# Difference between revisions of "Machine Learning Resources"

Jump to navigation
Jump to search

Line 1: | Line 1: | ||

Key Frameworks: | Key Frameworks: | ||

* [https://www.tensorflow.org/beta Tensorflow (beta 2.0)] | * [https://www.tensorflow.org/beta Tensorflow (beta 2.0)] | ||

− | |||

− | |||

* [https://www.tensorflow.org/probability Tensorflow Probability] | * [https://www.tensorflow.org/probability Tensorflow Probability] | ||

* [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: | ||

Line 11: | Line 9: | ||

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

+ | * [https://pytorch.org/docs/stable/torch.html Pytorch] | ||

+ | * [https://scikit-learn.org/stable/ Scikit-learn] | ||

+ | * [https://scikit-image.org/ Scikit-Image] | ||

* [https://docs.databricks.com/applications/deep-learning/index.html Deep learning Databricks] | * [https://docs.databricks.com/applications/deep-learning/index.html Deep learning Databricks] | ||

* [https://github.com/Microsoft/gated-graph-neural-network-samples Gated Graph Neural Networks Microsoft] | * [https://github.com/Microsoft/gated-graph-neural-network-samples Gated Graph Neural Networks Microsoft] |

## Revision as of 08:21, 10 July 2019

Key Frameworks:

- Tensorflow (beta 2.0)
- 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