Difference between revisions of "Benchmarks"
| Line 8: | Line 8: | ||
[http://www.google.com/search?q=Benchmark+machine+learning+model ...Google search] | [http://www.google.com/search?q=Benchmark+machine+learning+model ...Google search] | ||
| − | * [[Evaluation Measures - Classification Performance]] - Accuracy, Precision & Recall (Sensitivity), and Specificity | + | * [[Evaluation Measures - Classification Performance]] - [[Evaluation Measures - Classification Performance#Accuracy|Accuracy]], [[Evaluation Measures - Classification Performance#Precision & Recall (Sensitivity)|Precision & Recall (Sensitivity)]], and [[Evaluation Measures - Classification Performance#Specificity|Specificity]] |
* [[Datasets]] | * [[Datasets]] | ||
* [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 | ||
| + | https://www.aitrends.com/wp-content/uploads/2018/05/6-1MachineLearning-6.jpg | ||
<youtube>YygGzfkhtJc</youtube> | <youtube>YygGzfkhtJc</youtube> | ||
<youtube>hQRBLW6giRc</youtube> | <youtube>hQRBLW6giRc</youtube> | ||
<youtube>WlXhpXv9kDU</youtube> | <youtube>WlXhpXv9kDU</youtube> | ||
| − | <youtube> | + | <youtube>wpQiEHYkBys</youtube> |
| + | |||
== GLUE == | == GLUE == | ||
Revision as of 10:37, 24 December 2019
YouTube search... ...Google search
- Evaluation Measures - Classification Performance - Accuracy, Precision & Recall (Sensitivity), and Specificity
- Datasets
- DAWNBench | Stanford - an End-to-End Deep Learning Benchmark and Competition
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.
