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January 08, 06:47

A 2017 ML/DS year in review by some venerable / random authors:

- Proper year review by WildML (!!!) -

-- Includes a lot of links and proper materials

-- AlphaGo

-- Attention

-- RL and genetic algorithm renaissance

-- Pytorch - elephant in the room, TF and others


-- Medicine

-- GANs

If I had to summarize 2017 in one sentence, it would be the year of frameworks. Facebook made a big splash with PyTorch. Due to its dynamic graph construction similar to what Chainer offers, PyTorch received much love from researchers in Natural Language Processing, who regularly have to deal with dynamic and recurrent structures that hard to declare in a static graph frameworks such as Tensorflow.

Tensorflow had quite a run in 2017. Tensorflow 1.0 with a stable and backwards-compatible API was released in February. Currently, Tensorflow is at version 1.4.1. In addition to the main framework, several Tensorflow companion libraries were released, including Tensorflow Fold for dynamic computation graphs, Tensorflow Transform for data input pipelines, and DeepMind’s higher-level Sonnet library. The Tensorflow team also announced a new eager execution mode which works similar to PyTorch’s dynamic computation graphs.

In addition to Google and Facebook, many other companies jumped on the Machine Learning framework bandwagon:

- Apple announced its CoreML mobile machine learning library.

- A team at Uber released Pyro, a Deep Probabilistic Programming Language.

- Amazon announced Gluon, a higher-level API available in MXNet.

- Uber released details about its internal Michelangelo Machine Learning infrastructure platform.

- And because the number of framework is getting out of hand, Facebook and Microsoft announced the ONNX open format to share deep learning models across frameworks. For example, you may train your model in one framework, but then serve it in production in another one.- In Russian - - kind of meh review (source -

- Amazing 2017 article about global AI trends -

- Uber engineering highlights -




AI and Deep Learning in 2017 – A Year in Review

The year is coming to an end. I did not write nearly as much as I had planned to. But I’m hoping to change that next year, with more tutorials around Reinforcement Learning, Evolution, and Ba…