文件名称：Deep Learning深度学习入门论文
文件大小：11.78MB
文件格式：ZIP
更新时间：2018-09-16 15:38:46
Deep Learning, 深度学习, 论文
1. 概述类 首先是概述类论文，先后有2013年的“Representation Learning: A Review and New Perspectives”和2015年的”Deep Learning in Neural Networks: An Overview”两篇。 上传了较新的一篇。 3. 分布式计算 分布式计算方面论文涉及到具体解决计算能力的问题。有2012年的两篇论文Building High-level Features Using Large Scale Unsupervised Learning和Large Scale Distributed Deep Networks，其中后篇较好，其中第一次提到GPU对深度学习计算进行提速，其描述的情形大致是如何对多个GPGPU并行计算的深度学习框架进行编程。故上传了此篇 4. 具体算法 而后便是具体的算法方面的典型论文，包括K-means、单层非监督网络、卷积网络CNN、多级架构、Maxout和增强学习，论文列举如下： 2006年Notes on Convolutional Neural Networks 2009年What is the Best Multi-Stage Architecture for Object Recognition 2011年An Analysis of Single-Layer Networks in Unsupervised Feature Learning 2012年Learning Feature Representations with K-means 2012年Sparse Filtering (其中有RBM，auto-encoder等) 2014年Improving deep neural network acoustic models using generalized maxout networks 2014年Adolescent-specific patterns of behavior and neural activity during social reinforcement learning 2015年Reinforcement learning models and their neural correlates: An activation likelihood estimation meta-analysis和Human-level control through deep reinforcement learning
【文件预览】：
04 - 2015 - Human-level control through deep reinforcement learning.pdf
04 - 2012 - Sparse Filtering.pdf
04 - 2015 - Reinforcement learning models and their neural correlates - An activation likelihood estimation meta-analysis.pdf
01 - 201501 - Deep-learning-in-neural-networks-An-overview_2015_Neural-Networks.pdf
04 - 2006 - Notes on Convolutional Neural Networks.pdf
04 - 2009 - What is the Best Multi-Stage Architecture for Object Recognition.pdf
04 - 2012 - Learning Feature Representations with K-means.pdf
03 - Google - Large Scale Distributed Deep Networks.pdf
04 - 2014 - Improving deep neural network acoustic models using generalized maxout networks.pdf
04 - 2014 - Adolescent-specific patterns of behavior and neural activity during social reinforcement learning.pdf
04 - 2011 - An Analysis of Single-Layer Networks in Unsupervised Feature Learning.pdf