Difference between revisions of "Benchmarks"
| Line 14: | Line 14: | ||
* [http://towardsdatascience.com/benchmarking-simple-machine-learning-models-with-feature-extraction-against-modern-black-box-80af734b31cc Benchmarking simple models with feature extraction against modern black-box methods | Martin Dittgen - Towards Data Science] | * [http://towardsdatascience.com/benchmarking-simple-machine-learning-models-with-feature-extraction-against-modern-black-box-80af734b31cc Benchmarking simple models with feature extraction against modern black-box methods | Martin Dittgen - Towards Data Science] | ||
* [http://dawn.cs.stanford.edu//benchmark/index.html DAWNBench | Stanford] - an End-to-End Deep Learning Benchmark and Competition | * [http://dawn.cs.stanford.edu//benchmark/index.html DAWNBench | Stanford] - an End-to-End Deep Learning Benchmark and Competition | ||
| − | + | * [http://www.datasciencecentral.com/group/resources/forum/topics/benchmarking-20-machine-learning-models-accuracy-and-speed Benchmarking 20 Machine Learning Models Accuracy and Speed | Marc Borowczak - Data Science Central] | |
<img src="http://www.researchgate.net/profile/Benoit_Gallix/publication/324457640/figure/fig1/AS:622298201595905@1525378861825/Graph-illustrating-the-impact-of-data-available-on-performance-of-traditional-machine.png" width="500" height="400"> | <img src="http://www.researchgate.net/profile/Benoit_Gallix/publication/324457640/figure/fig1/AS:622298201595905@1525378861825/Graph-illustrating-the-impact-of-data-available-on-performance-of-traditional-machine.png" width="500" height="400"> | ||
Revision as of 12:40, 24 December 2019
YouTube search... ...Google search
- Evaluation Measures - Classification Performance - Accuracy, Precision & Recall (Sensitivity), and Specificity
- Datasets
- Gaming
- Machine Learning Benchmarks and AI Self-Driving Cars | Lance Eliot - AItrends
- Benchmarking simple models with feature extraction against modern black-box methods | Martin Dittgen - Towards Data Science
- DAWNBench | Stanford - an End-to-End Deep Learning Benchmark and Competition
- Benchmarking 20 Machine Learning Models Accuracy and Speed | Marc Borowczak - Data Science Central
GLUE
The General Language Understanding Evaluation (GLUE) benchmark is a collection of resources for training, evaluating, and analyzing natural language understanding systems. GLUE consists of: A benchmark of nine sentence- or sentence-pair language understanding tasks built on established existing datasets and selected to cover a diverse range of dataset sizes, text genres, and degrees of difficulty, A diagnostic dataset designed to evaluate and analyze model performance with respect to a wide range of linguistic phenomena found in natural language, and A public leaderboard for tracking performance on the benchmark and a dashboard for visualizing the performance of models on the diagnostic set.
