Coursera Deep Learning Specialization Progress | Learning Intelligence 15

what is going on learners welcome back to learning intelligence episode 14 fun fact about the number 14 I have two dogs divide 14 by 2 and you get 7 and I have also have a dog called 7 so fun fact to begin the video with but today's been an epic day I've been working through the deep learning course on Coursera as I was in the last video and check it out I just passed week 3 programming assignments so I got 100 out a hundred points and that was my second submission actually where is it there was the first one so got 90 out of 100 which is still passing you only need 70 at 100 to pass but then I went and resubmitted it fix my error and got a hundred out of 100 and that's one of the things I like about these courses is that you can resubmit the projects and resubmit the quizzes and implement the knowledge that you've learned from from your failures or from your alright from the answers you got wrong in the past one I think that's a really good way to learn because I remember a past in the past had submit projects and I wouldn't really get feedback on them or wouldn't really know where I go wrong or you'd submit an assignment for me in my case anyway at university and it would be two to three weeks minimum before you get feedback on it and this I know code is a lot sort of you can put in test cases and market almost instantly in some in in some cases be really hard to to mark an essay with a computer yet but when natural language processing gets better you could probably use that to mark essays who knows but I want to show you to two more things for the introductory clip first of all is the deep learning book by in Goodfellow actually in Goodfellow was in the interview of the Coursera course so at the end of each week there's an interview with a hero of deep learning and today was in Goodfella you may or may not have heard of him if you want this book by the way go to deep learning book org it's free all the chapters are free I'll put the link in the description but in Goodfellow has invented Ganz if you haven't heard of Ganz I won't go through them entirely in this video but a brief overview is it combines game theory with deep learning so essentially it creates two networks two neural networks to work against each other to produce an output but if you were interested more in Ganz I look up to one of the sera jazz videos on Ganz because he doesn't incredible job at explaining it and the third thing is one of my articles got published in the free code camp medium publication you may not heard of this but medium has some incredible articles and I wrote an article the next step towards artificial general intelligence Starcraft soon look there's me look at that face I read now girl in the other day and my face got to take it as a cat so wrecked but yeah this is this is my opinion on why why oh just why I really love reinforcement learning and gameplay and the kids even Christmas Christmas themed because that's coming up in a few days I got the Christmas tree explaining what what deep learning is in a nutshell so I'll put the link in the description to that as well if you could go you that's some love on medium get it get it out into the universe I'd really appreciate it I do love writing I do love making videos so if you guys could give some love to that thank you so much but as for the rest of this week I'm going to be going hard on the deep learning course and Coursera I want to get at least 50% done by the end of this week so we'll check in with the progress something I forgot to show you in the first clip so really it's for things I wanted to show you in this first clip it's been a big day was what I did for the project so the idea was to build a neural network to classify these blue and red dots and you may not be able to see but there's some blue dots here red dots and the idea was color these zones which color is predominantly of the dots so you can tell this one is predominantly red seal the red here this one's predominantly blue red blue blue red etc and so the neural network that you built by the end of the project classified them with an accuracy of 90% and now let's check out the logistic regression model which is essentially just a straight line so yeah see it's a this is predominantly red this is 20 blue an output was only a 47% accuracy so that's a really good example of how much of an improvement you can get with just a single layout neural network and deep learning uses multiple layers so over one is classified as deep learning and from what I've gathered so I think some of the biggest deep learning Nets like very deep learning is classified as about 16 different layers so you can imagine as the input goes through all those different layers and all these save math mathematical computations a moment you can get some very very accurate outputs and and predictions on the other end all right check this out guys I just passed both assignments for week four of the deep learning specialization and now I'm testing my models on my own image so ideally it will predict one if it's a cat picture or zero if it's a non cat picture so essentially it's a it's a cat version of hot dog not hot dog and I need to tweak my model because look at this cat picture that's clearly a photo of me predicts a cat picture another photo of me predicts our you guessed it cat picture that's a beautiful car though and can't picture and cat picture so clearly there's something going on with my model but the algorithms that I write the code that I wrote managed to pass all the all the test cases that they had for the assignment and the same that Kasane goes for the other assignment I completed today so I managed to get two done today it's been a big day it's been it's been good fun so this was number one essentially you build your deep neural network step by step and this is an overview of it you start why initially initializing the parameters so you have the the weights and the biases and you come through here you build the forward propagation network and then you come down calculate the loss feed it through the backward propagation Network update your parameters and this is a this little loop depending on how many layers of your neural network you have and then finally you make a prediction on the data set of what you think there what the algorithm thinks the next likely output is and because of this because I passed these to check it out guys we passed we we've successively successively successfully completed course one of five neural networks in deep learning so you know what that means tomorrow I'm going to be going on to part two of the specialization so improving deep neural networks high parameter tuning regularization and optimization I can't express to you how much I'm loving this course right so if you if you haven't done the deep learning now and agree before I would highly suggest if that's to be do an expenditure for you I would highly suggest going the deep learning allegories from Udacity by the way amazing program but this one if you've never done anything to do with deep learning I'm finding this has a much more foundational approach so it starts from the ground floor and they look I might be a bit biased because I've I've already done the deep learning allegories I do have some foundational knowledge of what deep learning does but from the brief time I've spent on this course everything have been enrolled for it for four days and I've I finished module one it has definitely much more of a ground floor approach to building up your knowledge and your your the ideologies of deep learning so tomorrow I'm gonna start in part two I'm really excited for that and the first one was a supposed to be four weeks but I got a completed in four days it's certainly about four to six hours per week so I was doing that per day or so and this one's part two is about three weeks in length but I should be able to get that done by the end of the week so potentially from the end of this video we'll be halfway through the deep learning specialization so we're saddled on week two of part two of the deep learning Coursera today I settled on part two of costume today I started on part two of the deep learning specialization on Coursera I've made it through the first week of classes so far up to the the programming side which I'm about to do next but I thought I'd share with you two of my favorite things that I found during the lectures and one of them was on baiance bias and variance so essentially if your model has high bias it's a set it's under fitting the data and if your model has high variance you can think of it as overfitting the data like being too close and the way to fix that is with high bias you can train the model for longer or add more complexity to your model maybe more layers or something like that more hidden units and to fix high variance so overfitting you can use more data or you can do ad regularization and regularization was something I liked and I found an analogy that regularization is akin to say you were raising a child right and you want to give the child enough freedom that they decide who they want to be on their own at the same time as you want to you want to give them some constraints so you make sure they don't go too far off the rails so for example if you want your if you want to raise your child in the perfect balance you might say okay you can have this this drawing pad you can use your iPad here and there however you also have to do your homework get good grades in school or exercise at least once a day or something like that so regularization helps to reduce the overfitting of your model by making sure that it fits just right so not too not too oh not too much which is overfitting which is what you don't want and not not too little which is underfitting so there's a balance there that that can be fine and that's that's with all all deep learning models right if you find that it's if your models under fitting data well it may have high bias and if you find that your models overfitting the data and well it probably has high variance and there's some some quick and easy ways that you can fix both of those things so I just finished the assignment for week one of part two of the deep learning specialization on Coursera and it was really great actually went over so this this part of the course is all about so you've got your your deep learning algorithm ready it's all about optimizing that out rhythm making it better so yeah your deep learning model sorry same but your model there and this this part of the course is going over I think week one is wants to do with initialization regularization and gradient checking and now we're up to optimization algorithms so these are all all ways to to improve your deep learning model so it's one thing to to have a good or diploma model that works and produces data but how do you make it better so for example initialization is something that we covered in the assignment and you build each of these step by step in this this course actually I really enjoy that it sort of breaks it down it really takes it back to first principles so initialization is an example of that is choosing the optimum weights to start your values at so if you start if you initialize your weights of your deep learning model to start all zeros well then it's not going to make any progress because it's just going to continually produce zeros so what I learned me initialization phase is that it's good to randomize the weights when you begin with and then as your model progresses forward through the forward propagation layers and then through the back propagation layers those weights get updated and then there was something really cool as well was an interview with yoshua bengio if you haven't heard of hear me is also a deep learning professor much like Geoffrey Hinton and if someone you should definitely look up and my favorite thing from when he said was his advice on deep learning is to play around a lot of toy problems because toy problems allow you to - it increases your research time oh sorry it speeds up your research time so you can do more problems over and over and over again so he said instead of trying to tackle the biggest thing you possibly can start with with the smallest thing you possibly can and then perfect that and iterate over and over and over again and that's something I need to work on so future videos I will be making deep learning models from scratch I'm going to finish this course first and then we'll we'll get into those videos sometime in the future today was all about optimization algorithms so we've started off with this initial data set this is called the moon data set so you see how the data the blue here is shaped in like a sort of a moon and so the red the red Delta shaped in a moon and what we did was we built a three layer neural network to model that data you see here we built uses a gradient descent optimization and with a learning rate they all have the same learning rate zero point triple zero seven because it's pretty noisy there and I ended up with seventy nine point six percent accuracy and fairly fairly okay split to the data day and then we used momentum optimization and that got the exact same results as the gradient descent optimization and then finally the atom optimizer blue the other two out of the water 91 point six percent accuracy surrounded up about 92% accuracy awesome modelling there each of those algorithms we've built from scratch and I found that to be really good because you can implement these I have implemented the atom optimizer before with tensorflow in the past and it is it is built into tensorflow which is a deep learning framework but building it from scratch is giving me a deeper understanding and that's what I'm finding with this course it really takes it from a ground-up approach so the deep learning now in degree that I did a couple of months ago was kind of like diving into the deep end of deep learning but now sort of I'm doing it backwards I started implementing the models in deep and the deep learning now the degree in building projects and whatnot but now I'm and now I'm getting that base baseline understanding of deep learning through the Coursera deep learning dot AI course so if you learn in a fundamental way I would suggest the deep learning AI cause and starting from there but if you like to dive straight anything's you've got a good good understanding of how how different neural networks and stuff work the deep learning that agrees probably for you so next we're learning about hyper parameter tuning bash normalization and programming frameworks this is week three of course - and I'm excited there's an assignment at the end of this one on tensor flow so let's do it oh yes you like my banana singlet if you can see it if you do leave a comment below or share with me your favorite fruit yeah what fruit should I put on the singlet next alrighty so we're about halfway through the lectures on week three of course - on the course there are deep learning specialization and this week goes through I had it here here we go hyper parameter tuning batch normalization multi-class classification an introduction to programming frameworks now I'm really excited for that last one I'll go over that in a sec but interesting point Andrew went over here about tuning your hyper parameters in terms of importance he ranked learning rate has been number one and then you got theta and the number of heating units and the mini batch size has been second in importance and then you've got the number of layers and the learning rate decay has been third most important and the beta the beta - and the epsilon he uses the atom optimization algorithm to set these automatically which go to 0.9 0.9 nine 10.8 and now if you're not sure what hyper parameters ah just imagine what can they be a set of criteria to help improve your model that's it so it's like it's like a guideline so when you go bowling when you first start out you might have the bumpers up to help you help guide your the ball down there down the lane and then as you get better and better you might slowly decrease the amount or or change the amount of bumper you use and that's the same with it a deep learning model but a bit more basic with a deep learning model you might start with some high parameters that are like a safety net like just trialing it out see how you go and then as it gets better and better you'll slowly tune those hyper parameters which would be equivalent to to lowering the bumpers on that bowling lane and now it's time to wrap up learning intelligence 15 it's been a great week guys getting getting some great progress through the deep learning specialization on Coursera as I said this video will probably drop just before Christmas or that time or New Years around there play a bit before New Year's more more around Christmas so Merry Christmas if you're watching and of course thank you for watching but my goal is to have the the course almost entirely finished by Christmas I think I'm I'm just on track or if not just a bit before I'm certainly ahead of schedule or what they recommend some of the courses they've said it will take up to a month like doing four to six hours a week instead I'm doing four to six hours a day so it's definitely accelerating the process it's really good fun and if you haven't been able to tell from the previous videos of the other clips I'm really enjoying this course and if you're looking to get into AI or deep learning in comparison I'll do a full review of this video of sorry obvious course once I've completed the whole thing but so far it is probably the best resource I found for learning deep learning or AI from a ground floor approach so if you if you're new to deep learning at all new to neural networks this is probably somewhere you want to start the the deep learning specialization on Coursera and of course there'll be a link in the description but otherwise it's time for some shout outs of the week in no particular order these people reached out commented on my videos sent me an email you can send me any amount to if you want Daniel at mr deburr calm otherwise leave a comment if you want my help on anything I'll try answer as best you can you want to see a video in the future leave a comment below nonetheless thank you too Ruta raj soon ham 0 1 nexus wars takea larry and michael you guys are amazing i really appreciate you guys reaching out it means the world to me that you're interacting with our videos I hope I'm keeping to bring you or I hope I'm continually bringing you value as much as I can what's in store for next week's video well we're gonna keep going through with the deep learning course on Coursera I'm really liking it as I said I want to try finish it through through before Christmas and we're just going to straight line it do that 4 to 6 hours a day and of course I'll be bringing you along with me thank you so much again for watching I'll see you in next week's video if you want to see anything else leave a comment below don't forget to like subscribe if you want to and always keep learning