Machine & Deep Learning Compendium

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Benchmarking

Algorithms

- 1.βscikit 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."

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

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**A great overview of NN type****s, 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.βMulti-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 taskβs 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.βRuder 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."

Last modified 5mo ago

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Algorithms