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# Exemplar
1. https://ml5js.org/
1. https://www.csie.ntu.edu.tw/~cjlin/libsvm/
1. http://halide-lang.org/
# Reference
1. https://nostalgebraist.tumblr.com/post/185326092369/the-transformer-explained
1. [HyperE: Hyperbolic Embeddings for Entities](https://hazyresearch.github.io/hyperE/)
1. https://www.samcoope.com/posts/playing_around_with_noise_as_targets
1. https://lobste.rs/s/hgejxf/why_is_machine_learning_most_often
1. https://boingboing.net/2018/11/12/local-optima-r-us.html/amp
1. https://crazyoscarchang.github.io/2019/02/16/seven-myths-in-machine-learning-research/
1. https://www.c4ml.org/
1. https://medium.com/@l2k/why-are-machine-learning-projects-so-hard-to-manage-8e9b9cf49641
1. https://github.com/MikeInnes/diff-zoo
1. https://cloud.google.com/blog/products/ai-machine-learning/introducing-feast-an-open-source-feature-store-for-machine-learning
1. https://towardsdatascience.com/introducing-manifold-db9e90f20347
1. http://snap.stanford.edu/graphsage/
1. https://heartbeat.fritz.ai/capsule-networks-a-new-and-attractive-ai-architecture-bd1198cc8ad4
1. http://super-ms.mit.edu/rum.html
# Deep learning
1. https://github.com/microsoft/tensorwatch
1. https://d2l.ai/
1. https://hadrienj.github.io/posts/Deep-Learning-Book-Series-Introduction/
1. http://nlp.seas.harvard.edu/NamedTensor
1. https://tvm.ai/
1. https://machinelearningmastery.com/framework-for-better-deep-learning/
1. [Geometric Understanding of Deep Learning](https://arxiv.org/abs/1805.10451)
1. https://towardsdatascience.com/what-is-geometric-deep-learning-b2adb662d91d
1. https://deeplearning4j.org/
1. [Deep(er) learning](http://www.jneurosci.org/content/early/2018/07/13/JNEUROSCI.0153-18.2018?versioned=true)
# Neural network
1. https://github.com/BrainJS/brain.js
1. https://blog.jle.im/entry/practical-dependent-types-in-haskell-1.html
1. https://matloff.wordpress.com/2018/06/20/neural-networks-are-essentially-polynomial-regression/
1. https://www.quantamagazine.org/foundations-built-for-a-general-theory-of-neural-networks-20190131#AI
1. https://rkevingibson.github.io/blog/neural-networks-as-ordinary-differential-equations/
# Tensor
1. http://nlp.seas.harvard.edu/NamedTensor.html
1. http://nlp.seas.harvard.edu/NamedTensor2
# Meta-learning
1. https://blog.fastforwardlabs.com/2019/05/22/metalearners-learning-how-to-learn.html
1. https://www.bayeswatch.com/2018/11/30/HTYM/
1. https://bender.dreem.com/
# Model
1. http://onnx.ai/
# Training
1. https://ai.googleblog.com/2019/03/introducing-gpipe-open-source-library.html
# Differentiable programming
1. https://fluxml.ai/2019/02/07/what-is-differentiable-programming.html
1. https://github.com/breandan/kotlingrad
1. https://colinraffel.com/blog/you-don-t-know-jax.html
1. https://github.com/tensorflow/mlir
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