First, the type system: Thinc was designed for type-checking from day 1, and AFAIK it's the first library to offer detailed support for numpy's API. This helps code readability, gives editor support, and catches some bugs. 2/5
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We also have type annotations on the model --- so you can use the types to plan out your model, and again, get type errors if your wiring is wrong. This works especially well with Thinc's functional programming approach. 3/5
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Speaking of functional programming...The conceptual model behind Thinc is also something that you might find interesting in itself. It's a different way to program backprop, using callbacks instead of a tape. 4/5https://thinc.ai/docs/concept
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Finally, Thinc also offers some extremely practical features. Have a look at the config system, but also the fact that you can easily wrap other frameworks. Thinc is designed to complement other frameworks, not replace them. 5/5https://thinc.ai/docs/usage-config …
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this is so well executed
Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi
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Čini se da učitavanje traje već neko vrijeme.
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Author of the
Founder
Static type checking
Mix
Integrated config system
Extensible backends incl. JAX (experimental)
Variable-length sequences & more