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 proceedings.neurips.cc
Algorithms for Hyper-Parameter Optimization
| | www.mit.edu
Workshop on Algorithms with Predictions
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| | papers.nips.cc
Algorithms for Hyper-Parameter Optimization
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| | [AI summary] This article details new hyper-parameter optimization algorithms for training neural networks and deep belief networks presented at NeurIPS 2011.
| | tomaugspurger.net
Distributed Auto-ML with TPOT with Dask | Tom's Blog
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| | This work is supported by Anaconda Inc. This post describes a recent improvement made to TPOT. TPOT is an automated machine learning library for Python. It does some feature engineering and hyper-parameter optimization for you. TPOT uses genetic algorithms to evaluate which models are performing well and how to choose new models to try out in the next generation. Parallelizing TPOT In TPOT-730, we made some modifications to TPOT to support distributed training. As a TPOT user, the only changes you need to make to your code are
| | wandb.ai
What is an ML Model Registry? - Weights & Biases
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| Learn why maintaining a dynamic ML model registry for collaborative teams is a best practice in machine learning and how to create one using Weights & Biases.