# Transformer

1. [Transformers from scratch](http://www.peterbloem.nl/blog/transformers)

# Exemplar

1. https://ml5js.org/
1. https://www.csie.ntu.edu.tw/~cjlin/libsvm/
1. http://halide-lang.org/

# Reference

1. ["Multi-Level Intermediate Representation" Compiler Infrastructure](https://github.com/tensorflow/mlir)
1. [Sampling can be faster than optimization](https://www.pnas.org/content/116/42/20881)
1. [Layer rotation: a surprisingly powerful indicator of generalization in deep networks](https://arxiv.org/abs/1806.01603v2)
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

# Inductive logic programming

1. [Inductive logic programming at 30: a new introduction](https://arxiv.org/abs/2008.07912)

# Deep learning

1. [GAME2020 4. Dr. Vincent Nozick Geometric Neurons](https://www.youtube.com/watch?v=KC3c_Mdj1dk)
1. [Evolution Strategies](https://lilianweng.github.io/lil-log/2019/09/05/evolution-strategies.html)
1. [Monadic Deep Learning: Performing monadic automatic differentiation in parallel](https://deeplearning.thoughtworks.school/assets/paper.pdf)
1. [Demystifying Differentiable Programming: Shift/Reset the Penultimate Backpropagator](https://arxiv.org/abs/1803.10228)
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. [The Taichi Programming Language](http://taichi.graphics/)
1. [The principles behind Differentiable Programming - Erik Meijer](https://www.youtube.com/watch?v=lk0PhtSHE38)
1. [Kotlin∇: Type-safe Symbolic Differentiation for Kotlin](https://github.com/breandan/kotlingrad)
1. [Differentiable Programming Manifesto](https://github.com/apple/swift/blob/master/docs/DifferentiableProgramming.md)
1. [Backpropagation in the Simply Typed Lambda-calculus with Linear Negation](https://arxiv.org/abs/1909.13768)
1. [One-and-a-Half Simple Differential Programming Languages](https://pages.cpsc.ucalgary.ca/~robin/FMCS/FMCS2019/slides/GordonPlotkin-FMCS2019.pdf)
1. [Differentiable Programming Mega-Proposal](https://forums.swift.org/t/differentiable-programming-mega-proposal/28547)
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