1"use strict";(self.webpackChunk_N_E=self.webpackChunk_N_E||[]).push([[485],{485:function(e){e.exports=JSON.parse('{"topicCode":"hyperopt","title":"Hyperopt","description":"Bayesian optimization","type":"tool","category":"library","level":"intermediate","estimatedHours":8,"parent":null,"order":72,"isActive":true,"slug":"hyperopt","color":"#3B82F6","lastUpdated":"2025-07-06","metadata":{"toolType":"library","vendor":"Community","license":"Open Source","primaryLanguage":"Python","currentVersion":"1.0.0"},"content":{"overview":"Hyperopt is a tool for bayesian optimization","keyFeatures":["Hyperopt capabilities","Easy integration","Good documentation"],"installation":{"pip":"pip install hyperopt","conda":"conda install hyperopt"},"gettingStarted":{"importStatement":"import hyperopt","basicExample":"# Basic Hyperopt example"},"commonUseCases":["Hyperopt applications","Real-world scenarios","Industry use cases"],"ecosystem":[]},"searchKeywords":["hyperopt","tool","library"],"dataSourceConfigs":{"papers":{"searchQueries":["Hyperopt tutorial","Hyperopt survey"],"platforms":["arxiv","googlescholar"],"filters":{"tags":["tutorial","survey"],"yearRange":"2020-2025"}},"tutorials":{"searchQueries":["Hyperopt beginner","Hyperopt guide"],"platforms":["youtube","coursera"],"filters":{"level":"intermediate","hasCode":true}},"documentation":{"searchQueries":["Hyperopt docs"],"sections":["tutorials","api","examples"]}}}')}}]);
Line numbers count LF bytes from the start of the resource, as the search results do. Vendor segments are library code the classifier recognised; they are stored but not indexed. Bytes are shown as Latin1 characters, one per byte.