# Difference between revisions of "Machine Learning Resources"

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* [https://github.com/tensorflow/ranking Tensorflow Rank] (TensorFlow Ranking is a library for Learning-to-Rank (LTR) techniques on the TensorFlow platform. It contains the following components: | * [https://github.com/tensorflow/ranking Tensorflow Rank] (TensorFlow Ranking is a library for Learning-to-Rank (LTR) techniques on the TensorFlow platform. It contains the following components: | ||

Commonly used loss functions including pointwise, pairwise, and listwise losses. | Commonly used loss functions including pointwise, pairwise, and listwise losses. | ||

− | Commonly used ranking metrics like 'Mean Reciprocal Rank (MRR)' and 'Normalized Discounted Cumulative Gain (NDCG)'. | + | Commonly used ranking metrics like ''Mean Reciprocal Rank (MRR)'' and ''Normalized Discounted Cumulative Gain (NDCG)''. |

− | 'Multi-item (also known as groupwise) scoring functions'. | + | ''Multi-item (also known as groupwise) scoring functions''. |

− | 'LambdaLoss' implementation for direct ranking metric optimization. | + | ''LambdaLoss'' implementation for direct ranking metric optimization. |

Unbiased Learning-to-Rank from biased feedback data.) | Unbiased Learning-to-Rank from biased feedback data.) | ||

## Revision as of 08:19, 10 July 2019

Key Frameworks:

- Tensorflow (beta 2.0)
- Pytorch
- Scikit-learn
- Tensorflow Probability
- Tensorflow Rank (TensorFlow Ranking is a library for Learning-to-Rank (LTR) techniques on the TensorFlow platform. It contains the following components:

Commonly used loss functions including pointwise, pairwise, and listwise losses.
Commonly used ranking metrics like *Mean Reciprocal Rank (MRR)* and *Normalized Discounted Cumulative Gain (NDCG)*.
*Multi-item (also known as groupwise) scoring functions*.
*LambdaLoss* implementation for direct ranking metric optimization.
Unbiased Learning-to-Rank from biased feedback data.)

## Contents

## Neural Network Interpretability

## Python Notebook Examples

## Image Quality Assessment