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With unbalanced outcome distribution, which ML classifier performs better? Any tradeoff?
A curated and opinionated list of resources for Chief Technology Officers, with the emphasis on startups
Qdrant - High-performance, massive-scale Vector Database and Vector Search Engine for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.
Machine learning, in numpy
Gorse open source recommender system engine
A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.
Context-Free Grammar Parser
Postgres with GPUs for ML/AI apps.
Contains Solutions and Notes for the Machine Learning Specialization By Stanford University and Deeplearning.ai - Coursera (2022) by Prof. Andrew NG
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