# Machine Learning Resources

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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.)

- Pytorch
- Scikit-learn
- Scikit-Image
- Deep learning Databricks
- Gated Graph Neural Networks Microsoft
- AutoKeras
- Keras-RL
- mleap
- MLflow models

## Contents

## Neural Network Interpretability

## Python Notebook Examples

## Image Quality Assessment

## Transfer Learning