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fchollet's profile
François Chollet
François Chollet
François Chollet
Verified account
@fchollet

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François CholletVerified account

@fchollet

Deep learning @google. Creator of Keras. Author of 'Deep Learning with Python'. Opinions are my own.

United States
fchollet.com
Joined August 2009

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    1. hardmaru‏ @hardmaru 8 Feb 2018

      hardmaru Retweeted Denny Britz

      Implementing fully connected nets, convnets, RNNs, backprop and SGD from scratch (using pure python, numpy, or even JS) and training these models on small datasets is a great way to learn how neural nets work. Invest time to gain valuable intuition before jumping onto frameworks.https://twitter.com/dennybritz/status/961829329985400839 …

      hardmaru added,

      Denny Britz @dennybritz
      A group of experienced researchers trying to fit a simple XOR function with a Deep Neural Net and a grid search over super complex activation functions and optimizers in PyTorch. No success. Of course, vanilla numpy w/o fancy tricks works just fine. 😂 https://discuss.pytorch.org/t/unable-to-learn-xor-representation-using-2-layers-of-multi-layered-perceptron-mlp/13287/18 …
      Show this thread
      8 replies 89 retweets 473 likes
      Show this thread
    2. François Chollet‏Verified account @fchollet 7 Mar 2018
      Replying to @hardmaru

      Implementing neural nets teaches you how to implement neural nets. It gives you an algorithmic understanding of how they work. It doesn't teach you what they do, or what they can or can't achieve. To learn that, you should apply them on a range of real problems (not XOR/MNIST)

      2 replies 10 retweets 54 likes
    3. François Chollet‏Verified account @fchollet 7 Mar 2018
      Replying to @fchollet @hardmaru

      Knowing the algorithm != Knowing how/why it works. My recommendation: do Kaggle competitions, and use more than neural nets. Use many different ML models. Make it visual. Plot your features.

      1 reply 21 retweets 85 likes
      François Chollet‏Verified account @fchollet 7 Mar 2018
      Replying to @fchollet @hardmaru

      It's perhaps clearer with a simpler example: implementing SVD teaches you how to implement SVD. Once you're done, you still don't know what it does. Using SVD on various datasets and plotting & using the results is what gives you intuition about SVD. Also, learning math

      6:34 AM - 7 Mar 2018
      • 4 Retweets
      • 27 Likes
      • Pham Minh Jayakrishna Rudra Alejandro Vidal J.P. McCarthy Ertugrul Bayraktar swann Jesper @ 🏡 Yash Katariya Samir
      2 replies 4 retweets 27 likes
        1. New conversation
        1. François Chollet‏Verified account @fchollet 7 Mar 2018
          Replying to @fchollet @hardmaru

          Grad students knew how to implement neural nets in C in 2000. And they didn't have good intuition about them. A high school student playing with NN frameworks in 2018 can develop stronger understanding of NNs in a matter of days -- just thanks to a better application context

          7 replies 29 retweets 125 likes
        2. Kyle Kastner‏ @kastnerkyle 7 Mar 2018
          Replying to @fchollet @hardmaru

          Understanding is great, until you hit a bug. Going down to the bottom is a reminder of how many *implementation details* actually matter. Random seeds, tie breaking in max-pool, whether the gradient is correct (eps tolerance can still be broken when chained), others.

          1 reply 1 retweet 7 likes
        3. Show replies
        1. Satish Shankar‏ @mailshanx 7 Mar 2018
          Replying to @fchollet @hardmaru

          That is indeed a compelling argument for moving up the stack. However, there might be a cognitive bias in play here - if you have, at some point in the past, understood lin. alg. / NNs under the hood, the application based intuition just “clicks” and becomes a reflexive skill.

          0 replies 0 retweets 0 likes
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