Completing Andrew Ng's Machine Learning Course on Coursera | 100 Days of Code 12

Key Takeaways

check it out guys day 55 and 100 days of code series as you can probably tell I was learning about Python today and if you're wondering why my screen so orange it's probably because of flux now what flux does is it takes up that blue light of your screen so as you can see it's 9 9:54 p.m. where I am so I'm about to get ready for bed and so taking the blue light out of your screen late at night helps to helps with sleep I don't know the science behind it something to do I'm getting with a fire maybe because humans has really evolved sort of we all spent time in tribes next to fires so maybe that's why having an orange screen is good but I've known for sure what I learned today I did a whole bunch of Python went over some notes I took in the past from when I started Python and added to my flashcard system which is called Anki I'll put a link in the description for all this stuff by the way that's my medium series you can check that out on medium but yeah there's piping hanky flashcard software amazingly good health with spaced repetition and yeah I was learning about regular expressions in Python so regular expressions from my understanding you can use them to sort through like a text document and pull out important detail so I took contacts emails names addresses whatnot and I'm hoping in the future it can be something that you can use for pulling data from websites because I think that'd be really handy I know you can use Python for that I just haven't learned about it yet and another thing to finish off tonight I mean looking at radar you actually read his book principles I run here I think you can find it online somewhere he email it available in 2011 I believe and so yeah as you can see I'm taking some notes here write down their principles some awesome YouTube videos I'll link them in description as well but just have an amazing time learning from the best he's worth 16 billion dollars has done a hedge fund one of the biggest in the world and all-around amazing guy but I'm about to do some reading go to bed tomorrow is we tend of machine learning so I'll see you then check it out y'all I just finished machine learning by Stanford University and Andry room on Coursera 11 weeks of courses done in about another ten nine I can't remember how long but finally finished and well not finally finished um I'm excited I learned an incredible amount through through that course but that being said it's not the end I've still got my deep Loden nano degree to go I still got the treehouse pipe and track that I'm working on and then I've got a whole bunch of planning to do to create my own data science slash slash artificial intelligence master's degree over for the rest of this year at least but welcome back to day 56 of the hundred days of code check out all this I just finished the last lecture so summary and thank you Andrew is an amazing teacher by the way I highly recommend this course it's all my notes eleven weeks of course smooth plus a massive Evernote document all the way back to week one I should put the date there so I know when I started oh well question is what do I do for the rest of the day because I feel like that's a major a major accomplishment I don't know I think I'm going to review some pison that I've done I've also got to I'm going to edit a veal OGG as well get that uploaded tomorrow remember every Friday I released movie log on I'm going to do some notes on finish off the notes I was doing on radar Leo's principal's book and get a podcast down and get a blog post out for you guys as well so I'll I'll link them in the description I think this video should be out know all that stuff I just said should be out before this video is but stay tuned I'm going to do a blog post later this week as well of detailing what I'm doing to learn or how I'm how I've been learning deep learning and machine learning so far in 2017 and check out my medium series guys always update that every day of what I've learned during the day and so you can follow along whatever I'll put that in the description as well but that's it that's a wrap for day 56 100 days of code we'll catch you tomorrow day 57 what's going on okay so wait I just realized that I say what's going on every clip so I'm going to sort of cut that down and work out how to transcend better from clip to clip but I'll work that out so epically raining today where I live took this out all look at that I was distracted so it's went outside perfect day to study if you ask me I'm doing some work here on Hankey I got - I added some new cards in the yesterday I got about 15 we can't do my trackpad is dead I need new batteries there's no batteries in the house and I'm also working on some podcast notes here for episode 107 so what I've learned from speaking to people not the not the topic itself like nothing to edit I love the show maybe it will not sure but it inspired me from I'm going to slide actually from last night I went to a meet-up in my hometown called the brisbane artificial intelligence made-up so shout out everyone that goes to that and I went by myself and I was trying to convince my brothers to come along and I'm not because I was like enjoy these these moments the best experience ramesh shared by and sound like that's right I'll go there and I'll meet some people who are interested in the same stuff I am and I was walking around in the crowd and I couldn't believe it like I was there by myself at about a hundred people there eating some food and the one person I start talking to like he was standing by himself I decided to talk to him was the host of the of the talk so shout out to David if you ever watching this unlikely but doing some amazing stuff with evolutionary algorithms which I didn't even have any idea about before I went I should have maybe done some research before I went but the talk was amazing and I don't know I really had a good time and I got David's card means had to reach out to me if I was interested in potentially some casual project working the future nothing may come of it but that's alright like that's the thing right you never know you go to these things and you speak to someone and you never know who you might talk to so I think they're valuable in in small doses that being said like when you're starting out like me say yes to almost all of these events but if you're sort of working on something and you know what you want to work on say no to all those events and just work on what you want to work on but I think the main the major key for me like going to be sort of things is finding finding people making relationships with with the people that are interested in the same things you are because no doubt I'm I'm weak in so many areas and you'll learn an incredible amount from people who are I don't know different to you just different that's all you've got to do that they're an expert in something you're not it you're not an expert in so find what that is learn from them and make yourself better but I'm going to go back to learning some Python on Anki and then do a podcast and then plan out what I'm doing to the rest of the day so we'll see you in the next clip I've also got to work out the out show the clip I don't know I'll work this out you know I'm still new to be logging I've done like 10 and yeah sup yo day 6,500 days of code series I'm filmed for the last couple of days because la I took the day off technology or most of the day off technology and and someday I was doing some writing so I didn't film any coding or not but today I was back into Udacity I also started writing blog post 3 or part tracery of the how I'm learning deep learning in 2017 series that I'm doing I'll link the other two in the description and probably by the time this video is up the third one will be out as well so check it out it's just literally me documenting the steps I'm going through and learning deep learning such as what I'm doing to do it like where I'm studying it what I'm using with it Khan Academy books treehouse Python resources all that sort of stuff I'm just going to document it all so if anyone wants to learn about it how exactly I'm doing it that'll be a long side the medium series I'm doing which is blogging everyday writing what I've learned today I learned about DC games in um Udacity so deep convolutional generative adversarial networks that's a mouthful so yeah games are this revolution and deep learning which is a massive thing that's happened under the last couple of years actually it involves training to neural networks and essentially getting them to work against each other to generate some output and it's really cool what stuff can you use games for video generation you can generate entirely new videos that never used to work the way you can think of games is to networks a generator and now on the size of D not a decoder I can't remember G and D dismember one and two so one you can imagine is a magician and the other one is the audience and so the generator which is a magician gets better and better at creating fake data which the what other what the audience has to sort of decipher and they have to try and guess whether it's real or not and then the output of that is is the new stuff right so I'll link a video on the description of Suraj explaining deep sorry Gans generative adversarial networks because here we have to do it a lot more better than I am but I've done enough learning coding today I'm going to go for a run I've got dodgeball later tonight but we'll see you tomorrow I'm back into Udacity again I'm going to keep learning about this and hopefully tomorrow I'll have a better explanation for you what exactly again is so soon check this out it's kind of creepy right this is using DC games or deep convolutional games to transform this space sating right to this face facing left using a whole bunch of other other faces so go slowly slowly slowly from facing right to facing left now the iPhone I'm filming this on is picking all of these up as faces so that's really cool and the crazy thing is these are all generated by a computer what's up guys we just saw was a just a paper that I was reading and it's the first academic paper I've written while and it took me a fairly long time at least 35 minutes and it's only about 10 pages or so long but I was just trying to wrap my head around all the new concepts that are in there and it's on DC Ganz which is deep convolutional generative adversarial networks and I learn more about Ganz today through the Udacity deep learning foundations nano degree I know yesterday I said something about that I would come back with a bit more knowledge about games good news is I do I remembered what the D stands for in the gang architecture it's a discriminator so you've got in again you've got two networks working against each other a generator and a discriminator so the generator will take in a random noise sample of data and create fake data that the discriminator will have to decipher between real samples and the fake samples that the generator produces and that will decide what the output is but I'm not going to go too much into depth otherwise this this vlog would be way too long but I will link an awesome article in the description that uses SpongeBob SquarePants as an analogy to describe games and I think it's I think it's really good so I'll link that in description make sure you check it out I'll also link this academic paper that I'm on archive I can't remember who it was by let me just find that out for you there we go unsupervised representation learning with deep convolutional generative adversarial networks that's a mouthful by some researchers at Indyk oh and Facebook AI research so really cool I also got some more diagrams here on Ganz as well so as you can see you've got the input random noise into the generator network and the generator creates these fake images and you've got the real samples here the code and the fake and the real samples go into the discriminative Network and then the discriminator chooses whether they unlock it's a real image and that's the output and so what are some outputs from a generative Eric so it could create an entirely new image so say for example you typed in the description of a bird the generator network could create an entirely new image of that of that bird or say for example I drew the outlines of a mountain ganz have been used to sort of by Adobe and I believe it is called I can and pics depicts I'll put a link in the description to that as well just drawing the outline of the picture and then using a generative network to bring up an example of what an actual real picture was so say I draw a mountain the outline of a mountain like a triangle in some some water again has been used to transform that into an actual real picture so that's that's cool as well as um edges to cats so you can draw like the outline of a cat and it'll transfer that into a photo of a real cat I think there was a hashtag going called edges to cats I'll link some examples in the description as well but that's it for learning I'm going to go do some jiu-jitsu it's my first time doing it so I'm really excited and tomorrow is going to be all about Python and say with the next day and I think I've got about three or so weeks left of this deep learning course so we'll keep doing that and then I'll do my planning for the next six months and I'll be sure to hit talk about it but catch you tomorrow