2015. Deep learning in neural networks: an overview. Neural Netw. 61:85-117Schmidhuber, J. Deep learning in neural networks: An overview. Neural Netw. 2015, 61, 85-117. [CrossRef] [PubMed]J. Schmidhuber, "Deep learning in neural networks: An overview," Neural Networks, vol. 61, pp....
Title Deep Learning in Neural Networks: An Overview Author(s) Juergen Schmidhuber Publisher: arxiv.org (October 2014) and University of Lugano License(s): Non-exclusive License to Distribute Hardcover N/A eBook PDF (206 pages) Language: English ISBN-10: N/A ISBN-13: N/A Share This: ...
Neural Networks and Deep Learning 3.6 Activation Function sigmoid: a=11+e−za=11+e−z 取值在(0,1)之间,除非是二分类的输出层,一般不选用,因为tanhtanh比sigmoid表现要好。 tanh: a=ez−e−zez+e−za=ez−e−zez+e−z 取值在(-1,1),有数据中心... ...
多任务学习有很多形式,如联合学习(Joint Learning),自主学习(Learning to Learn),借助辅助任务学习(Learning with Auxiliary Tasks)等,这些只是其中一些别名。概括来讲,一旦发现正在优化多于一个的目标函数,你就可以通过多任务学习来有效求解(Generally, as soon as you find yourself optimizing more than one loss fu...
Deep Learning in Neural Networks- This technical report provides an overview of deep learning and related techniques with a special focus on developments in recent years. 主要看点是深度学习近两年(2012-2014)的进展情况。 Tutorials UFLDL Tutorial 120 ...
3 Deep learning in SNNs Deep learning uses an architecture with many layers of trainable parameters and has demonstrated outstanding performance in machine learning and AI applications (LeCun et al., 2015a; Schmidhuber, 2015). Deep neural networks (DNNs) are trained end-to-end by using optimizat...
概括来讲,一旦发现正在优化多于一个的目标函数,你就可以通过多任务学习来有效求解(Generally, as soon as you find yourself optimizing more than one loss function, you are effectively doing multi-task learning (in contrast to single-task learning))。在那种场景中,这样做有利于想清楚我们真正要做的是什么...
Draft: Deep Learning in Neural Networks: An Overview Technical Report IDSIA-03-14 / arXiv:1404.7828 (v1.5) [cs.NE] J¨ urgen Schmidhuber The Swiss AI Lab IDSIA Istituto Dalle Molle di Studi sull’Intelligenza Arti?ciale University of Lugano & SUPSI Galleria 2, 6928 Manno-Lugano Switzerland...
第二门课 改善深层神经网络:超参数调试、正则化以及优化(Improving Deep Neural Networks:Hyperparameter tuning, Regularization and Optimization) 第一周:深度学习的实用层面(Practical aspects of Deep Learning):http://www.ai-start.com/dl2017/html/lesson2-week1.html ...
估算的办法是 Q( s_{t}, a_{t} ) = r_{t} + γ max_{a_{t+1} } Q( s_{t+1}, a_{t+1} ),术语是 Q-learning 算法。 Q-learning 算法有三个要点, 1. 当前的行动价值,取决于当前看到什么名画,这就是 r_{t} 表达的含义。而且取决于是否能为后续看到名画提供方便,即 Q( s_{t+1}...