Deep Learning for Nature Language Processing --- 第四讲（下）
A note on matrix implementations 将J对softmax的权重W和每个word vector进行求导： 尽量使用矩阵运算（向量化），不要使用for loop。 模型训练中有两个开销比较大的运算：矩阵乘法f=Wx和指数函数exp Softmax(=logistic regression) is not very powerful softmax只是在原来的向量空间中给出
相关文章
- 1. Deep Learning for Nature Language Processing---第四讲(上)
- 2. Deep Learning for Nature Language Processing --- 第七讲
- 3. Deep Learning for Nature Language Processing --- 第八讲
- 4. Deep Learning for Nature Language Processing --- 第六讲
- 5. Deep Learning for Nature Language Processing --- 第五讲
- 6. CS224d: Deep Learning for Natural Language Process
- 7. 【笔记】CS224N Natural Language Processing with Deep Learning
- 8. Language Understanding for TextGames using Deep Reinforcement
- 9. 李宏毅：deep learning for language modeling
- 10. Recent Trends in Deep Learning Based Natural Language Processing(arXiv)笔记
- 更多相关文章...
- • RSS
元素 - RSS 教程 - • Scala for循环 - Scala教程
- • RxJava操作符（四）Combining
- • Docker容器实战(七) - 容器眼光下的文件系统
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。
最新文章
欢迎关注本站公众号,获取更多信息
相关文章
- 1. Deep Learning for Nature Language Processing---第四讲(上)
- 2. Deep Learning for Nature Language Processing --- 第七讲
- 3. Deep Learning for Nature Language Processing --- 第八讲
- 4. Deep Learning for Nature Language Processing --- 第六讲
- 5. Deep Learning for Nature Language Processing --- 第五讲
- 6. CS224d: Deep Learning for Natural Language Process
- 7. 【笔记】CS224N Natural Language Processing with Deep Learning
- 8. Language Understanding for TextGames using Deep Reinforcement
- 9. 李宏毅：deep learning for language modeling
- 10. Recent Trends in Deep Learning Based Natural Language Processing(arXiv)笔记