Bayesian optimisation for smart hyperparameter search Fitting a single classifier does not take long, fitting hundreds takes a while. To find the best hyperparameters you need to fit a lot of classifiers. What to do? This post explores the inner work…

State of Hyperparameter Selection DANIEL SALTIEL VIEW NOTEBOOK Historically hyperparameter determination has been a woefully forgotten aspect of machine learning. With the rise of neural nets - which require more hyperparameters, more precisely tuned…

How to Evaluate Machine Learning Models, Part 4: Hyperparameter Tuning In the realm of machine learning, hyperparameter tuning is a “meta” learning task. It happens to be one of my favorite subjects because it can appear like black magic, yet its sec…

Illustrated: Efficient Neural Architecture Search --- Guide on macro and micro search strategies in ENAS 2019-03-27 09:41:07 This blog is copied from: https://towardsdatascience.com/illustrated-efficient-neural-architecture-search-5f7387f9fb6 Designi…

Research Guide for Neural Architecture Search 2019-09-19 09:29:04 This blog is from: https://heartbeat.fritz.ai/research-guide-for-neural-architecture-search-b250c5b1b2e5 From training to experimenting with different parameters, the process of design…

About this Course This course will teach you the "magic" of getting deep learning to work well. Rather than the deep learning process being a black box, you will understand what drives performance, and be able to more systematically get good res…

NAS(神经结构搜索)综述 文章转载自:http://www.tensorinfinity.com/paper_136.html 本文是对神经结构搜索(NAS)的简单综述,在写作的过程中参考了文献[1]列出的部分文献.深度学习技术发展日新月异,市面的书很难跟上时代的步伐,本人希望写出一本内容经典.新颖的机器学习教材,此文是对<机器学习与应用>,清华大学出版社,雷明著一书的补充.该书目前已经重印了3次,收到了不少读者的反馈,对于之前已经发现的笔误和印刷错误,在刚印刷出的这一版中已经做了校正,我会…

https://blog.csdn.net/y80gDg1/article/details/81463731 感谢阅读腾讯AI Lab微信号第34篇文章.当地时间 7 月 10-15 日,第 35 届国际机器学习会议(ICML 2018)在瑞典斯德哥尔摩成功举办.ICML 2018 所接收的论文的研究主题非常多样,涵盖深度学习模型/架构/理论.强化学习.优化方法.在线学习.生成模型.迁移学习与多任务学习.隐私与安全等,在本文中,腾讯 AI Lab 的研究者结合自身的研究重心和研究兴趣对部分 IC…

原文:NeurIPS 2018 | 腾讯AI Lab详解3大热点:模型压缩.机器学习及最优化算法 导读 AI领域顶会NeurIPS正在加拿大蒙特利尔举办.本文针对实验室关注的几个研究热点,模型压缩.自动机器学习.机器学习与最优化算法,选取23篇会议上入选的重点论文进行分析解读,与大家分享.Enjoy! NeurIPS (Conference on Neural Information Processing Systems,神经信息处理系统进展大会)与ICML并称为神经计算和机器学习领域两大顶级学…

本文为Awesome-AutoML-Papers的译文. 1.AutoML简介 Machine Learning几年来取得的不少可观的成绩,越来越多的学科都依赖于它.然而,这些成果都很大程度上取决于人类机器学习专家来完成如下工作: 数据预处理 Preprocess the data 选择合适的特征 Select appropriate features 选择合适的模型族 Select an appropriate model family 优化模型参数 Optimize model hyperp…