Welcome @lrajlich!
Thinking about doing something with word vectors on the Aozora Bunko corpus. Nothing too specific yet. 
Any input is appreciated! 
Welcome!@lrajlich I would be interested in your thought on how deep learning will help startup founder as most of the startup donāt have the data to start with. Would love to know your perspectives.
Thank for the intro @jeremy and thanks @zaoyang for the intro here. Happy to be a late comer, I want to get my hands dirty with deep learning and building models. I donāt have an extremely specific objective other than to learn how to practically apply deep learning and Iāll go from there once I have more experience.
Iāve had a long passion for analytical work and some experience experience - first job out of college was working on quant trading strategies at a hedge fund and I built a rather janky analytics system to measure cohort retention in R ā this was pre mixpanel or other SaaS analytics providers at my startup with Zao.
Thanks @KevinB! @anandsaha you jest, but maybe ;)ā¦ @atulkum - it probably depends on what the startup is doing but if you plan to build features to run models against user input data, then you could possibly train a model on publicly available data? Also having some friendlies who fit your customer profile and are willing to share data with you would be a huge help to get the ball rolling.
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Hi All,
I have been meaning to introduce myself properly here on the forums so sorry this is a little late
A little bit about meā¦Iām an entrepreneur with a business degree and non-technical background. How did I get into DL? Well the story is that my business was really struggling and I didnāt have many options left (I have 2 kids so a lot of pressure ), so I figured maybe if I pick up some more technical skills such as coding then I could come up with some solutions. Long story short is I ended up in the Deep Learning and AI nanodegrees at Udacity. Our fellow fastai students @Ekami and @apil.tamang were actually my classmates!
Since I donāt have any formal CS degree and at the time I was just getting started coding so it was a little daunting and overwhelming at first, but I found through a lot of repetition things started to slowly make sense. Also, it was while I was enrolled with Udacity that I discovered fast.ai part 1 MOOC videos and fell in love with the course and the teaching style. It really helped me to understand the underlying concepts of DL much more clearly. It was also fastai that motivated me to participate in my first Kaggle competition āPlanet: Understanding the Amazon from Spaceā along with @Ekami as teammates hahaā¦Now I am officially addicted - currently participating in 10 active competitions atm!
I also wanted to take this opportunity to wholeheartedly thank @jeremy and @rachel for making this course (my heart goes out to Rachel - hope she is on a speedy path to recovery!). Not only is the education top-notch but also I have found that fastai seems to attract some really amazing people from all over the world and the sense of community here is above any that I have experienced in other courses. I feel so fortunate to be here amongst all of you!
-James
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@jamesrequa
Great introduction.
I appreciate your enterpreneurship (i can never spell this word right), and definitely admire how far you mightāve come. Youāre definitely very active at the forums and such.
Iām a backend Java developer, and Python is (at times) very annoying. I hate it that I cannot always tell what kind of objects are being passed to a method because of the lack of static typing support for Python. I still loose my way through the ā*ā operator, the zip(ā¦), stack( ), lambdas, iter, next, and list operators, to name a few. Although now with a bit of getting used to, I feel better at it and definitely see the utility of those shorthand type operators.
Iām not sure what I hoped to get out of this program, but would like to be better at PyTorch, or so I thought. Although Iāve been trying to interface better with the fastai library, I hope to write some more native PyTorch code going forward. For right now, two evenings later and Iām still struggling to come up with the final piece for the planet kaggle competition to generate the submission file. Now if I could only understand what the opt_th(ā¦) method does, and how to leverage thatā¦
Good to share and know you all. Keep posting. I wish I could spend more time on this matter, and build and train SeNet, Capsule Nets etc. a full time job keeps me pretty much on the edge most days.
@jeremy @rachel and everyone who contributed to the fastai lib. and videos, really appreciate the effort and opportunity.
- Apil
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Firstly, thanks @jeremy and @rachel (wishing you speedy recovery!) for creating this course!
I am working from last ~20 years in variety of fields from transistor level simulations to big data application. Recently, I am working in Big Data Application Performance Management domain. I really like the top-down approach to teaching otherwise had to spend 5-10 years learning lots of theory before trying on DL! I am a visual learner, so like when things are explained in easy to understand visual way vs lots of equations and numbers.
I would like to use the techniques learned to do timeseries anomaly, and prediction. @jeremy please send me some pointers.
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Hi I am from Hong Kong. I have been watching videos for Part 1 and Part 2 v1. I start learning ML and DL last year from MOOC and gained some experience from Kaggle. I am planning to take it seriously and study v2 line by line.
Iām just joining today now that the version 2 of part 1 has been unofficially released.
Iām a business analyst working in agile ways with cultural non-profit professionals to light their ādark dataā in order to serve their patrons in better ways.
Iām interested in recommender systems when it comes to working with my cultural non-profit colleagues. On a personal basis as a digital photographer / image-maker, Iām particularly interested in the style transfer section of part 2.
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Hi everyone,
Happy New Year!
I am from Singapore. My background is in software engineering. I quit my job early of Dec last year to focus on my study. I am currently transitioning to be a deep learning engineer at a startup working on medical diagnostic. Prior to that, I was working full-time in a local consulting company providing web and mobile app development services to our clients around the world. I am an āexpertā generalist and self learner/life-long learner.
I first got to know deep learning when I visited Stanford CS231n website where thereās a demo model training live in the web browser. Out of curiosity, I dig deeper and found it was created by Andrej Karpathy who was at that time the co-lecturer for CS231n. So, I started learning neural network in JavaScript through that demo. Strangely, I donāt understand a thing about neural network at that point of time. So, I keep looking and discovered Andrew Ngās Stanford Machine Learning course and I completed it about 2 years ago. This give me enough grounding to study CS231n course where we learn to develop deep learning model for Convolutional Neural Network (CNN) from scratch by hand using just Python and numpy.
When Part 1 v1 of the course just released to the public, I took the chance to study and completed it. I didnāt manage to complete Part 2 v1 due to my full-time job obligations. With the āsoft-launchā of Part 1 v2 and in conjunction to Nurture.aiās AI Saturdays global initiatives kicking off this week, I am planning to re-take Part 1 v2 seriously together with some local study groups here. I think itās worth doing. On top of that, I think we can learn PyTorch and the new fastai library. What not to love. 
Thank you to Jeremy and Rachel for the awesome course. 
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Hello, everyone and happy New year. Well Iām from France, with a background in econometrics and applied mathematics in the decision theory field. I work in the consulting industry for aboutā¦two decades now and came along this course from class-central.com Iām currently finishing the Deeplearning.Ai specialization and felt quite interested in learning how to deploy and execute models on the cloud, as well as crafting new implementations-models in other fields than strictly computer vision (even though image classifying is not per se a āvisionā problem), mainly decision making and forecasting.
Like Cedric Iād like to thank Rachel and Jeremy for the great work and awesome course here.
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Hi everyone, my name is Arib from Indonesia, not found any Indonesian before, just one Malaysian (hi neighbor), please come forward fellow Indonesian if you guys read this.
I start Deep learning 1 year ago, in the big data class when I get to pair up with 1 Ph.D. guy from Pakistan (heās not continuing this pursue sadly), he is the one who told me about deep learning and Tensorflow. We end up presenting our final project using Tensorflow. I get hooked up with deep learning, taking Andrew Ng class, taking formal machine learning class in school (Iām taking master in computer science at Shanghai, China), following another professor weekly lab meeting that work in deep learning, since my lab is about embedded & pervasive computing, following Stanford Conv Network online class, get stuck in implementation, found fast.ai. After a moment with fast.ai bought GeForce 750 Ti with pocket money (Iām a poor student), it so sucks, now working on my proposal to get my sponsor fund me buying 1080 Ti 
I implement simple computer vision in agriculture field in Indonesia to detect paddy pest and disease, get flight ticket back home from biggest telco company in Indonesia to join their social competition, that really simple idea that just runs on simple 750 Ti considered as quite a novel idea for my country, oh my, I get tons of idea ready to execute in more powerful machine, wish me luck with my sponsor fund. Thanks, @jeremy and @rachel without you guys Iām still hanging around with no clear direction how to implement the idea, haha.
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Hello All,
Iām really excited to (re)start this course. I had just finished week 1 of v1 and took a little break over the holidays. I was surprised to see that there is a v2 and was glad to have taken a little break.
I majored in CS, but aside from 1 year of coding custom middleware components for banking web-enablement early on in my career, I had not had a technical role. My experience has mainly been in supply chain management (SCM) as a functional consultant and solution architect.
I recently finished the Coursera Machine Learning Specialization from the University of Washington. It was supposed to have a deep learning component which was unfortunately dropped from the specialization (course not yet developed). I really liked the set of courses with its case study based approach and was happy to find fast.ai and its approach to teaching to start my education in deep learning.
Iām still exploring applications of deep learning in my domain. So far, my list is composed of user activities that require decisions and manual intervention using current process (e.g., promotional activity classification, finding commonalities in items so that they can be managed together, what to do when there is not enough information such as when forecasting new items, identifying and managing exceptions like when a stock-out is projected or when demand changes drastically, etc.). Iām hoping going through these lessons will also help me see new ways of thinking about current SCM processes - perhaps a paradigm shift in how forecasts and replenishment plans are generated and managed or how supply chains will be orchestrated. Iāve seen other posts here that are related and Iām sure deep learning will be a big part of the next generation supply chain (if it isnāt already).
I liked reading about the different backgrounds of the learners here - so much important work to be done and discoveries to be made. Many thanks to @jeremy and @rachel and others for making this course available and making deep learning accessible to all.
Looking forward to learning!
-Ed
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Hi everyone,
Iām a software developer from Italy.
Iām grateful to @jeremy and @rachel for this course, and Iām also grateful to the community members for their support.
I had my first exposure to ML about two years ago, when working on a project that required a recommendation system (we used Apache PredictionIO with a customized recommendation engine template, based on a collaborative filtering algorithm).
Deep Learning is uncharted terrain for me, and I felt intimidated by the heavy mathematical background which is considered to be a prerequisite for even the simplest tasks, but the top-down approach on which the course is based is really a great idea and makes learning feasible and interesting.
Even though I canāt commit myself fully to the course due to work/family duties, I hope Iāll be able to learn as much as possible and give back too.
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Hi all
I always wanted to be a magician. Sounds naive but is true. As I grew up I was looking for real magic. To me software engineering sounded like magic. Creating something and having it work was magic for me. That got me into software. After I understood programming Machine learning sounded like magic.
I started learning with ML in mid-2014. That was just after I got my first job. I was trying out Andrew Ngās course. Got bored. Stopped that. Tried again in mid-2015. Same thing happened. At the end of 2015 the CTO of the project that I was working for (a person whose experience is same as my whole age) said that we want to start with ML. He shared some coursera courses and said he would love people to jump in and do it. I thought that was the best thing ever as now I had the opportunity to align my work with this. So I tried and completed some of the courses. They were in R. They were better but still not so great. Then in beginning of 2016 I started looking and found Udacity. The Data Science Nanodegree. It had a monthly subscription and if I started doing that I knew I would have nothing left. But I decided this is important and took the step. 8 months later where I dedicated most of my spare time I completed it. It was good. The ML initiative in the project was looking bleak. But I learned.
In 2017 I found fast.ai randomly via a reddit thread. I donāt use reddit that much but it was just random that I found it. I think I was really lucky that day because it was an eye opener. I was like finally I found the magic that I was looking for. After Jeremey and Rachaelās explanation of bottom-up and top-down approach I finally understood why I was bored with the coursera courses and why I was not get anywhere. I did not complete all courses but till middle of Video 4 of Part 1 v1. But that was still great learning. Gave me a lot of confidence about things people consider magic. ML initiative in the company finally went to production. There were problems but the CEO had explicitly mentioned that exploring AI was really important to him. Others in the industry are doing it and reporting good returns so it is important. Currently working in production.
Middle of 2017 I got a chance to become Google certified Data engineer. I never would have cleared the ML/DL questions if I had not went through this course even half way. Thatās when I understood I finally found the right thing. Later I got sick for a few months so wasnāt really able to focus on anything. I started with the computational linear algebra but that is still in the early parts.
I participated in a hackathon recently. The theme was hack for india. I tried my hand but wasnāt able to get anything good for solving local issues because of lack of data. But then I remembered that Jeremy mentioning somewhere that you donāt need a lot of data to get good results. I was thinking ok maybe I should complete the course. Maybe I need to discuss data science machine learning more. Because the current government of India is organizing many hacks. There is a vision for Digital India. Maybe learning about what is possible and then going out educating others and discussing with more people is what is required for me to be able to get to solutions. Coincidentally the company that I am working for also wants to get more people into this field and as I am the one who has been working on this they asked whether I would like to help with that and I said yes.
Today I saw http://www.fast.ai/2018/01/02/diversity-2018/ and decided thatās great because international fellowship will open and I will have a deadline. I know that when I have deadlines I do great. So made a resolution to fulfill the requirements of international fellowship and get both of the courses completed by end of April.
I believe in God and I believe that he helps those who help themselves. With all these things happening one by one recently in sequence I believe he is just telling me to go for it. You know when they say God has plans for everyone.
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Hey everybody,
I started teaching myself how to program about 10 years ago while getting my BS in mining engineering. I worked at mines for a few years after college then ran the alumni community at Udacity for a couple of years and now Iām doing independent consulting back in the mining industry. Iāve done a lot of data work in the past, but the last few years had mostly been doing web work. Itās exciting to be learning something new again and has even reignited old interests in robotics and video games. Hopefully, I will get to apply machine learning to both. Cheers everyone!
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Hello, Iām Hui from Malaysia, currently working as an iOS Developer in Taiwan. AI is one of my interested field especially the capabilities of creating a lot of interesting applications. Iāve been studying through online courses, such as Coursera (ML by Professor Andrew Ng), Fast.ai (just finish v2 lesson 1), and even apply to be AI Saturdays Ambassador to host AI learning group events. This is my very first decision to jump to AI field after developing apps for 4 years, hope I can learn faster here and move to my target this year.
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Hello Hui Qin,
Welcome to the forum I am another learner from AI Saturdays Singapore. Nice to see more people from Asia here. I can relate to your decision as I also recently take the plunge. It is a big step. Cheers!
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hi everyone!
I started this course end of Oct. I had discovered this course incidently (via meetup.com) on recommendation of some other local students. But this was/is exactly what i needed. I am a developer by profession but did my BS with Maths (but it was almost 17 years ago so i have forgotten almost all of that except the reassurance in my mind that math wont kill me if i have to do/learn it again)
My first attempt to get into ML field was in beginning of 2014 when i read the the book āAn Introduction to Statistical Learning with Applications in Rā (which is a great free book btw) and then started Andrew ngās course Machine Learning at Coursera. Unfortunately course was not right for me. it started with some language called Octave and went streight to using gradient descent which i couldnt really understand. So after auditing the course i gave it up ā¦ hoping to maybe find another oppertunity or resource some other day. I think the courses at Coursera in meantime have been updated or new launched but my previous bad experience always told me in the back of my mind that it wont work for me. I saw some other online courses which were not free from other websites but having a bad experience i wasnt sure if that money would be worth it or it would work for me.
It took me another 3 years to force myself to start over again, and after revising the above mentioned book was clueless on what to do next. In the hope of building a DL foundation, i had gotten Ian Goodfellows DL and Sheldon Rossās Intro to Probablity models books but the length of equations in them were intimidating. I couldnt even really start with them. The knowledge i had gained from ISLR book wasnt enough to compete at Kaggle or do any enterprise level project.
so i am very happy to have found this course and I am absolutely loving it. I work full time so have to learn at my own pace. I try to give it around 3-4 hours a day on most days in a week.
Also i really like Jeremyās teaching style, starting with an intiutive data set of pictures (instead of with some boring decades old crime rate data) and starting from top to bottom with explaination of everything. He is teaching with heart. Thank you Jeremy and Rachel!
I hope to complete Part 1 V2 asap and look forward to the next part.
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