Difference between revisions of "Machine Learning Resources"

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* [https://medium.com/airbnb-engineering/learning-market-dynamics-for-optimal-pricing-97cffbcc53e3 ML & Pricing Dynamics of Homes]
 
* [https://medium.com/airbnb-engineering/learning-market-dynamics-for-optimal-pricing-97cffbcc53e3 ML & Pricing Dynamics of Homes]
 
* [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]
 
* [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]

Revision as of 08:09, 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