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:


State of the art in AI:

  1. In terms of domain X datasets

Cloud providers:





  • Comparing accuracy, speed, memory and 2D visualization of classifiers:

SVM, k-nearest neighbors, Random Forest, AdaBoost Classifier, Gradient Boosting, Naive, Bayes, LDA, QDA, RBMs, Logistic Regression, RBM + Logistic Regression Classifier

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

  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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