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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. François Chollet‏Verified account @fchollet 11 Mar 2019

      12) To build deep learning models, you don't have to use object-oriented programming all the time. All layers we've seen so far can also be composed functionally, like this (we call it the "Functional API"):pic.twitter.com/OohI9IZlQ5

      1 reply 12 retweets 60 likes
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    2. François Chollet‏Verified account @fchollet 11 Mar 2019

      The Functional API tends to be more concise than subclassing, & provides a few other advantages (generally the same advantages that functional, typed languages provide over untyped OO development). Learn more about the Functional API: https://www.tensorflow.org/alpha/guide/keras/functional …

      1 reply 8 retweets 60 likes
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    3. François Chollet‏Verified account @fchollet 11 Mar 2019

      However, note that the Functional API can only be used to define DAGs of layers -- recursive networks should be defined as `Layer` subclasses instead. In your research workflows, you may often find yourself mix-and-matching OO models and Functional models.

      2 replies 5 retweets 33 likes
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    4. François Chollet‏Verified account @fchollet 11 Mar 2019

      That's all you need to get started with reimplementing most deep learning research papers in TensorFlow 2.0 and Keras! Now let's check out a really quick example: hypernetworks.

      1 reply 7 retweets 44 likes
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    5. François Chollet‏Verified account @fchollet 11 Mar 2019

      A hypernetwork is a deep neural network whose weights are generated by another network (usually smaller). Let's implement a really trivial hypernetwork: we'll take the `Linear` layer we defined earlier, and we'll use it to generate the weights of... another `Linear` layer.pic.twitter.com/11HjEvBBkh

      4 replies 23 retweets 126 likes
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    6. François Chollet‏Verified account @fchollet 11 Mar 2019

      Another quick example: implementing a VAE in either style, either subclassing (left) or the Functional API (right). I've posted this before. Find what works best for you!pic.twitter.com/3xUliC3nFb

      3 replies 16 retweets 109 likes
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    7. François Chollet‏Verified account @fchollet 11 Mar 2019

      This is the end of this thread. Play with these code examples in this Colab notebook: https://colab.research.google.com/drive/17u-pRZJnKN0gO5XZmq8n5A2bKGrfKEUg … 🦄🚀

      19 replies 51 retweets 287 likes
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    8. Mo‏ @mamadlin 11 Mar 2019
      Replying to @fchollet

      This is great summary. Thanks. Not totally related, but version curious if you have plans to put an updated version of the book "Deep learning with Python" with TF 2.x updates?

      1 reply 0 retweets 0 likes
    9. François Chollet‏Verified account @fchollet 11 Mar 2019
      Replying to @mamadlin

      Yes, there will be a 2nd edition (probably by late 2019)

      6 replies 1 retweet 13 likes
    10. Patrick Gray‏ @clifgray 19 Dec 2019
      Replying to @fchollet @mamadlin

      Hey @fchollet, looking for holiday present ideas (and a text for teaching this coming year), do you have any updated estimate on the 2nd edition?

      1 reply 0 retweets 0 likes
      François Chollet‏Verified account @fchollet 19 Dec 2019
      Replying to @clifgray @mamadlin

      Yeah, this was over-optimistic. It will be out some time in 2020.

      10:17 AM - 19 Dec 2019
      • 5 Likes
      • Vinh pravinks Daniel O'Connor Picaro Byte Patrick Gray
      1 reply 0 retweets 5 likes
        1. Daniel O'Connor‏ @Singularitarian 16 Jan 2020
          Replying to @fchollet @clifgray @mamadlin

          I suppose there's not a preliminary version of the 2nd edition that I could use when teaching deep learning and tensorflow this semester?

          0 replies 0 retweets 0 likes
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