“大数据智能”版本间的差异
来自iCenter Wiki
（→国际研究） |
|||
| 第57行： | 第57行： | ||
# Jordan, M. I., and T. M. Mitchell. "Machine learning: Trends, perspectives, and prospects." Science 349, no. 6245 (2015): 255-260. [http://science.sciencemag.org/content/349/6245/255 Machine_learning_science_2015] | # Jordan, M. I., and T. M. Mitchell. "Machine learning: Trends, perspectives, and prospects." Science 349, no. 6245 (2015): 255-260. [http://science.sciencemag.org/content/349/6245/255 Machine_learning_science_2015] | ||
| − | = | + | =深度学习= |
[[卷积神经网络]] | [[卷积神经网络]] | ||
| 第63行： | 第63行： | ||
Deep Neural Networks，简称DNN | Deep Neural Networks，简称DNN | ||
| − | |||
| − | |||
| + | 入门导读 | ||
| + | # Stanford Deep Learning tutorials [http://ufldl.stanford.edu/wiki/index.php/UFLDL_Tutorial DL_tutorials] | ||
# LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. "Deep learning." Nature 521(7553), pp:436-444, 2015. [http://www.nature.com/nature/journal/v521/n7553/full/nature14539.html Deep_Learning_Nature] | # LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. "Deep learning." Nature 521(7553), pp:436-444, 2015. [http://www.nature.com/nature/journal/v521/n7553/full/nature14539.html Deep_Learning_Nature] | ||
# Jeff Dean, Large-Scale Deep Learning for Intelligent Computer Systems, WSDM 2016. [http://research.google.com/pubs/jeff.html WSDM_keynote] | # Jeff Dean, Large-Scale Deep Learning for Intelligent Computer Systems, WSDM 2016. [http://research.google.com/pubs/jeff.html WSDM_keynote] | ||
| 第89行： | 第89行： | ||
[https://github.com/dmlc/mxnet dmlc_mxnet] | [https://github.com/dmlc/mxnet dmlc_mxnet] | ||
| + | [http://mxnet.io/ mxnet] | ||
=机器感知= | =机器感知= | ||
2016年11月23日 (三) 03:22的版本
目录
人工智能简介
人工智能，是指计算机系统具备从听说读写到搜索、推理、决策和回答问题等类人智能的能力。即， 感知、理解、决策的能力。
人工智能历史
过去经历了2次高潮与2次低谷
网络和云计算所支持的计算能力
基于大数据的机器学习的算法进步
阅读材料：
- The rebirth of neural networks-ISCA-2010 rebirth_NN
四个层面
目标与功能分类
语音识别、机器视觉、自然语言理解。
智能问答是综合以上功能的高级系统。
核心技术分类
特定算法 机器学习算法 深度神经网络
软件工具
TensorFlow / Caffe / Torch
底层硬件分类
可编程逻辑阵列 FPGA / 通用图形处理器 GPGPU / 通用处理器 CPU 群集
国际研究
Facebook_AI-Research (Yann LeCun)
MSR Deep Learning Technology Center (DLTC) (Li Deng)
OpenAI (Ilya Sutskever)
机器学习
Machine Learning
- Jordan, M. I., and T. M. Mitchell. "Machine learning: Trends, perspectives, and prospects." Science 349, no. 6245 (2015): 255-260. Machine_learning_science_2015
深度学习
Deep Neural Networks，简称DNN
入门导读
- Stanford Deep Learning tutorials DL_tutorials
- LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. "Deep learning." Nature 521(7553), pp:436-444, 2015. Deep_Learning_Nature
- Jeff Dean, Large-Scale Deep Learning for Intelligent Computer Systems, WSDM 2016. WSDM_keynote
- Jeffrey Dean et al. "Large scale distributed deep networks." Advances in Neural Information Processing Systems. 2012.
- TensorFlow: A System for Large-Scale Machine Learning, OSDI 2016. TensorFlow_OSDI2016_paper TensorFlow_paper
深度学习工具
谷歌
微软
百度
机器感知
基于深度学习的机器感知
语音识别
语音识别（Automatic Speech Recognition），简称ASR
- Google Speech
- Google Speech Processing from Mobile to Farfield, CHiME 2016. Google_Speech_Processing
- Long short term memory neural network (LSTM)
- Long short term memory neural computation, Neural computation 9 (8), 1735-1780, 1997. LSTM
- Connectionist temporal classification (CTC)
- Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks, ICML 2006.
计算机视觉
计算机视觉（Computer Vision），简称 CV
- Object Detection
- R-CNN (Region-based Convolutional Network method)
- Region based convolutional networks for accurate object detection and segmentation, TPAMI, 2015.
- Rich feature hierarchies for accurate object detection and semantic segmentation, CVPR 2014.
- Fast R-CNN (Fast Region-based Convolutional Network method)
- Fast R-CNN, ICCV 2015.
- Faster R-CNN (Faster Region-based Convolutional Network method)
- Faster R-CNN Towards real-time object detection with region proposal networks, NIPS, 2015.
- • Fast_R-CNN(Python): https://github.com/rbgirshick/fast-rcnn
- • Faster_R-CNN(matlab): https://github.com/ShaoqingRen/faster_rcnn
- • Faster_R-CNN(Python): https://github.com/rbgirshick/py-faster-rcnn
自然语言理解
自然语言理解（Natural Language Processing），简称NLP
智能问答
整合语音识别ASR，计算机视觉CV和自然语言处理NLP的问答系统QA。
