查看“大数据智能”的源代码

=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 [http://pages.saclay.inria.fr/olivier.temam/homepage/ISCA2010web.pdf 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==

[http://research.google.com/teams/brain/ Googel_Brain]
([http://research.google.com/pubs/jeff.html Jeff Dean])

[https://research.facebook.com/ai Facebook_AI-Research]
(Yann LeCun)

[https://www.microsoft.com/en-us/research/group/dltc/ MSR Deep Learning Technology Center (DLTC)]
(Li Deng)

[https://www.openai.com/blog/ OpenAI]
(Ilya Sutskever)

==机器学习==

Machine Learning

[http://scikit-learn.org scikit-learn]

# 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=

Deep Neural Networks，简称DNN

[[卷积神经网络]]

[[深度学习-入门导读]]

==深度学习工具==

===谷歌===

[https://github.com/tensorflow/tensorflow TensorFlow]

[http://download.tensorflow.org/paper/whitepaper2015.pdf Google_TensorFlow_whitepaper]

===Facebook===

[https://github.com/facebook/fbcunn fbcunn]
[http://torch.ch/ Torch]

===微软===

[http://cntk.ai CNTK_Microsfot]

[https://www.microsoft.com/en-us/research/product/cognitive-toolkit/tutorials/ CNTK]

===DMLC===

[http://dmlc.ml/ dmlc]

[http://mxnet.io/ MXNet]
[https://github.com/dmlc/mxnet dmlc_mxnet]

===Université de Montréal===


[https://github.com/Theano/Theano/ Theano_code]

=增强学习=
增强学习（Reinforcement Learning）

[[增强学习-入门导读]]

==工具==
===谷歌===
deepmind/lab  
[https://github.com/deepmind/lab deepmind_lab]

===OpenAI===
openai/universe
[https://github.com/openai/universe openai_universe]

=机器感知=

机器感知(Machine Perception)，如语音，图像，视频，手势，姿态等

==<B>基于深度学习的机器感知</B>==

===语音识别===

语音识别（Automatic Speech Recognition），简称ASR

基本工具
*:Long short term memory neural network (LSTM)
:# Long short term memory neural computation, Neural computation 9 (8), 1735-1780, 1997. [http://ieeexplore.ieee.org/document/6795963 LSTM]

*:Connectionist temporal classification (CTC) 
:#Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks, ICML 2006.

*:Gated Recursive Unit (GRU)
:#On the Properties of Neural Machine Translation: Encoder-Decoder Approaches, SSST-8, 2014.

Alex Graves，DeepMind研究员，语音识别多项技术开创者。

[https://scholar.google.com.hk/citations?user=DaFHynwAAAAJ Alex Graves] 

#Towards End-To-End Speech Recognition with Recurrent Neural Networks, ICML 2014.
#Speech recognition with deep recurrent neural networks, 2013.
#Hybrid speech recognition with deep bidirectional LSTM, ASRU 2013.
#Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks, ICML 2006.





:Google Speech
:# Google Speech Processing from Mobile to Farfield, CHiME 2016. [http://spandh.dcs.shef.ac.uk/chime_workshop/presentations/CHiME_2016_Bacchiani_keynote.pdf Google_Speech_Processing]

===计算机视觉===

计算机视觉（Computer Vision），简称 CV

*	Object Detection
:<B>R-CNN (Region-based Convolutional Network method)</B>
::#Region based convolutional networks for accurate object detection and segmentation, TPAMI, 2015.
::#Rich feature hierarchies for accurate object detection and semantic segmentation, CVPR 2014.

:<B>Fast R-CNN (Fast Region-based Convolutional Network method)</B>
::#Fast R-CNN, ICCV 2015.

:<B>Faster R-CNN (Faster Region-based Convolutional Network method)</B>
::#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

=机器认知=

机器认知（Machine Cognition），自然语言理解，推理，注意，知识，学习，决策，交互等。

<B>技术手段：</B>

深度学习（Deep Learning） +  增强学习（Reinforcement/Unsupervised Learning）

=前沿应用进展=

==自然语言理解==

自然语言理解（Natural Language Understanding），使用的技术称为自然语言处理（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。

Reasoning in vector space: An exploratory study of question answering, ICLR 2016.

=其它课程=

[[实验室探究课-智能问答与智能系统]]