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9 Cool Deep Learning Applications | Two Minute Papers #35

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Two Minute Papers

Machine learning provides us an incredible set of tools. If you have a difficult problem at hand, you don’t need to hand craft an algorithm for it. It finds out by itself what is important about the problem and tries to solve it on its own. In this video, you’ll see a number of incredible applications of different machine learning techniques (neural networks, deep learning, convolutional neural networks and more).

Note: the fluid simulation paper is using regression forests, which is a machine learning technique, but not strictly deep learning. There are variants of it that are though (e.g., Deep Neural Decision Forests).
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The paper “Toxicity Prediction using Deep Learning” and “Prediction of human population responses to toxic compounds by a collaborative competition” are available here:
http://arxiv.org/pdf/1503.01445.pdf
http://www.nature.com/nbt/journal/v33/n9/full/nbt.3299.html

The paper “A Comparison of Algorithms and Humans For Mitosis Detection” is available here:
http://people.idsia.ch/~juergen/deeplearningwinsMICCAIgrandchallenge.html
http://people.idsia.ch/~ciresan/data/isbi2014.pdf

Kaggle-related things:
http://kaggle.com
https://www.kaggle.com/c/dato-native
http://blog.kaggle.com/2015/12/03/dato-winners-interview-1st-place-mad-professors/

The paper “Deep AutoRegressive Networks” is available here:
http://arxiv.org/pdf/1310.8499v2.pdf
https://www.youtube.com/watch?v=-yX1SYeDHbg&feature=youtu.be&t=2976

The furniture completion paper, “Data-driven Structural Priors for Shape Completion” is available here:
http://cs.stanford.edu/~mhsung/projects/structure-completion

Data-driven fluid simulations using regression forests:
https://graphics.ethz.ch/~sobarbar/papers/Lad15/DatadrivenFluids.mov
https://www.inf.ethz.ch/personal/ladickyl/fluid_sigasia15.pdf

Selfies and convolutional neural networks:
http://karpathy.github.io/2015/10/25/selfie/

Multiagent Cooperation and Competition with Deep Reinforcement Learning:
http://arxiv.org/abs/1511.08779
https://www.youtube.com/watch?v=Gb9DprIgdGw&index=2&list=PLfLv_F3r0TwyaZPe50OOUx8tRf0HwdR_u
https://github.com/NeuroCSUT/DeepMind-Atari-Deep-Q-Learner-2Player

Kaggle automatic essay scoring contest:
https://www.kaggle.com/c/asap-aes
http://www.vikparuchuri.com/blog/on-the-automated-scoring-of-essays/

Great talks on Kaggle:
https://www.youtube.com/watch?v=9Zag7uhjdYo
https://www.youtube.com/watch?v=OKOlO9nIHUE
https://www.youtube.com/watch?v=R9QxucPzicQ

The thumbnail image was created by Barn Images – https://flic.kr/p/xxBc94

Subscribe if you would like to see more of these! – http://www.youtube.com/subscription_center?add_user=keeroyz

Splash screen/thumbnail design: Felícia Fehér – http://felicia.hu

Károly Zsolnai-Fehér’s links:
Patreon → https://www.patreon.com/TwoMinutePapers
Facebook → https://www.facebook.com/TwoMinutePapers/
Twitter → https://twitter.com/karoly_zsolnai
Web → https://cg.tuwien.ac.at/~zsolnai/

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25 thoughts on “9 Cool Deep Learning Applications | Two Minute Papers #35”

  5. Lots of interesting videos Károly! Do you actually read all those papers and digest them before sharing them with us? If so, how do you do it so fast (based on the number of videos you upload, I assume you read them fast)?

  6. Thank you for continuing to make these videos. Right now I'm a student just starting to study to become a data scientist and your videos inspire me and make me excited about the future. Keep up the good work!

  7. great video! while here with the graphics nerds: one thing i have never been able to wrap my head around is spherical harmonics lighting. can't find anyone who will explain the concept cleanly. without giving me a calculus paper. anyone know where to start?

  8. exciting times indeed. cant wait for personal assisting Deep learnig Application like in the movie Her. What is your prediction about that ? And what do you think about Ray Kurzweils prophecies ?

  9. I'm an UG student, graduating couple months later. I have been following the Deep Learning hype train for the past few months.
    All I can do is CNN at this moment, using python. Eager to learn RNN and other aspects of deep learning after graduation. 🙂

    Can you provide name for two books (one mathematically rigorous and another easy to under stand) for deep learning/machine learning purposes.

    My UG study was focused on Signal Processing and Communication Engineering. I'm thinking about moving on to Machine Learning (Probably Computer Science) for MS and/or PhD. How sound is this idea?

  11. Great video! I am a mechanical engineering undergrad who is going to transition into computer science in order to work on machine learning. Your videos inspire me A LOT! Just keep up your work so that I can be constantly aware of the frontier of the field. I am currently enhancing my knowledge and hopefully will be accepted by a grad school in the future. Fight on!!

  16. Hello ^^ I have 2 questions:
    1) If Károly Zoslnai-Fehér is your full name (correct?), can I just call you Károly?
    2) Are you aware of any ongoing research on a bi-directional language translator? Something that may work as a real time chat application that translates the input text of 2 users that type in 2 different languages?

  22. I used to think all of this was exciting in the best possible sense of the word but now I'm having second thoughts thinking about what all could go wrong with this technology and it seems really terrifying instead.

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