In 2006, there was a resurgence of interest in deep neural networks. This was triggered by the discovery that there was a simple and effective way to pre-train deep networks as generative models of unlabeled data. The pre-trained networks could then be fine-tuned discriminatively to give excellent performance on labeled data. In this lecture, I will describe the pre-training procedure used for Deep Belief Nets and show how it evolved from an earlier training procedure for Boltzmann machines that was theoretically elegant but too inefficient to be practical. I will also show how the pre-training procedure overcame a major practical problem in training densely connected belief nets.
2006年，人们对深部神经网络的兴趣重新抬头。这是由于发现有一种简单而有效的方法将深层网络预训练为未标记数据的生成模型而引发的。然后，可以对经过预先培训的网络进行有区别的微调，以在标记的数据上提供出色的性能。在这堂课中，我将描述用于深度信念网的预培训程序，并展示它是如何从早期的Boltzmann机器培训程序演变而来的，Boltzmann机器在理论上很优雅，但效率太低，不实用。我还将展示训练前的程序如何克服训练紧密相连的信念网中的一个重大实际问题。
Geoffrey Hinton received his PhD in Artificial Intelligence from Edinburgh in 1978. After five years as a faculty member at Carnegie-Mellon he became a fellow of the Canadian Institute for Advanced Research and moved to the Department of Computer Science at the University of Toronto where he is now an Emeritus Distinguished Professor. He is also a Vice President & Engineering Fellow at Google and Chief Scientific Adviser of the Vector Institute.
Geoffrey Hinton was one of the researchers who introduced the backpropagation algorithm and the first to use backpropagation for learning word embeddings. His other contributions to neural network research include Boltzmann machines, distributed representations, time-delay neural nets, mixtures of experts, variational learning and deep learning. His research group in Toronto made major breakthroughs in deep learning that revolutionized speech recognition and object classification.
Geoffrey Hinton is a fellow of the UK Royal Society, a foreign member of the US National Academy of Engineering and a foreign member of the American Academy of Arts and Sciences. His awards include the David E. Rumelhart prize, the IJCAI award for research excellence, the Killam prize for Engineering, the IEEE Frank Rosenblatt medal, the IEEE James Clerk Maxwell Gold medal, the NEC C&C award, the BBVA award, the Turing award, and the NSERC Herzberg Gold Medal which is Canada's top award in Science and Engineering.
杰弗里·辛顿于1978年从爱丁堡获得了人工智能博士学位。在卡内基梅隆大学当了五年的教员后，他成为了加拿大高级研究所的研究员，并搬到了多伦多大学计算机科学系，现在他是一位退休的杰出教授。他还是谷歌的副总裁兼工程研究员，也是矢量研究所的首席科学顾问。
杰弗里·辛顿（geoffrey hinton）是研究者之一，他引入了反向传播算法，并首次使用反向传播来学习单词嵌入。他对神经网络研究的其他贡献包括玻尔兹曼机器、分布式表示、时滞神经网络、专家混合、变分学习和深度学习。他在多伦多的研究小组在深入学习方面取得了重大突破，彻底改变了语音识别和对象分类。
杰弗里·辛顿是英国皇家学会会员，美国国家工程院外籍院士，美国艺术与科学院外籍院士。他的奖项包括大卫E.鲁梅尔哈特奖、ijcai杰出研究奖、基拉姆工程奖、IEEE Frank Rosenblatt奖、IEEE James Clerk Maxwell金奖、NEC C C&C奖、BBVA奖、图灵奖和加拿大科学与工程最高奖NSERC Herzberg金奖。
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