Ensembles
Numpy Blas:
    1.
    ​Another comparison​
    2.
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GLUE:
    1.
    ​Glue / super glue ​
State of the art in AI:
    1.
    In terms of domain X datasets​
Cloud providers:
Datasets:
Hardware:
Platforms
Algorithms:
    ​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.
NLP

Multi-Task Learning

    1.
    ​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.
    ​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."
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