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{ "category": "SciPy 2013", "language": "English", "slug": "hyperopt-a-python-library-for-optimizing-machine-1", "speakers": [], "tags": [ "Tech" ], "id": 2125, "state": 1, "title": "Hyperopt: A Python library for optimizing machine learning algorithms; SciPy 2013", "summary": "Hyperopt: A Python library for optimizing the hyperparameters of machine learning algorithms\n\nAuthors: Bergstra, James, University of Waterloo; Yamins, Dan, Massachusetts Institute of Technology; Cox, David D., Harvard University\n\nTrack: Machine Learning\n\nMost machine learning algorithms have hyperparameters that have a great impact on end-to-end system performance, and adjusting hyperparameters to optimize end-to-end performance can be a daunting task. Hyperparameters come in many varieties--continuous-valued ones with and without bounds, discrete ones that are either ordered or not, and conditional ones that do not even always apply (e.g., the parameters of an optional pre-processing stage)--so conventional continuous and combinatorial optimization algorithms either do not directly apply, or else operate without leveraging structure in the search space. Typically, the optimization of hyperparameters is carried out before-hand by domain experts on unrelated problems, or manually for the problem at hand with the assistance of grid search. However, even random search has been shown to be competitive [1].\n\nBetter hyperparameter optimization algorithms (HOAs) are needed for two reasons:\n\nHOAs formalize the practice of model evaluation, so that benchmarking experiments can be reproduced by different people.\n\nLearning algorithm designers can deliver flexible fully-configurable implementations (of e.g. Deep Learning algorithms) to non-experts, so long as they also provide a corresponding HOA.\n\nHyperopt provides serial and parallelizable HOAs via a Python library [2, 3]. Fundamental to its design is a protocol for communication between (a) the description of a hyperparameter search space, (b) a hyperparameter evaluation function (machine learning system), and (c) a hyperparameter search algorithm. This protocol makes it possible to make generic HOAs (such as the bundled \"TPE\" algorithm) work for a range of specific search problems. Specific machine learning algorithms (or algorithm families) are implemented as hyperopt search spaces in related projects: Deep Belief Networks [4], convolutional vision architectures [5], and scikit-learn classifiers [6]. My presentation will explain what problem hyperopt solves, how to use it, and how it can deliver accurate models from data alone, without operator intervention.", "description": "", "quality_notes": "", "copyright_text": "", "embed": "<object width=\"640\" height=\"390\"><param name=\"movie\" value=\";hl=en_US\"></param><param name=\"allowFullScreen\" value=\"true\"></param><param name=\"allowscriptaccess\" value=\"always\"></param><embed src=\";hl=en_US\" type=\"application/x-shockwave-flash\" width=\"640\" height=\"390\" allowscriptaccess=\"always\" allowfullscreen=\"true\"></embed></object>", "thumbnail_url": "", "duration": null, "video_ogv_length": null, "video_ogv_url": null, "video_ogv_download_only": false, "video_mp4_length": null, "video_mp4_url": null, "video_mp4_download_only": false, "video_webm_length": null, "video_webm_url": null, "video_webm_download_only": false, "video_flv_length": null, "video_flv_url": null, "video_flv_download_only": false, "source_url": "", "whiteboard": "needs editing", "recorded": "2013-07-01", "added": "2013-07-04T10:08:58", "updated": "2014-04-08T20:28:26.403" }