Udacity Artificial Intelligence Nanodegree NLP Concentration Progress | Learning Intelligence 23

Key Takeaways

this week we're jumping into the natural language processing section of term 2 of the Udacity artificial intelligence nano degree let me run you through what I've been up to today it's been a been a pretty productive day we look here I was reading this book on a pile of books and actually before I I'm really keen to read this one I've been listening to the audio version of it actually all these are good books you want to see what what books I'm reading check out mr deburr comm slash books i put the link in the description anyway been reading this book on deep learning and told it like it can't even touch the screen it's called deep learning with with - I believe or deep learning in Python something along the lines of that the link will be in the description anyway and I'm taking some notes here it's written by I think I mentioned it in a previous video it's written by Francis or Francois I'm not entirely sure how to pronounce his name Francois Charlotte Charlotte maybe I think it's French but here's here's not the author of carrots which is a massively popular deep learning framework and a deep learning library as well and he works at Google so he knows his stuff and the way this book is written I've already I've been studying deep learning in AI for almost a year now and I'm picking up different I'm putting together different pieces of the puzzle by reading this book so that's why I think it's so important guys - to try and learn although there's some great courses online like I'm doing the best courses in the world at the moment in my opinion but it's always good to learn from different perspectives that means blog posts books podcasts wherever you can so just and create your own mental models and don't take any one word that one person says to be gospel compare that to someone else who's an expert in the field and then you can draw your own conclusions right we go into the Udacity classroom I went through this lesson today and that is very orange so give me a second I'll just turn off flux so we can see this screen a bit better there we go so I went through this lesson here which is intro to natural language processing and that was really fun actually there was a member here or a staff member from IBM there we go I'll show you quickly is this gonna live me the Islanders in Italy that need to resolve what the biggest challenges is understanding and maintaining their contacts with logical so upon who you just saw or Armand I'm not entirely sure that's our plan I think and that's almond almond works for IBM which is where we've been going through or what we've been using for this project now this is called bookworm and it's one of the projects in the NLP section on the AI nanodegree term - and we're going through here using IBM's Watson which is kind of like IBM so AI models are best running the cloud guys unless you have a giant computer which can run all your or your deep learning models and all that sort of stuff I don't I have a laptop with a CPU not a GPU so I use cloud models so IBM Google cloud Amazon Web Services you can you can use a state-of-the-art ABI api's from them to build incredible AI models and so what we're doing with this this project here is using IBM's Watson to ingest I think it's Star Wars a corpus of text of Star Wars dialogue and then derive some meaning from that but I'm still working through that and I'll show you a bit more once I've I've progressed really and the last thing my last two last things for this clip this book I mean I was recommended it by our plan which is on an Armin I'm sorry if I'm getting the name right the guy that works for IBM really smart dude I really love to listen to him talk and he recommended this book the master algorithm how the quest for the ultimate learning machine will remake our world and you know I love I love anything to do with AI so I think and I love reading of course so I think this this book I've got the the trial or the sample chapter on Kindle so I'm gonna read through that maybe later tonight maybe in the next few days or so I'll let you know how it goes and if it's worth picking up or not the last but not least if you want some free credits on IBM's platform and another free learning resource I just found this one out today I think it's there we go cognitive classes AI you can get 1200 US dollars worth of IBM Cloud value by signing up don't use my code because I've already used it sign up for a free account on cognitive classes today I they've got a bunch of other courses I don't even check them out yet I've already got my courses that I'm supposed to be doing so it's it's best for me not to get distracted I think we're gonna get a storm here tonight it's coming skies are cloudy rain starting to fall who else studies really well when it's raining let me show you what I've been up to today was all about the feature extractions class in the Udacity NLP concentration on the AI nano degree and it's really cool so let me let me just read out here what feature extraction is so transform text using models like bag of words and you can't read that tf-idf word Tyvek and glove to extract features that you can use in machine learning models i won't go through all those in detail but i'll just i'll just share with you some of my quick notes that I took on it we come over here we start with bag of words so essentially bag of words treats each documents like an unordered group of words and then it picks out each term in it each term can be treated let's term frequency so how often does the word little occur in this sentence we've got one house one and then we have we go on to you can work out the relationships of those between those words by using the the cosine similarity or the dot product I think that's really cool that's a really entry-level way of finding it out and then tf-idf is another way of adding weights to words to which signify their relevance in documents so you compare the term frequency which is back up here how many times it occurs with the inverse document frequency so how many times does a particular term let's say little occur in in the one document and how many times does it occur in all of the documents now we've got one hot encoding which is essentially creating one plot vectors for different different words so house obviously has one there zero so everything else same with lamb zeros for everything else if you've seen one hot encoding before similar set up to a computer convolutional neural networks and then word embeddings is probably one of my favorites actually it's it maps the dimensionality of the relationships between these two words so if you see here woman and queen are fairly close because a queen is a woman right and man and queen a man man and woman are related because they're both humans and different sexes but man and king a closer than woman in queen or as close as woman in queen so that's like working out the different relationships between the words and then you can go further on by using models like word to Vic to really turn them into numbers by creating a a word vector using some networks here by feeding in a word at the start we're getting too deep on this stuff guys if you want to check it out I'd highly suggest looking up some blog post on word to Victoire glove I believe is is another major one which uses co-occurrence probabilities so for example given the context solid how likely is the word ice to appear versus the word steam and now if you think if you're given the context solid the word ice is going to appear much more than the word steam so as always what it's trying to do what we want to do here with the feature extraction is get that extract data out of text because computers they don't speak like us we are we are diverse creatures we can say whatever we want we can we can even say if we wanted to so they have to extract the features out extract the patterns out what it is is it's just like a convolutional neural network or computer vision task it's it's drilling down getting the features out how are these words related to each other how can we turn them into numbers and how can we eventually model them to work out something significant which is what tomorrow's class will be on which is modeling and then treated myself with a little video here I'll put the link in the description which is MIT self-driving cars course Sascha nailed I believe arnout director of engineering at way Moe and that's a really cool video so it's a great insight into how to build a production or than exactly how to build it or what it takes to run a production level AI driven get it AI driven did you get that pun ai driven company which is really cool and I think way mo is now there they've had four million purely self driven miles I think that's incredible like no one no one in the driver seat like the car is driving itself I've one can't wait for self-driving cars can you put a little comment below if you think self-driving cars are a good thing I'm not the biggest fan of driving I don't and getting driven other places I love over I think self-driving cars are gonna be really cool when they come about before we get to this week's shout outs and wrap up learning and television is 23 I want to tell you a quick story of something that happened yesterday let me let me just sit this down sit this down and get on I should I do it on a squat or a kneeling down and let's do a meeting down on one okay so what happened well can you even see any problem maybe I shouldn't lean down I'll get the chair you probably couldn't see me about oh well so yesterday or a couple of days ago last week actually I went for lunch with my godfather who used to work for LinkedIn so that's a really good contact to have and oh well I love him so it's a really good relationship to have regardless of where he worked I went to lunch with him and he was meeting up with someone and he said give give me a second all I'll introduce you to to this person I'm meeting and so long story short we were we got into talking and I just he was asking me questions about what I'm doing I told him I'm studying hey I I told him I've I've done a bit of web development in the past with my own website and social media marketing and whatnot just little little bits and pieces long story short I think I've said that twice already so trying to make this long story even shorter he wanted to meet up with me and we met up yesterday and we had a great conversation we had lunch and essentially it was just talking about what I'm studying right now and what I what my ideas are in terms of where AI is going so I think the lesson in that guys is that if you are studying AI just be aware that this is a massively growing field and there are lots of people that will want to share your knowledge and at least if if you all sort of I'm still new to the field right and so what I'm trying to say is don't be afraid to talk about what you're learning and I think there there can be some imposter syndrome and I get that as well I've had the benefit of having a lot of practice driving uber telling people what I study every week so I see someone and I they ask me what do I study and I say artificial intelligence even though it's I'm still about only six to twelve months into this field at the start it was hard for me to tell people that because I felt like I wasn't the right person or I wasn't capable or I wasn't really a developer it does take a lot of practice and even me someone who I look I appear put on these videos and trust me I that's how I am in nature but it still takes a lot of practice to be able to break into that and break through that barrier of an imposter syndrome what I can say is that if you've ever wrote in the line of code if you've ever doubled in AI if you've ever watched a deep learning lecture you're you're in the field don't be afraid to have a conversation with someone about AI and I'm gonna the the follow-on from this is that I'm going to meet up with this person in the future and keep discussing the stuff that I'm learning that's that's what these things can learn to it just to lead to sorry just having a conversation with someone you never know what they're who they are or what they're into they might not be into AI at all but that's okay you probably won't be into half the stuff they're into they've takeaway message from this is relationships are key in whatever field you're in and as AI starts to get more and more popular it's it's fundamentally important that we all start talking about it more and more and that way we can share our knowledge and that way we can we can all help to build a better world and make sure the I don't know the dystopian world that some people think of AI is going to take over because I think it's it's such a great field to be in and I thank you all for watching watching my videos and together along with everyone else in this community and on the internet and everywhere else we can build something great that's a quick little story don't forget relationships and don't forget to speak your truth let's get to some shout outs of the week whoa okay guys so these people either reached out to me by YouTube comment email Twitter wherever you can contact me on the Internet any way you like Daniel out mr. Deever calm a ke w WM s to deburr calm or Twitter at mrs. evoke otherwise leave a comment everyone else can see it that's probably the best place to do it sorrow but and now this is a reply to the question I had last week which was what are your thoughts on the singularity will we reach our official terminal intelligence by 2045 sir I hope I'm saying your name right sarva sorrow sorrow sorrow I think it's Arab sir great answer on last week's singularity question and it is true the amount of data that we're getting these days is expanding dramatically and computing power is only getting more and more accessible and that's what allows like people like you and me we can work on the latest frontiers in AI from our bedrooms and we have an internet connection that's I think that's that's amazing and it's only gonna get this we couldn't have done this stuff five five years ago five ten years ago so I think it's only going to get more and more prevalent in the society well in in the world really this data data flowing out everywhere and I can only imagine I'm sure you can too what's going to happen by 2045 so maybe not that date specifically but just within the next 20 30 50 year time frame it's going to be crazy guys I think we're gonna be talking to our children when we're 40 50 60 years old 70 years old 80 years old being like we had to use our iPhones to to connect the Internet who knows what it's going to be like in the future thank you sir up from the YouTube comments base as well we have Deepak so Deepak thank you so much for your comment and thank you for your kind words on my deep learning know every review video and as for your question versus the ml nano machine learning nano degree on Udacity and the deep learning nano degree on Udacity I had a look quickly I haven't done the machine learning nano degree but what it seems is it's a bit more broader in terms of scope in the the world of machine learning it does have some deep learning projects in there and it is a longer time frame so the deep-learning nanodegree goes for four months whereas the machine learning now a degree goes for six months no II know you messed up my fingers there and it's it is it costs a little bit more so you will look at some machine learning stuff versus to end deep learning machine learning stuff machine learning content as well as deep learning content in the machine learning area whereas the deep learning a degree is solely focused on deep learning what are the two major differences between them while machine learning is its deep learning a quite similar but deep learning you do need a lot more data that's how I think of it d for deep learning stands for data deep learning you can never have enough data you just keep throwing it at it back to what we just said before so much data is becoming available that's why deep learning is taking off so if you're looking at problems that you want to work on that have a lot of data available I would say look at the deep learning 9 degree first maybe but if you want to get into the general machine learning maybe you don't have as much data for the problems you want to solve machine learning maybe somewhere you want to look into this week's question of the week now in light of the way my video that I shared throughout the throughout this episode what is your favorite AI company and why Wei Mon looks really cool I love their love their philosophy I love their attack and everything I love I love Google's approach to almost any problem they just they just get stuff done comment below what's your favorite AI company and why and I'll shout out the best comment in next week's video and thank you again to survive for your comment on last week's question of the week next week I got invited to a Google cloud event so there's going to be a bunch of Google cloud platform trainers coming to Brisbane and they invited me along to come and check out what to build next with Google cloud platform so I'm really excited for that I'll bring the camera along I'll film what I can I'll write about what I can so so be sure to tune into next week's video I'll put something there maybe it'll be a separate video who knows I'll try and work out how to best share with you guys otherwise we're always gonna be jumping following a machine of the machine the AI master's degree next week continuing on with the natural language processing classes in the AI nanodegree thank you so much for watching and keep learning you you