1. 1.
    scikit bench - "scikit-learn_bench benchmarks various implementations of machine learning algorithms across data analytics frameworks. It currently support the scikit-learn, DAAL4PY, cuML, and XGBoost frameworks for commonly used machine learning algorithms."
Numpy Blas:
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State of the art in AI:
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    In terms of domain X datasets
Cloud providers:
  • Comparing accuracy, speed, memory and 2D visualization of classifiers:
Scaling networks and predicting performance of NN:
  • A great overview of NN types, but the idea behind the video is to create a system that can predict train time and possibly accuracy when scaling networks using multiple GPUs, there is also a nice slide about general hardware recommendations.

Multi-Task Learning

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    Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics (Yarin Gal) GitHub - "In this paper we make the observation that the performance of such systems is strongly dependent on the relative weighting between each task’s loss. Tuning these weights by hand is a difficult and expensive process, making multi-task learning prohibitive in practice. We propose a principled approach to multi-task deep learning which weighs multiple loss functions by considering the homoscedastic uncertainty of each task. "
  2. 2.
    Ruder on Multi Task Learning - "By sharing representations between related tasks, we can enable our model to generalize better on our original task. This approach is called Multi-Task Learning (MTL) and will be the topic of this blog post."