# Transformer

0. []()
0. [Transformers from scratch](http://www.peterbloem.nl/blog/transformers)

# Exemplar

0. []()
0. https://ml5js.org/
0. https://www.csie.ntu.edu.tw/~cjlin/libsvm/
0. http://halide-lang.org/

# Reference

0. []()
0. [Predibase: Declarative ML](https://predibase.com/)
	0. [ludwig: Data-centric declarative deep learning framework](https://github.com/ludwig-ai/ludwig)
	0. [horovod: Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.](https://github.com/horovod/horovod)
0. [AutoML: Automated Machine Learning](https://www.automl.org/automl/)
0. [Why are ML Compilers so Hard?](https://petewarden.com/2021/12/24/why-are-ml-compilers-so-hard/)
0. ["Multi-Level Intermediate Representation" Compiler Infrastructure](https://github.com/tensorflow/mlir)
0. [Sampling can be faster than optimization](https://www.pnas.org/content/116/42/20881)
0. [Layer rotation: a surprisingly powerful indicator of generalization in deep networks](https://arxiv.org/abs/1806.01603v2)
0. https://nostalgebraist.tumblr.com/post/185326092369/the-transformer-explained
0. [HyperE: Hyperbolic Embeddings for Entities](https://hazyresearch.github.io/hyperE/)
0. https://www.samcoope.com/posts/playing_around_with_noise_as_targets
0. https://lobste.rs/s/hgejxf/why_is_machine_learning_most_often
0. https://boingboing.net/2018/11/12/local-optima-r-us.html/amp
0. https://crazyoscarchang.github.io/2019/02/16/seven-myths-in-machine-learning-research/
0. https://www.c4ml.org/
0. https://medium.com/@l2k/why-are-machine-learning-projects-so-hard-to-manage-8e9b9cf49641
0. https://github.com/MikeInnes/diff-zoo
0. https://cloud.google.com/blog/products/ai-machine-learning/introducing-feast-an-open-source-feature-store-for-machine-learning
0. https://towardsdatascience.com/introducing-manifold-db9e90f20347
0. http://snap.stanford.edu/graphsage/
0. https://heartbeat.fritz.ai/capsule-networks-a-new-and-attractive-ai-architecture-bd1198cc8ad4
0. http://super-ms.mit.edu/rum.html

# Inductive logic programming

0. []()
0. [Inductive logic programming at 30: a new introduction](https://arxiv.org/abs/2008.07912)

# Deep learning

0. []()
0. [GAME2020 4. Dr. Vincent Nozick Geometric Neurons](https://www.youtube.com/watch?v=KC3c_Mdj1dk)
0. [Evolution Strategies](https://lilianweng.github.io/lil-log/2019/09/05/evolution-strategies.html)
0. [Monadic Deep Learning: Performing monadic automatic differentiation in parallel](https://deeplearning.thoughtworks.school/assets/paper.pdf)
0. https://github.com/microsoft/tensorwatch
0. https://d2l.ai/
0. https://hadrienj.github.io/posts/Deep-Learning-Book-Series-Introduction/
0. http://nlp.seas.harvard.edu/NamedTensor
0. https://tvm.ai/
0. https://machinelearningmastery.com/framework-for-better-deep-learning/
0. [Geometric Understanding of Deep Learning](https://arxiv.org/abs/1805.10451)
0. https://towardsdatascience.com/what-is-geometric-deep-learning-b2adb662d91d
0. https://deeplearning4j.org/
0. [Deep(er) learning](http://www.jneurosci.org/content/early/2018/07/13/JNEUROSCI.0153-18.2018?versioned=true)

# Tensor

0. []()
0. http://nlp.seas.harvard.edu/NamedTensor.html
0. http://nlp.seas.harvard.edu/NamedTensor2

# Meta-learning

0. []()
0. https://blog.fastforwardlabs.com/2019/05/22/metalearners-learning-how-to-learn.html
0. https://www.bayeswatch.com/2018/11/30/HTYM/
0. https://bender.dreem.com/

# Model

0. []()
0. http://onnx.ai/

# Training

0. []()
0. https://ai.googleblog.com/2019/03/introducing-gpipe-open-source-library.html