扩大深度学习Scaling Up Deep Learning |
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课程网址: | http://videolectures.net/kdd2014_bengio_deep_learning/ |
主讲教师: | Yoshua Bengio |
开课单位: | 蒙特利尔大学 |
开课时间: | 2014-10-07 |
课程语种: | 英语 |
中文简介: | 深度学习在不到十年前已经从机器学习社区的边缘方法迅速发展到具有强大工业影响的方法,特别是对于语音和图像等高维感性数据,以及自然语言。因此,我们对博士研究生的需求比他们的市场需求增长得更快。深度学习是基于学习多层次表征的思想,高层次作为较低层次的函数计算,与学习者自动发现的更抽象的概念相对应。深入学习源于对人工神经网络和图形模型的研究,近年来关于这一主题的文献有了很大的增长,最终形成了一个专门的会议(ICLR)。本教程将介绍一些基本算法,包括有监督的和无监督的,并讨论一些在实践中成功使用它们的指导原则。最后,本文将介绍当前的研究问题,即如何将深度学习扩展到更大的模型,从而成功地从巨大的数据集中提取信息。 |
课程简介: | Deep learning has rapidly moved from a marginal approach in the machine learning community less than ten years ago to one that has strong industrial impact, in particular for high-dimensional perceptual data such as speech and images, but also natural language. The demand for experts in deep learning is growing very fast (faster than we can graduate PhDs), thereby considerably increasing their market value. Deep learning is based on the idea of learning multiple levels of representation, with higher levels computed as a function of lower levels, and corresponding to more abstract concepts automatically discovered by the learner. Deep learning arose out of research on artificial neural networks and graphical models and the literature on that subject has considerably grown in recent years, culminating in the creation of a dedicated conference (ICLR). The tutorial will introduce some of the basic algorithms, both on the supervised and unsupervised sides, as well as discuss some of the guidelines for successfully using them in practice. Finally, it will introduce current research questions regarding the challenge of scaling up deep learning to much larger models that can successfully extract information from huge datasets. |
关 键 词: | 深度学习; 基本算法; 数据 |
课程来源: | 视频讲座网 |
数据采集: | 2020-11-27:yxd |
最后编审: | 2020-11-27:yxd |
阅读次数: | 38 |