图 书 馆
深度学习教程A tutorial on Deep Learning |
|
| 课程网址: | http://videolectures.net/jul09_hinton_deeplearn/ |
| 主讲教师: | Geoffrey E. Hinton |
| 开课单位: | 多伦多大学 |
| 开课时间: | 2009-09-15 |
| 课程语种: | 英语 |
| 中文简介: |  可以通过组合更简单的模型来创建未标记数据的复杂概率模型。混合模型是通过对较简单模型的密度求平均值而获得的，而“专家产品”是通过将密度相乘并重新归一化而获得的。一种更强大的组合类型是通过将一个模型的潜在变量的值视为学习下一个模型的数据来形成“专家组合”。本教程的前半部分将展示如何通过构成仅具有一个隐藏层的专家模型的简单，无向，乘积来一次学习具有多个隐藏变量层的深度置信网络定向生成模型。它还将解释为什么组成定向模型不起作用。深度信念网在大型，未标记的数据集上作为生成模型进行训练，但是一旦通过无监督学习创建了多层特征，就可以对其进行微调，以对小型，标记的数据集进行出色的区分。本教程的后半部分将描述深层信任网在若干任务中的应用，包括对象识别，非线性降维，文档检索和医学图像解释。它还将展示如何将深度信任网的学习过程扩展到高维时间序列和条件随机场的层次结构。 |
| 课程简介: | Complex probabilistic models of unlabeled data can be created by combining simpler models. Mixture models are obtained by averaging the densities of simpler models and "products of experts" are obtained by multiplying the densities together and renormalizing. A far more powerful type of combination is to form a "composition of experts" by treating the values of the latent variables of one model as the data for learning the next model. The first half of the tutorial will show how deep belief nets -- directed generative models with many layers of hidden variables -- can be learned one layer at a time by composing simple, undirected, product of expert models that only have one hidden layer. It will also explain why composing directed models does not work. Deep belief nets are trained as generative models on large, unlabeled datasets, but once multiple layers of features have been created by unsupervised learning, they can be fine-tuned to give excellent discrimination on small, labeled datasets. The second half of the tutorial will describe applications of deep belief nets to several tasks including object recognition, non-linear dimensionality reduction, document retrieval, and the interpretation of medical images. It will also show how the learning procedure for deep belief nets can be extended to high-dimensional time series and hierarchies of Conditional Random Fields. |
| 关 键 词: | 模型生成; 线性降维; 数据集 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2021-03-07：zyk |
| 最后编审: | 2021-03-10：zyk |
| 阅读次数: | 48 |