論文筆記系列-Speeding Up Automatic Hyperparameter Optimization of Deep Neural Networks by Extrapolation of...
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. 課程二(Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization)，第三週（Hype
- 5. Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization--筆記（第一週）
- 6. 【論文閱讀筆記】Ristretto： Hardware-Oriented Approximation of Convolutional Neural Networks
- 7. 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
- 9. [論文筆記]Relay Backpropagation for Effective Learning of Deep Convolutional Neural Networks
- 10. Exploring the teaching of deep learning in neural networks