- A Purely Functional Typed Approach to Trainable Models (Differentiable Programming in Haskell) https://blog.jle.im/entry/purely-functional-typed-models-1.html 5 comments programming
- A Purely Functional Typed Approach to Trainable Models (Differentiable Programming in Haskell) https://blog.jle.im/entry/purely-functional-typed-models-1.html 26 comments haskell
Linking pages
- Machine Learning: The Great Stagnation - by Mark Saroufim https://marksaroufim.substack.com/p/machine-learning-the-great-stagnation 218 comments
- The Const Applicative and Monoids · in Code https://blog.jle.im/entry/const-applicative-and-monoids.html 13 comments
- Hasktorch v0.0.1. Originally published at stites.io on… | by Sam Stites | Medium https://medium.com/@stites/hasktorch-v0-0-1-28d9ab270f3f 10 comments
- Can I use Deep Learning for that? | by Mark Saroufim | Medium https://medium.com/@marksaroufim/can-deep-learning-solve-my-problem-a-type-theoretic-heuristic-e57f4d1658f 9 comments
- GitHub - mstksg/backprop: Heterogeneous automatic differentiation ("backpropagation") in Haskell https://github.com/mstksg/backprop 7 comments
Linked pages
- TensorFlow http://tensorflow.org/ 440 comments
- Facebook https://www.facebook.com/yann.lecun/posts/10155003011462143 106 comments
- Neural Networks, Types, and Functional Programming -- colah's blog http://colah.github.io/posts/2015-09-NN-Types-FP/ 80 comments
- Currying - Wikipedia https://en.wikipedia.org/wiki/Currying#Contrast_with_partial_function_application 62 comments
- reddit.com: Anmelden http://reddit.com/submit 54 comments
- Practical Dependent Types in Haskell: Type-Safe Neural Networks (Part 1) · in Code https://blog.jle.im/entry/practical-dependent-types-in-haskell-1.html 32 comments
- The Const Applicative and Monoids · in Code https://blog.jle.im/entry/const-applicative-and-monoids.html 13 comments
- Logistic regression - Wikipedia http://en.wikipedia.org/wiki/Logistic_regression#Model_accuracy 8 comments
- backprop: Heterogeneous automatic differentation http://hackage.haskell.org/package/backprop 5 comments
- Caffe | Deep Learning Framework http://caffe.berkeleyvision.org/ 3 comments
- Logical conjunction - Wikipedia https://en.wikipedia.org/wiki/Logical_conjunction 1 comment
- DIY Deep Learning for Vision: a Hands-On Tutorial with Caffe - Google Präsentationen https://docs.google.com/presentation/d/1UeKXVgRvvxg9OUdh_UiC5G71UMscNPlvArsWER41PsU/edit#slide=id.gc2fcdcce7_216_128 0 comments
- GitHub - HIPS/autograd: Efficiently computes derivatives of numpy code. https://github.com/HIPS/autograd 0 comments
- Linear regression - Wikipedia https://en.wikipedia.org/wiki/Linear_regression 0 comments
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