into an environment we don't necessarily get given all of the labeled data like in this room like in this room if I walked into it I don't get told what everything is I sort of just have to work it out myself I might get told what a few things are and work out the rest of it that's a whole idea with semi-supervised learning is that using Ganz so generative adversarial networks you have a small amount of labeled data and then the discriminator part of the generative adversarial network the gains the discriminator will be used eventually as a classifier Network so you have a small amount of label data and then the began network works out how to know how to label the rest of the data so say for example we had 10,000 10,000 images of house numbers we knew the numbers of a thousand of them using again you can sort of go over that thousand thousand label data set and then use what it's learned on that to sort of guess the rest of it so that's semi-supervised learning rather than sort of a supervised learning set where you have all them labeled the 10,000 label and then it'll model that that 10,000 so you're modeling a small portion of it and then working out the rest of them and now that explanation probably wasn't the best is because I've literally just started learning about it today probably one of the most complicated modules I've learned to date yet we're going to hear here some code I didn't write all this of course this is provided by Iain quintella but slowly but surely learning I know it's new so I know it's going to be complicated I'm going to keep recording I'll record tomorrow actually once I've learnt some more about this hopefully the explanations a bit better but as with anything you're just going to keep going keep learning keep practicing but I'm going to go do some pull-ups before go and play dodgeball tonight and then they'll get back into studying about semi-supervised models tomorrow yo day 69 of 100 days of code series I left this video clip way too late it's like quarter to 11 where I am and it's my bedtime but I'm doing some writing in bed I disassembled my my bed frame this morning because I didn't record yesterday because I've had a incredibly sore neck and shoulder I think it was because I did a massive workout before going to be eligible on Monday but nonetheless I've been learning about Python and semi-supervised semi-supervised learning yeah that's right in the deep learning no degree that was Tuesday I finished that module and then started the intro to data science module on Udacity and it got me really excited because I'm starting to work on actual projects like well hold on I've been working on projects the whole time but projects that a little bit more entry-level to programming so I could understand them better and recently in Python I've been learning about how to access databases which is getting me excited because I'm starting to sort of think about how I can use this like sequel databases and whatnot to build applications and build out my ideas but nonetheless I'm going to try and finish the key team member lisauk I'm going to try and finish the Python track tomorrow that will be my goal so I'll check in with you then pull out in a day and hopefully finished it by then day 70 of the hundred days of code series it's so dark in here I've been studying too long bring it some water alright so what have you up to today - learning all about Python it is what is it Thursday the 20th of July 2017 day 70 but then all that Python yesterday and today and guess what guys I did it I finished it I finally finished the Python track on three hounds I'll link it in the description but I work through it took a lot longer than what I initially thought it would just because I haven't I wasn't focusing solely on that all sort of learning bits and pieces in there but exciting times I've just learned about databases and unit tests and testing you can do in Python and I feel like I've got a good fundamental knowledge of the language now and I can sort of expand on that that's a good question what is next well I've been looking at data camp and there Python track on there so I can sort of take the knowledge I've learned on treehouse and expanded in more of a data science terms or sense of you using data camp but I'm not entirely sure yet as to the rest of today I'm not sure I've got some good some decisions to make and I've got some planning to do about where I'm going to spend my time next I've still got about two to three weeks left of the Udacity deep learning course and then all the courses I've enrolled in over the past I don't know hundred days or 70 days 180 code series will be complete so I've got to sort of I know rework and replay in my my learning but I'll hit you up when it's finished otherwise check out treehouse I've said it a phenomenal resource to learn some fundamental things about programming coding and they haven't just got Python they've got literally every language you can imagine and I'm excited to learn more but I'm going to go tell my friends how much Python I know and I'll be back tomorrow hey yo Dave 74 100 days of code series aka 24th of July 2017 I just finished gym and I forgot to record a clip earlier today so I'm going to do it now it's my brother's birthday so shout out to Marella happy 22nd birthday will whatever what have I been learning today I went over some semi-supervised learning with Ganz that I was studying last week on Udacity just because it's my last module of the Udacity deep learning nanodegree foundation series and I've got the assignment which is due on April 3rd so I'll keep you up to date with what I what I do for that the assignment is actually to generate phases using a again a generative adversarial network which is absolutely awesome but as for today I was using Gans to train a semi-supervised network which semi-supervised means not all of the data is labeled and what I'm showing you here is different epochs of what the generator network of again would create of the street view house numbers dataset so you can sort of make out that these are numbers here so that's a 1 a 5 let's go down to one of the later epochs so an 8 another 5 another 8 5 what's a fine eight ones and whatnot but the goal of using semi-supervised learning is to rather than having a massive labeled data set which is going to be hard to come by sort of all the time right it's good fair enough you have a labeled data set but human beings we don't usually have a massive labeled data set when we learn and so the idea of semi-supervised learning using again is to use the generator network to produce fake images so that the discriminator network has more images to train on rather than just using an entirely labelled data set so of course you use a little bit of real data a little bit of real unlabeled data and then the generated networks creates completely fake images or new images and the discriminative Network then has more data to Train off and that's the whole idea of semi-supervised learning with Ganz what else was I doing today I was working on a bit more of the Udacity intro and data science course because once I finished those sort of the deep learning and memory I'm going to be creating my own data science master's degree so just getting my head around data science and whatnot and it's it's going awesome but that's a wrap up for today tomorrow I'm going to be trying to fix up my github and going to start work on the latest Udacity sort of my last assignment in the deep learning and in brief foundations course so we'll see you then you