机器学习领域的几种主要学习方式
解密最接近人脑的智能学习机器——深度学习及并行化实现
5 deep learning startups to follow in 2015
How to run the Caffe deep learning vision library on Nvidia’s Jetson mobile GPU board
Hacker’s Guide to Neural Networks
Google Turns to Deep Learning Classification to Fight Web Spam
Michael Jordan on deep learning
Scaling up Deep Learning – Yoshua Bengio
Deep Learning – important resources for learning and understanding
Where to Learn Deep Learning – Courses, Tutorials, Software
DEEP LEARNING-An MIT Press book in preparation
Computer Eyesight Gets a Lot More Accurate
深度学习word2vec笔记之应用篇
深度学习word2vec笔记之算法篇
深度卷积神经网络CNNs的多GPU并行框架 及其在图像识别的应用
Distributed Neural Networks with GPUs in the AWS Cloud
Dropout: A Simple Way to Prevent Neural Networks from Overfitting
Twitter acquires deep learning startup Madbits
How to build and run your first deep learning network
ICML 2014 Highlights 2: On Deep Learning and Language Modeling
Deep Learning, NLP, and Representations
TUTORIAL ON DEEP LEARNING FOR VISION
Does Deep Learning Have Deep Flaws?
deeplearning4j – 分布式 deep learning 开源项目
Get off the deep learning bandwagon and get some perspective
对话机器学习大神Yoshua Bengio(下)
Implementation of Stanford’s UFLDL Tutorial in Python
Hacking neural networks
Phn2vec Embeddings
How Deep Learning will change our world. Melbourne Data Science, Jeremy Howard
Deep Learning From The Bottom Up
Where to Learn Deep Learning – Courses, Tutorials, Software
Yann LeCun’s answers from the Reddit AMA
A Primer on Deep Learning
深度学习概述:从感知机到深度网络
对话Facebook人工智能实验室主任、深度学习专家Yann LeCun
ConvNetJS: Deep Learning in your browser
【数字智能三篇】之三: 一页纸说清楚“什么是深度学习?”
深度学习(Deep Learning) 学习资料
Nervana takes $600K to build hardware for deep learning
对话机器学习大神Yoshua Bengio(上)
Galaxy Zoo Challenge: code published
Deep learning these days
Deep learning made easy
Word2vec资料汇总
A Simple Deep Network
Deep Learning业界现状
Neural Networks, Manifolds, and Topology
Deep Learning新星-Charlie Tang(Yichuan Tang)
Hinton独家采访
Galaxy Zoo challenge第一名的解决之道-Deep Learning之CNN
余凯在清华的讲座笔记
【科普随笔:NLP的宗教战争?兼论深度学习】
Neural Networks Course By Hugo Larochelle
Clarifai,一个好玩的图片识别网站
互联网世界的“人工智能”——探秘“深度学习”的前世今生
自动微分简介
UFLDL-斯坦福大学Andrew Ng教授“Deep Learning”教程
Introduction to Restricted Boltzmann Machines
围猎深度学习——初创公司、科技巨头、研究机构在角力
10 Breakthrough Technologies in 2013 (Deep Learning排第一)
NIPS2013两大热点:Deep Learning和分布式机器学习
问问讲堂-深度神经网络语言模型在统计机器翻译系统中的应用
问问讲堂-深度学习在NLP中的应用
广告数据上的大规模机器学习-夏粉-百度技术沙龙48期
deeplearning—-卷积神经网络 – I know you
DeepFace: Closing the Gap to Human-Level Performance in Face Verification
DeepFace
Deep Learning经典论文列表(Reading List)
Deep Learning for NLP 文章列举
Deep Learning for NLP (without Magic)
KDnuggets Exclusive: Interview with Yann LeCun, Deep Learning Expert, Director of Facebook AI Lab
Deep Learning实战之word2vec
深度学习让计算机学会思考 逐步朝人工智能靠近
深度学习: 推进人工智能的梦想
百度技术沙龙第46期回顾:读图时代的识图技术(含资料下载)
Shallow Learning and Deep Learning
Deep Learning(深度学习) 学习笔记(三)
Machine Learning与Deep Learning – 积翠如云_小林大悟
deep leaning(深度学习)介绍
Deep learning:五十一(CNN的反向求导及练习) – tornadomeet
Deep learning:五十(Deconvolution network简单理解) – tornadomeet
Deep learning:四十九(RNN-RBM简单理解) – tornadomeet
Deep learning:四十八(Contractive AutoEncoders简单理解) – tornadomeet
Deep learning:四十七(Stochastic Pooling简单理解) – tornadomeet
Deep learning:四十六(DropConnect简单理解) – tornadomeet
Deep learning:四十五(maxout简单理解) – tornadomeet
Deep learning:四十四(Pylearn2中的Quick-start例子) – tornadomeet
deep learning学习环境Theano安装(win8+win7) – 梦之缘工作坊
[转]Deep Learning(深度学习)学习笔记整理系列
【Deep Learning学习笔记】A Unified Architecture for Natural Language Processing_ICML2008
Deep Learning 学习随记(三)Softmax regression – bzjia
【Deep Learning学习笔记】Dynamic Auto-Encoders for Semantic Indexing_Mirowski_NIPS2010
【Deep Learning学习笔记】Deep learning for nlp without magic_Bengio_ppt_acl2012
Deep Learning(Logistic Regression)学习之MNIST C++实现
【Deep Learning学习笔记】Learning meanings for sentences
转帖:Deep Learning(深度学习)学习笔记整理系列之(八) – CUG_信子
Deep Belief Network(DBN)的实现(c++)
Deep Learning源代码收集-持续更新…
Deep Learning 学习笔记(8):自编码器( Autoencoders ) – Pony_s
Deep Learning 学习笔记(7):神经网络的求解 与 反向传播算法(Back Propagation) – Pony_s
【Deep Learning学习笔记】Modeling Documents with a Deep Boltzmann Machine_Hinton_uai2013
Deep Learning and Shallow Learning
[开源推荐]Google开源基于Deep Learning的word2vec工具
Deep Learning小结
Deep Learning基础理论--Classification RBM – HarryJiang
[原]Deep Learning论文笔记之(八)Deep Learning最新综述
Deep learning:四十三(用Hessian Free方法训练Deep Network) – tornadomeet
Deep Learning论文笔记之(七)深度网络高层特征可视化
Deep Learning论文笔记之(六)Multi-Stage多级架构分析
Deep learning:四十二(Denoise Autoencoder简单理解) – tornadomeet
Deep Learning论文笔记之(五)CNN卷积神经网络代码理解
Deep Learning论文笔记之(四)CNN卷积神经网络推导和实现
Deep Learning论文笔记之(三)单层非监督学习网络分析 – Class Xman
[原]Deep Learning论文笔记之(三)单层非监督学习网络分析
Deep Learning论文笔记之(一)K-means特征学习
Deep learning:四十一(Dropout简单理解) – tornadomeet
【面向代码】学习 Deep Learning(三) Stacked Auto-Encoders(SAE)
【deep learning学习笔记】Greedy Layer-Wise Training of Deep Networks
Unsupervised Feature Learning and Deep Learning(UFLDL) Exercise 总结
【deep learning学习笔记】Restricted Boltzmann Machines for Collaborative Filtering
【Deep Learning】一、AutoEncoder
【deep learning学习笔记】注释yusugomori的SDA代码 — Sda.cpp — 模型训练与预测
【deep learning学习笔记】注释yusugomori的SDA代码 — 准备工作
Deep Learning in NLP (一)词向量和语言模型
【面向代码】学习 Deep Learning(三)Convolution Neural Network(CNN)
【deep learning学习笔记】注释yusugomori的DA代码 — dA.cpp — 模型测试
【deep learning学习笔记】注释yusugomori的DA代码 — dA.cpp — 训练
【deep learning学习笔记】注释yusugomori的DA代码 — 头文件
【deep learning学习笔记】Autoencoder
【deep learning学习笔记】注释yusugomori的LR代码 — 模型测试
【deep learning学习笔记】注释yusugomori的LR代码 — LogisticRegression.cpp
【deep learning学习笔记】注释yusugomori的LR代码 — LogisticRegression.h
Deep learning:四十(龙星计划2013深度学习课程小总结) – tornadomeet
【deep learning学习笔记】读张春霞《受限波尔兹曼机简介》
【deep learning学习笔记】注释yusugomori的RBM代码 — cpp文件 — 模型测试
【deep learning学习笔记】注释yusugomori的RBM代码 — cpp文件 — 准备工作
【deep learning学习笔记】注释yusugomori的RBM代码 — 头文件
【deep learning学习笔记】最近读的几个ppt(四)
Deep Learning(5) – Loull
【deep learning学习笔记】最近读的几个ppt(三)
深度学习(Deep Learning)算法简介
【deep learning学习笔记】最近读的几个ppt(二)
量子学习及思考10-人脑更需要Deep Learning
【deep learning学习笔记】最近读的几个ppt(未完…)
[原]如何正确理解深度学习(Deep Learning)的概念
Deep learning:三十九(ICA模型练习)
Deep learning:三十八(Stacked CNN简单介绍)
Deep learning高质量交流群
Deep learning:三十七(Deep learning中的优化方法)
Deep learning:三十六(关于构建深度卷积SAE网络的一点困惑)
Deep learning:三十五(用NN实现数据降维练习)
Deep learning:三十四(用NN实现数据的降维)
Large Scale, Sparse Coding, Deep Learning, …
Deep learning:三十三(ICA模型)
Deep learning:三十二(基础知识_3)
Deep learning:三十一(数据预处理练习)
Deep learning:三十(关于数据预处理的相关技巧)
Deep learning:二十九(Sparse coding练习)
Deep learning:二十八(使用BP算法思想求解Sparse coding中矩阵范数导数)
Deep learning:二十七(Sparse coding中关于矩阵的范数求导)
Deep learning:二十六(Sparse coding简单理解)
Deep learning:二十五(Kmeans单层网络识别性能)
Deep Learning(深度学习)学习笔记整理系列之(八)
Deep Learning(深度学习)学习笔记整理系列之(七)
Deep learning:二十四(stacked autoencoder练习)
Deep Learning(深度学习)学习笔记整理系列之(五)
Deep learning:二十三(Convolution和Pooling练习)
找工作声明
Deep Learning(深度学习)学习笔记整理系列之(四)
Deep Learning(深度学习)学习笔记整理系列之(三)
Deep Learning(深度学习)学习笔记整理系列之(二)
Deep Learning(深度学习)学习笔记整理系列之(一)
Deep learning:二十二(linear decoder练习)
Deep Learning深度学习相关入门文章汇摘
supervised learning & semi-supervised learning & transfer learning & self-taught learning & deep learning
Deep learning:二十一(随机初始化在无监督特征学习中的作用)
Deep learning:二十(无监督特征学习中关于单层网络的分析)
Deep learning:十九(RBM简单理解)
Deep learning:十八(关于随机采样)
Deep learning:十七(Linear Decoders,Convolution和Pooling)
Deep learning:十六(deep networks)
Deep learning:十四(Softmax Regression练习)
Deep learning:十三(Softmax Regression)
Deep learning:十二(PCA和whitening在二自然图像中的练习)
Deep learning:十(PCA和whitening)
Deep learning:九(Sparse Autoencoder练习)
Deep learning:八(Sparse Autoencoder)
CV特征
Deep learning:六(regularized logistic回归练习)
Deep learning:五(regularized线性回归练习)
Deep learning:四(logistic regression练习)
Deep learning:三(Multivariance Linear Regression练习)
Deep learning:二(linear regression练习)
Deep learning:一(基础知识_1)
Deep Learning开山祖师爷加入Google,科技帝国雏形初现
Deep Learning学习(开篇) – Jack King
A shallow understanding on deep learning
语义搜索与创新者的窘境
Deep learning的一些教程[rz]
深度学习的一些教程
Deep Learning for Efficient Discriminative Parsing
Deep Learning在自然语言理解中的应用
关于深度学习——Deep Learning
Spanner and Deep Learning
深度学习
浅谈Deep Learning的基本思想和方法
关于深度学习(deep learning)
Deep Learning 自学
Deep Learning, Feature Learning的一个summer school的slides
机器学习——深度学习(Deep Learning)