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論文筆記系列-Speeding Up Automatic Hyperparameter Optimization of Deep Neural Networks by Extrapolation of...
時間 2020-12-27
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I. 背景介紹 1. 學習曲線(Learning Curve) 我們都知道在手工調試模型的參數的時候,我們並不會每次都等到模型迭代完後再修改超參數,而是待模型訓練了一定的epoch次數後,通過觀察學習曲線(learning curve, lc) 來判斷是否有必要繼續訓練下去。那什麼是學習曲線呢?主要分爲兩類: 1.模型性能是訓練時間或者迭代次數的函數:performance=f(time) 或 p
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相關文章
1.
[Topic Discussion] Hyperparameter Optimization for Neural Networks
2.
On the Optimization of Deep Networks: Implicit Acceleration by Overparameterization
3.
COURSE 2 Improving Deep Neural Networks Hyperparameter tuning, Regularization and Optimization
4.
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Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization--筆記(第一週)
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【論文閱讀筆記】Ristretto: Hardware-Oriented Approximation of Convolutional Neural Networks
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論文筆記:Automatic generation of HCCA-resistant scalar multiplication algorithm by proper sequencing of
8.
[論文筆記] [2010] Understanding the Difficulty of Training Deep Feedforward Neural Networks
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10.
Exploring the teaching of deep learning in neural networks
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