图 书 馆
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: |  深度学习已从不到十年前的机器学习社区中的边缘方法迅速发展为具有强大工业影响力的方法，特别是对于语音，图像等高维感知数据以及自然语言。深度学习专家的需求增长非常快（比我们毕业的博士更快），从而大大提高了他们的市场价值。深度学习基于学习多个表示形式的思想，其中较高的水平作为较低水平的函数进行计算，并且与学习者自动发现的更多抽象概念相对应。深度学习源于对人工神经网络和图形模型的研究，近年来，有关该主题的文献大量增长，最终形成了专门会议（ICLR）。本教程将在有监督的和无监督的方面介绍一些基本算法，并讨论在实践中成功使用它们的一些准则。最后，它将介绍有关将深度学习扩展到可以从大型数据集中成功提取信息的更大模型的挑战的当前研究问题。 |
| 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; 需求增长; 专门会议 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2020-06-09：吴淑曼 |
| 最后编审: | 2020-06-15：吴淑曼(课程编辑志愿者) |
| 阅读次数: | 44 |