This new file format is new, so please try it out, and if you run into any issues, please report them on GitHub! In the coming months, we will make this format the new default for any file with the `.keras` extension.
@fchollet
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Keras Lookup Layers Store Vocabulary as Inspectable Plaintext Files
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…as well as custom assets written by layers that manage non-numerical state, such as vocabulary text files. Keras lookup layers store their vocabulary files as plaintext, so you can inspect and reuse them in a new context.
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White-box model format: JSON config and weights in ZIP archive
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The format is entirely whitebox — the file saved is a zip archive that you can inspect. It contains the config of the model as a human-readable JSON file (a description of the architecture and hyperparameters of the model), a weights file (essentially a dict of arrays)…
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New Keras Model Saving Format Available in TensorFlow Nightly
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Announcement: the new Keras model saving format is available in tf-nightly. This format is aimed at reloading the exact same Python object, in a safe way (it does not rely on bytecode or pickling at all). In the future, the TF SavedModel format will be for inference-only export.
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AI-Generated Text: Will It Reduce or Increase Verbal Excess?
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Best case scenario: automated wordy BS generation nudges humans towards dispensing with wordy BS entirely. People refocus on actionable thinking and concise communication. Worst case scenario: the world is flooded with a lot more wordy BS. (This is more plausible.)
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Biological Plausibility of Models: From LSTMs to Transformers
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When people bring up how the human brain works just like a Transformer, I remember those simpler times when people argued for the biological plausibility of LSTM (back when a stack of LSTM layers trained with deep RL seemed like the golden path to AGI). https://arxiv.org/abs/1604.06635
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Favorite MLOps and hosted ML tools discussion
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What are your favorite MLOps tools or hosted ML tools, and why?
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Learning and Refining Semantic Categories from Single Instances
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Learn a word based on a single instance (e.g. this particular airplane = word "airplane"), then use the word to refer to a broad category of related things (all flying things are now airplanes), then gradually refine semantic categories (helicopters, dirigibles…)
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Large language models cannot provide sustainable competitive advantage
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And in the event you find a niche use case where the curve *on its own* is valuable, you should expect that use case will get commoditized in short order. A big curve trained on public data can never be a moat, no matter how large the dataset / how big the curve.
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Self-Supervised Learning Models Need Strong Applications to Create Value
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I'm bullish on leveraging interpolative manifolds trained on mountains of self-supervised data (big curves) to build valuable applications. But you should expect the app will do most of the valuable lifting. A curve on its own is a bit like a database with no app attached to it.