(Review cs231n) Optimized Methods
Mini-batch SGD的步骤： 1.Sample a batch of data 2.Forward prop it through the graph，get loss 3.backprop to calculate the gradient 4. updata the parameters using the gradient The initialization of weights
相关文章
- 1. (Review cs231n) Object Detection
- 2. (Review cs231n) Gradient Vectorized
- 3. (Review cs231n)loss function and optimization
- 4. (Review cs231n) BN and Activation Function
- 5. A review of gradient descent optimization methods
- 6. CS231n Lecture 15 | Efficient Methods and Hardware for Deep Learning
- 7. (Review cs231n) Spatial Localization and Detection（classification and localization）
- 8. (Review cs231n) The Gradient Calculation of Neural Network
- 9. 【CRR-FMM】A Concise Review of Recent Few-shot Meta-learning Methods
- 10. solr的Optimized
- 更多相关文章...
- • Docker 安装 MySQL - Docker教程
- • R Excel 文件 - R 语言教程
相关标签/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。
欢迎关注本站公众号,获取更多信息
相关文章
- 1. (Review cs231n) Object Detection
- 2. (Review cs231n) Gradient Vectorized
- 3. (Review cs231n)loss function and optimization
- 4. (Review cs231n) BN and Activation Function
- 5. A review of gradient descent optimization methods
- 6. CS231n Lecture 15 | Efficient Methods and Hardware for Deep Learning
- 7. (Review cs231n) Spatial Localization and Detection（classification and localization）
- 8. (Review cs231n) The Gradient Calculation of Neural Network
- 9. 【CRR-FMM】A Concise Review of Recent Few-shot Meta-learning Methods
- 10. solr的Optimized