I’m Naresh. Started working as Data Scientist. I have completed the Machine Learning and Deep Learning Specialization by Andrew Ng and have worked around 5 lectures in Fast AI. I found this course to be fantastic and made me get up to speed in machine learning. I have posted my understandings on machine learning as a blog in nareshr8.github.io . I hope I could get in thick with Machine Learning and Artificial Intelligence by completing the courses.
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I’m Brian, currently working as a machine learning engineer. My experience in deep learning came from taking version 2 (both part 1 and 2) of this course. I have a background in math and healthcare informatics. I’m currently working on content-based collaborative filtering problems, while on the side working on translating a few benchmark datasets to Kenyan languages, starting with Swahili. e.g. I was inspired by the DEViSE paper to look into image captioning, and I’m going to translate a subset of the MS-COCO dataset to Swahili.
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I’m Andrii(from Ukraine). I’m working with ML/DL and already have some successful project done, but I’m still strugling with going from “modify a little existing solution” to “create your own from scratch”.
I’ve also created offline AI club for people that wants to learn and apply AI.
My main area of interes is CV and image generation. One of the nice projects I’ve done is translating summer photos to winter photos. If you have something to say about those two topics - feel free to contact me anytime.
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Hi! I am an NLP researcher. My main interests are distributed word representations, language modelling and dialogue systems.
I am very glad to be joining this course with all the people around the world crazy deep learning. I hope this will be a fascinating learning experience for all of us, and thank you, Jeremy and fast.ai team, for providing this opportunity!
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I’m Virgil from Timisoara, Romania. I’m an ASIC design engineer converted to ML/DL. I have done version 1 of the two fast.ai DL courses in spring/summer 2017 and have been working since then on my own DL projects + doing contractor work in ML&DL.
I’ve come back here to learn the latest tricks from the fastai framework, hone my pytorch skills and try to give something back to this amazing project & community. I’m setting up a study group in my city and telling everyone they should do the fast.ai courses right now !
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I am Akshay and I work as data science engineer (currently in NYC). My most of the experience is in healthcare/life-science domain and I believe deep learning could be literally a life-saver in the industry. One idea I have is to create a clinicaltrials.gov parser using NLP which can help patients find relevant clinical trials without much hustle. Looking forward to learn from everyone and contribute to the community.
In case you are based in NYC hope to meet you in person at NYC Study Group.
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Hi,
I’m really looking forward to joining this course from Geneva, Switzerland, even if it means a weekly red-eye at 2:30 local time.
After having written fiction for German TV (screenplays for series & TV movies) for the past 20+ years and continuing to do so, I have found in Python something that writing fiction totally lacks: an immediate feedback to one’s efforts. Either a line of code works or it doesn’t. If it doesn’t, you know immediately (no room for doubt), you go back and try to improve. Repeat. Very gratifying!
The intersection between my prof. background and deep learning that I’d like to explore:
I have long been fascinated by how dialogue works. Most deep learning interest in dialogue and dialogue generation (chatbots) seems to concentrate on customer relations - it’s all about trying to converge on a common goal: the algorithm should understand/parse what the customer wants, and the customer should receive a satisfactory answer.
But in fiction, dialogue is interesting as long as it DOESN’T converge too quickly on a resolution of tension. Conflict needs to be sustained & fanned, not reduced. In what ways could such a kind of dialogue (which is adversarial, not cooperative in nature) be generated with the help of an NLP algorithm? I’d like to find out, even if it’s in a very basic, sandboxy way.
So far, I have succesfully preprocessed a couple hundred screenplays scraped form the web, parsing them into structured JSON objects. Done very basic experiments, like approximative (and not too successfull) topic identification, TF-IDF. Looking forward to play around with this dataset.
My first goal (and maybe the right course project for me) would be to try to get an RNN to do the parsing part - nonstandard screenplay formatting keeps my Python code’s parsing success rate at measly 30% so far.
I’m very curious about getting to understand how ULMFIT transfer learning and the fastai 1.0 library’s NLP parts work. And I am thrilled and awed that people from such diverse backgrounds and their different projects are part of the fast.ai community.
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I’m Tr, I’m a PhD Student on telecommunication. But I don’t feel so good on what I’m doing now. I don’t really like the topic and also feel tired with the working environment. It’s lack of communication and team work, sometime I feel so lonely that I am going alone on this path. I have strong base in math, and last year I started to learn Machine learning and realized that I like it so much. It combines math and programming that what I want to do. I took both machine learning and deep learning MOOC from Andrew NG. But with Fast.ai, it really attracts me. The instructors, Jeremy and Rachel are really role model. They are so free, open-minded and concentrate on the real values but not the labels. They taught even some basics programming tips that are very helpful. From Rachel, her post on What You Need to Know Before Considering a PhD, motivates me to follow what really have meaning to me. Thank you a lot.
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Hi, my name is Fawaz (from Saudi Arabia), I took Udacity deep learning nano degree back in April 2018. I did the first two lectures from Fast.ai part 1 and I am super impressed by the result I achieved using Inception_4 on very few currency images (100% accuracy). I am interested on building an Arabic toxic content detector, and I am interested in content curation using collaborative filtering.
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Hi, I am Vijay Narayanan. I am Super excited to be a part of this course. I have been a part of the version 2 of the course via fellowship. I want to pursue ML/DL and be a good practitioner in the same. I work as a Presales Manager in a Software MNC. Hope to learn a lot from the course and community. I wish to contribute back as well along the way.
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Hi, I’m from China and have been watching fast. ai since 2017.Jeremy’s top-down teaching method and excellent course notes attracted me, which enabled me to learn and improve in practice。thank Jeremy and fast.ai team for providing this opportunity! I believe fast. ai will get better and better.
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My name is Sudarshan. I’m doing my PhD in Computer Science. My research involves using ML and DL on electronic health records (EHR) for developing systems for clinical decision making. I hope to graduate next year and want to contribute to healthcare through DL and AI.
I’ve been following FastAI since the begining of this year and have found Jeremy’s teachings very helpful in getting a grasp of practical DL which heavily supplemented the knowledge I gained from reading papers and books.
My objectives for this course are:
- Gain practical knowledge of how to use the new FastAI library.
- Get access to latest DL research and understand them easily.
- Get help in implementing certain aspects of my research (through the course and the forums).
Thank you.
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I am Shreyans ( from Bangalore,India) i have done my Master thesis in Deep Learning related to NLP. Currently i am working as Machine Learning Engineer in a MNC. I am also an avid data science competition participant currently hold the tittle of Kaggle Competition Expert (among top 1.5% Kagglers). My objective behind joining this course is to enhance my knowledge and be better at ML/DL. Looking forward for a fascinating learning experience and contribute back.
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Hi,
I am a professional Python developer who mostly spends time with data processing and data visualizing projects. I’ve completed Part 1 and working on Part 2 right now. Learning PyTorch, and trying to re-implement some of fast.ai models “manually”. I have experience with theoretical and classical machine learning as well (scikit-learn), including classical readings like An Introduction to Statistical Learning and Learning from Data.
Eventually, would like to start working on Reinforcement Learning projects and robotics. Not that much interested in data science competitions =) However, very interested in Computer Vision, and don’t mind to take part in data science team. Have an undergraduate degree in software engineering, and finished a couple of Udacity nanodegrees (MLND and AIND). As well as several MOOCs on Coursera and edX.
Have some (very humble) experience in contributing to open-source projects and want to contribute more. Right now trying to implement and train RNN-based language model with PyTorch, and apply it to custom dataset classification. (Something similar to lecture 10).
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Hehe, I am in UTC+5, and was thinking that 6:30 local time would be too early 
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Hi, I am Shreyas from Pune, India.I am a Software Developer. I have done ML course by Andrew Ng from Coursera. I started doing fast.ai courses since the start of this year. I have completed part 1 of deep learning fast.ai. Looking forward to dive deep into fast.ai and deep learning and applying the concepts to other data sets and start participating in Kaggle competitions. I am looking forward to learn from all the other students! Transfer learning is a great tool!
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Hi, I am Mathu from Chennai, India. I work as a Data Scientist. Have learnt and used the classical Machine Learning algorithms and am eager to learn Deep Learning. Mostly interested in using Deep Learning for NLP and structured data. I am looking forward to learning and sharing with you all.
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I am a professional Java developer from Bangalore, India with 13 years of product development experience in networking/telecom industry. Very recently, I have started working as a Machine Learning Engineer for an organization in the energy sector building ML pipeline and models for predictive maintenance use cases.
Three years back, I started my Machine Learning journey with Andrew NG’s Machine Learning course. Since then, I have completed several courses including (almost) an year long, on-the-job training program by my previous employer (Cisco). I have learned data science in the traditional way (Learn Math first and then statistics and then ML and then build things…). I did that as I was familiar with that academic methodology and since internet told me to do so (back in 2015). But over time, I started to feel that I should have done it in the reverse order. That is when I got to know about fastai and the whole approach of learning. I went through several videos from the ML courses at the beginning of this year. That is helping me at my work right now.
With the knowledge acquired from this course I want to:
- Apply DL on tabular, time series data (at work).
- Apply NLP on Indian Languages [Bengali (my mother tongue) to start with]
In between, I had the opportunity to volunteer for multiple Indian, non-profit organizations helping them solve their Data Science related problems (as a volunteer of DataKind, Bangalore). It was a very fulfilling experience. I am already 38, but I am still hopeful that at some point of my life, I will be able to use technology for the improvement of ‘not so privileged’ people around the world.
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Hi, I am Amil, a radiologist from California. I have been doing a little programming for the past 20+ year as a hobby. I started becoming interested in deep learning after participating in the RSNA Bone Age competition in 2017. I have been using NLP in my research for several years and I am interested in applying deep learning for classification of medical texts and radiology reports. I also want to use deep learning for classification and segmentation of radiological images. I watched fast.ai v2 course, and after practicing the first 3 lectures, I was able to participate in the ImageCLEF 2018 Tuberculosis - MDR detection task and achieve the best accuracy. I want to learn what the new Fast.ai V1 library offers.
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Hi, I’m Jan from South Africa. I have a background in statistics but spent most of my last years at university teaching myself deep learning and coding, mostly through the previous versions of this course.
Currently working for a machine learning consultancy and have used DL to solve many of the problems (vision, text and tabular). Looking forward to take a deep dive into the new library. Hope to apply it to problems in the audio domain. Would be cool to build an audio version of the language model.
Thanks for this opportunity.
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