Machine Learning Weekly Review №1

Source of latest credible papers, videos and projects on machine learning for scientists and engineers.


Recommended this week

Articles

  1. Using Deep Learning to Reconstruct High-Resolution Audio
  2. Understand PyTorch code in 10 minutes

Papers

  1. “FreezeOut: Accelerate Training by Progressively Freezing Layers” yields 20% speedup without loss of accuracy for ResNet, 20% speedup with 3% loss in accuracy for DenseNets.
  2. “YellowFin and the Art of Momentum Tuning” — introduces an automatic tuner for both momentum and learning rate in SGD. Converges in fewer iterations than Adam on large ResNet and LSTM models, with a speedup up to 2.8x in synchronous and 2.7x in asynchronous settings.
  3. “Noisy Networks for Exploration” — DeepMind guys introduced NoisyNet: general RL exploration method that achieves a superhuman performance in a wide range of Atari games.
  4. “Stick-Breaking Variational Autoencoders” — Published as a conference paper at ICLR 2017.

Videos

  1. “Generative Adversarial Networks for Beginners” a practical tutorial accompanied with article and blog.
  2. “Deep Reinforcement Learning” Course Lecture Videos from UC Berkeley
  3. “DeepMind’s AI Learns Superhuman Relational Reasoning” a two-minute overview of the paper.

Projects

  1. Keras-vis — neural network visualization toolkit for keras.
  2. BayesSearchCV — notebook showcasing a new Bayesian hyperparameter search in scikit-optimize.

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ML Review

Highlights from Machine Learning Research, Projects and…

Machine Learning Digest

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A hand-curated monthly round-up of credible papers, books, videos and software related to Machine Learning: https://mlreview.com.

ML Review

ML Review

Highlights from Machine Learning Research, Projects and Learning Materials. From and For ML Scientists, Engineers an Enthusiasts.

Machine Learning Digest

Written by

A hand-curated monthly round-up of credible papers, books, videos and software related to Machine Learning: https://mlreview.com.

ML Review

ML Review

Highlights from Machine Learning Research, Projects and Learning Materials. From and For ML Scientists, Engineers an Enthusiasts.

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