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EvalML: a library for automated machine learning and model understanding

EvalML is an AutoML library that builds, optimizes, and evaluates machine learning pipelines using domain-specific objective functions, it is a library for automated machine learning (AutoML) and model understanding, written in Python

16 de May de 20212 de April de 2021

Machine Learning From Scratch

An extensive list of fundamental machine learning models and algorithms from scratch in vanilla Python.

30 de May de 2020

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• Vector-Quantized Image Modeling with Improved VQGAN
  18 de May de 2022
  Posted by Jiahui Yu, Senior Research Scientist, and Jing Yu […]
• Contextual Rephrasing in Google Assistant
  17 de May de 2022
  Posted by Aurelien Boffy, Senior Staff Software Engineer, and Roberto […]
• Challenges in Multi-objective Optimization for Automatic Wireless Network Planning
  12 de May de 2022
  Posted by Sara Ahmadian and Matthew Fahrbach, Research Scientists, Google […]

• DIGITAL SME at the Data Week 2022
  20 de May de 2022
  DIGITAL SME will present at the Data Week 2022, which begins next week, by organising two workshops and participating in […] The post DIGITAL SME at the Data Week 2022 appeared first on European DIGITAL […]

• Taylor Genetic Programming for Symbolic Regression. (arXiv:2205.09751v1 [cs.NE])
  Genetic programming (GP) is a commonly used approach to solve symbolic regression (SR) problems. Compared with the machine learning or […]
• A Novel Underwater Image Enhancement and Improved Underwater Biological Detection Pipeline. (arXiv:2205.10199v1 [cs.CV])
  For aquaculture resource evaluation and ecological environment monitoring, automatic detection and identification of marine organisms is critical. However, due to […]
• Low-cost Relevance Generation and Evaluation Metrics for Entity Resolution in AI. (arXiv:2205.10298v1 [cs.IT])
  Entity Resolution (ER) in voice assistants is a prime component during run time that resolves entities in users request to […]