Difference between revisions of "Machine Learning Resources"

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Key Frameworks:
 
Key Frameworks:
 
* [https://www.tensorflow.org/beta Tensorflow (beta 2.0)]
 
* [https://www.tensorflow.org/beta Tensorflow (beta 2.0)]
* [https://pytorch.org/docs/stable/torch.html Pytorch]
 
* [https://scikit-learn.org/stable/ Scikit-learn]
 
 
* [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:
 
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)'.
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Commonly used ranking metrics like ''Mean Reciprocal Rank (MRR)'' and ''Normalized Discounted Cumulative Gain (NDCG)''.
'Multi-item (also known as groupwise) scoring functions'.
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''Multi-item (also known as groupwise) scoring functions''.
'LambdaLoss' implementation for direct ranking metric optimization.
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''LambdaLoss'' implementation for direct ranking metric optimization.
 
Unbiased Learning-to-Rank from biased feedback data.)
 
Unbiased Learning-to-Rank from biased feedback data.)
  
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* [https://pytorch.org/docs/stable/torch.html Pytorch]
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* [https://scikit-learn.org/stable/ Scikit-learn]
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* [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]
 
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* [https://github.com/Microsoft/gated-graph-neural-network-samples Gated Graph Neural Networks Microsoft]
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* [https://autokeras.com/ AutoKeras]
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* [https://github.com/keras-rl/keras-rl Keras-RL]
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* [https://github.com/combust/mleap mleap]
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* [https://github.com/mlflow/mlflow/blob/master/docs/source/models.rst MLflow models]
 
==Neural Network Interpretability==
 
==Neural Network Interpretability==
 
*[https://github.com/tensorflow/lucid Lucid on tensorflow]
 
*[https://github.com/tensorflow/lucid Lucid on tensorflow]
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==Transfer Learning==
 
==Transfer Learning==
 
* [https://github.com/WillKoehrsen/pytorch_challenge/blob/master/Transfer%20Learning%20in%20PyTorch.ipynb PyTorch image classification using VGG-16]
 
* [https://github.com/WillKoehrsen/pytorch_challenge/blob/master/Transfer%20Learning%20in%20PyTorch.ipynb PyTorch image classification using VGG-16]
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==Object Recognition In Images==
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* [https://github.com/OlafenwaMoses/ImageAI Image AI] ()
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* [https://towardsdatascience.com/object-detection-and-tracking-in-pytorch-b3cf1a696a98 Object detection PyTorch]
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* [https://github.com/ayooshkathuria/pytorch-yolo-v3 Yolo_v3 Pytorch Implementation]
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==Articles==
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* [https://towardsdatascience.com/introducing-tf-ranking-f94433c33ff Introduction to Tensorflow Ranking]
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* [https://medium.com/expedia-group-tech/real-time-serving-machine-learning-models-with-mleap-151b39dfc3d7 Mlleap usage]
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* [https://github.com/combust/mleap Converting Create & Export Spark Pipeline]
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* [https://medium.com/airbnb-engineering/learning-market-dynamics-for-optimal-pricing-97cffbcc53e3 ML & Pricing Dynamics of Homes]
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* [https://medium.com/airbnb-engineering/listing-embeddings-for-similar-listing-recommendations-and-real-time-personalization-in-search-601172f7603e List embeddings for recommendation & real time personalisation in search ranking]
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* [https://eng.lyft.com/empowering-personalized-marketing-with-machine-learning-fd36e6bdeca6 ML applications in personalised marketing]

Latest revision as of 08:29, 14 July 2019

Key Frameworks:

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.)

Neural Network Interpretability


Python Notebook Examples

Image Quality Assessment


Transfer Learning


Object Recognition In Images

Articles