论文笔记系列-Speeding Up Automatic Hyperparameter Optimization of Deep Neural Networks by Extrapolation of...
I. 背景介绍 1. 学习曲线(Learning Curve) 我们都知道在手工调试模型的参数的时候，我们并不会每次都等到模型迭代完后再修改超参数，而是待模型训练了一定的epoch次数后，通过观察学习曲线(learning curve, lc) 来判断是否有必要继续训练下去。那什么是学习曲线呢？主要分为两类： 1.模型性能是训练时间或者迭代次数的函数：performance=f(time) 或 p
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相关文章
- 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. 课程二(Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization)，第三周（Hype
- 5. Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization--笔记（第一周）
- 6. 【论文阅读笔记】Ristretto： Hardware-Oriented Approximation of Convolutional Neural Networks
- 7. 论文笔记：Automatic generation of HCCA-resistant scalar multiplication algorithm by proper sequencing of
- 8. [论文笔记] [2010] Understanding the Difficulty of Training Deep Feedforward Neural Networks
- 9. [论文笔记]Relay Backpropagation for Effective Learning of Deep Convolutional Neural Networks
- 10. Exploring the teaching of deep learning in neural networks