CS231N-Lecture6 Training Neural Network part-2
一、概述 這一集講了一些列不同的梯度下降算法，做了一些比較。 之後，講到了Dropout機制，隨機關閉一些neuron的訓練方式可以使整個模型更加有效。 然後講到了一點關於Model Ensemble的技巧。 最後，做了一個Convnet的引入，介紹了Convnet的歷史起源，以及後人的一些成就。 二、梯度下降算法 SGD SGD(Stochastic Gradient Descent)就是之前一
相關文章
- 1. Training Recurrent Neural Network
- 2. Lecture 6 Training Neural Network (2)
- 3. Lecture 6 Training Neural Network (1)
- 4. Tips for Training Deep Neural Network
- 5. 1804.03235-Large scale distributed neural network training through online distillation.md
- 6. Principles of training multi-layer neural network using backpropagation
- 7. 【ML&DL】【skimming】Global Optimality in Neural Network Training
- 8. (zhuan) Recurrent Neural Network
- 9. Training Neural Networks, part I
- 10. Neural Network Introduction
- 更多相關文章...
- • Docker 容器連接 - Docker教程
- • Docker 容器使用 - Docker教程
- • Flink 數據傳輸及反壓詳解
- • 算法總結-廣度優先算法
相關標籤/搜索
每日一句
-
每一个你不满意的现在，都有一个你没有努力的曾经。
歡迎關注本站公眾號,獲取更多信息
相關文章
- 1. Training Recurrent Neural Network
- 2. Lecture 6 Training Neural Network (2)
- 3. Lecture 6 Training Neural Network (1)
- 4. Tips for Training Deep Neural Network
- 5. 1804.03235-Large scale distributed neural network training through online distillation.md
- 6. Principles of training multi-layer neural network using backpropagation
- 7. 【ML&DL】【skimming】Global Optimality in Neural Network Training
- 8. (zhuan) Recurrent Neural Network
- 9. Training Neural Networks, part I
- 10. Neural Network Introduction