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  1. proslijedio/la je Tweet

    If you're bored and uninspired, get to work, don't wait for inspiration to magically strike. Inspiration isn't something you receive from your surroundings, it's something you make for yourself. It comes from what you choose to think about and what you choose to do.

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  2. proslijedio/la je Tweet

    Left: MIT computer scientist Katie Bouman w/stacks of hard drives of black hole image data. Right: MIT computer scientist Margaret Hamilton w/the code she wrote that helped put a man on the moon. (image credit )

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  3. proslijedio/la je Tweet
    9. tra 2019.

    Very excited to announce a project 3 years in the making: BLUE, a low-cost, safe, capable robot designed from the ground up with AI in mind. Want BLUE? You can now sign up for priority access here: Learn more here: w/

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  4. proslijedio/la je Tweet
    28. ožu 2019.

    FOUR Productivity FEYNMAN- strategies: i) Stop trying to know-it-all. ii) Don't worry about what others are thinking. iii) Don't think about what you want to be, but what you want to do. iv) Have a sense of humor and talk honestly.

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  5. proslijedio/la je Tweet
    30. lis 2018.

    It would be great if your body could store sleep hours, like sleep for 16 hours, than you could stay up for two days without getting tired.

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  6. proslijedio/la je Tweet

    Here is the same dynamic RNN implemented in 4 different frameworks (TensorFlow/Keras, MXNet/Gluon, Chainer, PyTorch). Can you tell which is which?

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    When you use a 10 layer Deep Neural Network where Logistic Regression would suffice

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    25. srp 2018.

    If you cannot explain something in simple terms, you don't understand it.

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    21. srp 2018.

    FEYNMAN learning strategy in THREE points: 1. Continually ask "Why?" 2. When you learn something, learn it to where you can explain it to a child. 3. Instead of arbitrarily memorizing things, look for the explanation that makes it obvious.

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  10. proslijedio/la je Tweet
    22. lip 2018.

    Do you often find it cumbersome to track down the best datasets or the state-of-the-art for a particular task in NLP? I've created a resource (a GitHub repo) to make this easier.

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  11. proslijedio/la je Tweet
    14. lip 2018.
    Odgovor korisniku/ci

    Doesn't work in bay area though. Once an Uber driver asked me sth similar and I answered as I am doing statistical learning. He then asked what do you think about Hinton's Capsule Networks.

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  12. proslijedio/la je Tweet
    13. lip 2018.

    Texter is a latex symbol retriever. It is developed using which allows an offline version and a much faster prediction time then Detexify. DEMO : CODE :

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  13. proslijedio/la je Tweet

    The most successful people I've met: 1. Read constantly 2. Workout daily 3. Are innately curious 4. Have laser focus 5. Believe in themselves 6. Build incredible teams 7. Admit they know very little 8. Constantly work to improve 9. Demand excellence in everything they do

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  14. proslijedio/la je Tweet

    No matter where you are, there's always room to grow. You came this far, now it would be a shame to just stay there

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  15. proslijedio/la je Tweet

    If publishing your paper requires you to select specific ideas, architectures, and hyperparameters according to a fixed validation set, then it's not longer a validation set, it's a training set. And there are no guarantees that the selected ideas will generalize to real data.

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  16. proslijedio/la je Tweet
    4. lip 2018.

    We're thrilled to announce that we've entered into an acquisition agreement with !

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  17. proslijedio/la je Tweet
    30. svi 2018.

    The most effective engineers I’ve met in my career are those who: - choose the right problems to tackle at the right time - communicate effectively - are humble and have empathy for others

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  18. proslijedio/la je Tweet
    31. svi 2018.

    An observation about linear methods in .

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  19. proslijedio/la je Tweet

    me trying to read papers (or articles), getting lost in references and not finishing any :S

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  20. proslijedio/la je Tweet
    16. svi 2018.

    AI and Compute: Our analysis showing that the amount of compute used in the largest AI training runs has had a doubling period of 3.5 months since 2012 (net increase of 300,000x):

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