图 书 馆
学习深层架构教程Tutorial on Learning Deep Architectures |
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| 课程网址: | http://videolectures.net/icml09_bengio_lecun_tldar/ |
| 主讲教师: | Yoshua Bengio; Yann LeCun |
| 开课单位: | 蒙特利尔大学 |
| 开课时间: | 2009-08-26 |
| 课程语种: | 英语 |
| 中文简介: |  这个简短的深度学习教程将回顾各种学习多层次，分层表示的方法，并强调它们的共同特征。尽管深层架构在表示能力和表示效率方面具有理论优势，但它们也为哺乳动物皮质中的信息处理提供了可能的模型，该模型似乎依赖于具有多个抽象级别的表示。自2005年以来，已经提出了许多深度学习方法，这些方法在多个领域取得了令人惊讶的良好性能，尤其是在视觉（对象识别）和自然语言处理方面。他们都使用某种形式的无监督学习来学习多种表示形式。将根据新的实验结果来讨论解释这些算法为何有效的假设。这些算法中的许多算法都可以在无监督学习的基于能量的视图框架中进行转换，该框架泛化了用作深度架构构建模块的图形模型，例如受限玻尔兹曼机（RBM）和正则化自动编码器的变体。将介绍新旧算法，用于训练，采样和估计RBM和Deep Belief网络的分区功能。将描述深度架构在计算机视觉和自然语言处理中的应用。在观众的积极参与下，将讨论许多未解决的问题和未来的研究途径。 |
| 课程简介: | This short tutorial on deep learning will review a variety of methods for learning multi-level, hierarchical representations, emphasizing their common traits. While deep architectures have theoretical advantages in terms of expressive power and efficiency of representation, they also provide a possible model for information processing in the mammalian cortex, which seems to rely on representations with multiple levels of abstractions. A number of deep learning methods have been proposed since 2005, that have yielded surprisingly good performance in several areas, particularly in vision (object recognition), and natural language processing. They all learn multiple levels of representation using some form of unsupervised learning. Hypotheses to explain why these algorithms work well will be discussed in the light of new experimental results. Many of these algorithms can be cast in the framework of the energy-based view of unsupervised learning, which generalizes graphical models used as building blocks for deep architectures, such as the Restricted Boltzmann Machines (RBM) and variations of regularized auto-encoders. Old and new algorithms will be presented for training, sampling, and estimating the partition function of RBMs and Deep Belief Networks. Applications of deep architectures to computer vision and natural language processing will be described. A number of open problems and future research avenues will be discussed, with active participation from the audience. |
| 关 键 词: | 多层次学习; 深度学习; 玻尔兹曼机（RBM）; 自动编码器; RBM网络; Deep Belief网络 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2020-03-30：zhouxj |
| 最后编审: | 2020-05-25：cxin |
| 阅读次数: | 35 |