愛(ài)華網(wǎng)吳恩達(dá)（Andrew Ng），華裔美國(guó)人，斯坦福大學(xué)計(jì)算機(jī)科學(xué)系和電子工程系副教授，人工智能實(shí)驗(yàn)室主任，百度公司首席科學(xué)家。吳恩達(dá)是人工智能和機(jī)器學(xué)習(xí)領(lǐng)域國(guó)際上最權(quán)威的學(xué)者之一，也是在線教育平臺(tái)Coursera的聯(lián)合創(chuàng)始人（with Daphne Koller）。2011年，吳恩達(dá)在Google創(chuàng)建Google Brain項(xiàng)目，以通過(guò)分布式集群計(jì)算機(jī)開(kāi)發(fā)超大規(guī)模的人工神經(jīng)網(wǎng)絡(luò)。2014年5月16日，吳恩達(dá)加入百度，負(fù)責(zé)“百度大腦”計(jì)劃，同時(shí)擔(dān)任百度公司首席科學(xué)家。2015年6月5日，吳恩達(dá)暫任百度深度學(xué)習(xí)實(shí)驗(yàn)室（IDL）的Acting Director職務(wù)。
吳恩達(dá)_吳恩達(dá) -人物經(jīng)歷
吳恩達(dá)1976年出生于倫敦，父親是一位香港醫(yī)生
，英文名叫Andrew Ng，吳恩達(dá)年輕時(shí)候在香港和新加坡度過(guò)。1992年吳恩達(dá)就讀新加坡萊佛士書(shū)院，并于1997年獲得了卡內(nèi)基梅隆大學(xué)的計(jì)算機(jī)科學(xué)學(xué)士學(xué)位。之后他在1998年獲得了麻省理工學(xué)院的碩士學(xué)位，并于2002年獲得了加州大學(xué)伯克利分校的博士學(xué)位，并從這年開(kāi)始在斯坦福大學(xué)工作。他（2002年）住在加利福尼亞州的帕洛阿爾托。
吳恩達(dá)是斯坦福大學(xué)計(jì)算機(jī)科學(xué)系和電子工程系副教授，人工智能實(shí)驗(yàn)室主任。吳恩達(dá)主要成就在機(jī)器學(xué)習(xí)和人工智能領(lǐng)域，他是人工智能和機(jī)器學(xué)習(xí)領(lǐng)域最權(quán)威的學(xué)者之一。
2010年，時(shí)任斯坦福大學(xué)教授的吳恩達(dá)加入谷歌開(kāi)發(fā)團(tuán)隊(duì)XLab――這個(gè)團(tuán)隊(duì)已先后為谷歌開(kāi)發(fā)無(wú)人駕駛汽車和谷歌眼鏡兩個(gè)知名項(xiàng)目。
吳恩達(dá)與谷歌頂級(jí)工程師開(kāi)始合作建立全球最大的“神經(jīng)網(wǎng)絡(luò)”，這個(gè)神經(jīng)網(wǎng)絡(luò)能以與人類大腦學(xué)習(xí)新事物相同的方式來(lái)學(xué)習(xí)現(xiàn)實(shí)生活。谷歌將這個(gè)項(xiàng)目命名為“谷歌大腦”。
吳恩達(dá)最知名的是，所開(kāi)發(fā)的人工神經(jīng)網(wǎng)絡(luò)通過(guò)觀看一周YouTube視頻，自主學(xué)會(huì)識(shí)別哪些是關(guān)于貓的視頻。這個(gè)案例為人工智能領(lǐng)域翻開(kāi)嶄新一頁(yè)。吳恩達(dá)表示，未來(lái)將會(huì)在谷歌無(wú)人駕駛汽車上使用該項(xiàng)技術(shù)，來(lái)識(shí)別車前面的動(dòng)物或者小孩，從而及時(shí)躲避。
2014年5月16日，百度宣布吳恩達(dá)加入百度，擔(dān)任百度公司首席科學(xué)家，負(fù)責(zé)百度研究院的領(lǐng)導(dǎo)工作，尤其是Baidu Brain計(jì)劃。
2014年5月19日，百度宣布任命吳恩達(dá)博士為百度首席科學(xué)家，全面負(fù)責(zé)百度研究院。這是中國(guó)互聯(lián)網(wǎng)公司迄今為止引進(jìn)的最重量級(jí)人物。消息一經(jīng)公布，就成為國(guó)際科技界的關(guān)注話題。美國(guó)權(quán)威雜志《麻省理工科技評(píng)論》(MIT Technology Review)甚至用充滿激情的筆調(diào)對(duì)未來(lái)給予展望：“百度將領(lǐng)導(dǎo)一個(gè)創(chuàng)新的軟件技術(shù)時(shí)代，更加了解世界?！?/p>
吳恩達(dá)_吳恩達(dá) -所獲榮譽(yù)
2007年，吳恩達(dá)獲得斯隆獎(jiǎng)（Sloan Fellowship）。
2008年，吳恩達(dá)入選“the MIT Technology Review TR35”，即《麻省理工科技創(chuàng)業(yè)》雜志評(píng)選出的科技創(chuàng)新35俊杰，入選者是35歲以下的35個(gè)世界上最頂級(jí)的創(chuàng)新者之一。
“計(jì)算機(jī)和思想獎(jiǎng)”的獲得者。
2013年，吳恩達(dá)入選《時(shí)代》雜志年度全球最有影響力100人，成為16位科技界代表之一。
吳恩達(dá)_吳恩達(dá) -主要成就
機(jī)器學(xué)習(xí)
Ng's Research
吳恩達(dá)早期的工作包括斯坦福自動(dòng)控制直升機(jī)項(xiàng)目，
吳恩達(dá)團(tuán)隊(duì)開(kāi)發(fā)了世界上最先進(jìn)的自動(dòng)控制直升機(jī)之一。
吳恩達(dá)同時(shí)也是機(jī)器學(xué)習(xí)、機(jī)器人技術(shù)和相關(guān)領(lǐng)域的100多篇論文的作者或合作者，他在計(jì)算機(jī)視覺(jué)的一些工作被一系列的出版物和評(píng)論文章所重點(diǎn)引用。
人工智能
早期的另一項(xiàng)工作是the STAIR (Stanford Artificial Intelligence Robot) project，即斯坦福人工智能機(jī)器人項(xiàng)目，項(xiàng)目最終開(kāi)發(fā)了廣泛使用的開(kāi)源機(jī)器人技術(shù)軟件平臺(tái)ROS。
Andrew Ng通過(guò)筆記本解析Deep Learning
2011年，吳恩達(dá)在谷歌成立了“Google Brain”項(xiàng)目，這個(gè)項(xiàng)目利用谷歌的分布式計(jì)
算框架計(jì)算和學(xué)習(xí)大規(guī)模人工神經(jīng)網(wǎng)絡(luò)。這個(gè)項(xiàng)目重要研究成果是，在16000個(gè)CPU核心上利用深度學(xué)習(xí)算法學(xué)習(xí)到的10億參數(shù)的神經(jīng)網(wǎng)絡(luò)，能夠在沒(méi)有任何先驗(yàn)知識(shí)的情況下，僅僅通過(guò)觀看無(wú)標(biāo)注的YouTube的視頻學(xué)習(xí)到識(shí)別高級(jí)別的概念，如貓，這就是著名的“Google Cat”。這個(gè)項(xiàng)目的技術(shù)已經(jīng)被應(yīng)用到了安卓操作系統(tǒng)的語(yǔ)音識(shí)別系統(tǒng)上。
在線教育
Ng at Machine Learning Class
吳恩達(dá)是在線教育平臺(tái)Coursera的聯(lián)合創(chuàng)始人，吳恩達(dá)
在2008年發(fā)起了“Stanford Engineering Everywhere”（SEE）項(xiàng)目，這個(gè)項(xiàng)目把斯坦福的許多課程放到網(wǎng)上，供免費(fèi)學(xué)習(xí)。NG也教了一些課程，如機(jī)器學(xué)習(xí)課程，包含了他錄制的視頻講座和斯坦福CS299課程的學(xué)生材料。
吳恩達(dá)的理想是讓世界上每個(gè)人能夠接受高質(zhì)量的、免費(fèi)的教育。今天，Coursera和世界上一些頂尖大學(xué)的合作者們一起提供高質(zhì)量的免費(fèi)在線課程。Coursera是世界上最大的MOOC平臺(tái)。
學(xué)術(shù)著作
Deep Learning with COTS HPC Systems
Adam Coates, Brody Huval, Tao Wang, David J. Wu, Bryan Catanzaro and Andrew Y. Ng in ICML 2013.
Parsing with Compositional Vector Grammars
John Bauer,Richard Socher, Christopher D. Manning, Andrew Y. Ng in ACL 2013.
Learning New Facts From Knowledge Bases With Neural Tensor Networks and Semantic Word Vectors
Danqi Chen,Richard Socher, Christopher D. Manning, Andrew Y. Ng in ICLR 2013.
Convolutional-Recursive Deep Learning for 3D Object Classification.
Richard Socher, Brody Huval, Bharath Bhat, Christopher D. Manning, Andrew Y. Ng in NIPS 2012.
Improving Word Representations via Global Context and Multiple Word Prototypes
Eric H. Huang, Richard Socher, Christopher D. Manning and Andrew Y. Ng in ACL 2012.
Large Scale Distributed Deep Networks.
J. Dean, G.S. Corrado, R. Monga, K. Chen, M. Devin, Q.V. Le, M.Z. Mao, M.A. Ranzato, A. Senior, P. Tucker, K. Yang, A. Y. Ng in NIPS 2012.
Recurrent Neural Networks for Noise Reduction in Robust ASR.
A.L. Maas, Q.V. Le, T.M. O'Neil, O. Vinyals, P. Nguyen, and Andrew Y. Ng in Interspeech 2012.
Word-level Acoustic Modeling with Convolutional Vector Regression Learning Workshop
Andrew L. Maas, Stephen D. Miller, Tyler M. O'Neil, Andrew Y. Ng, and Patrick Nguyen in ICML 2012.
Emergence of Object-Selective Features in Unsupervised Feature Learning.
Adam Coates, Andrej Karpathy, and Andrew Y. Ng in NIPS 2012.
Deep Learning of Invariant Features via Simulated Fixations in Video
Will Y. Zou, Shenghuo Zhu, Andrew Y. Ng, Kai Yu in NIPS 2012.
Learning Feature Representations with K-means.
Adam Coates and Andrew Y. Ng in Neural Networks: Tricks of the Trade, Reloaded, Springer LNCS 2012.
Building High-Level Features using Large Scale Unsupervised Learning
Quoc V. Le, Marc'Aurelio Ranzato, Rajat Monga, Matthieu Devin, Kai Chen, Greg S. Corrado, Jeffrey Dean and Andrew Y. Ng in ICML 2012.
Semantic Compositionality through Recursive Matrix-Vector Spaces
Richard Socher, Brody Huval, Christopher D. Manning and Andrew Y. Ng in EMNLP 2012.
End-to-End Text Recognition with Convolutional Neural Networks
Tao Wang, David J. Wu, Adam Coates and Andrew Y. Ng in ICPR 2012.
Selecting Receptive Fields in Deep Networks
Adam Coates and Andrew Y. Ng in NIPS 2011.
ICA with Reconstruction Cost for Efficient Overcomplete Feature Learning
Quoc V. Le, Alex Karpenko, Jiquan Ngiam and Andrew Y. Ng in NIPS 2011.
Sparse Filtering
Jiquan Ngiam, Pangwei Koh, Zhenghao Chen, Sonia Bhaskar and Andrew Y. Ng in NIPS 2011.
Unsupervised Learning Models of Primary Cortical Receptive Fields and Receptive Field Plasticity
Andrew Saxe, Maneesh Bhand, Ritvik Mudur, Bipin Suresh and Andrew Y. Ng in NIPS 2011.
Dynamic Pooling and Unfolding Recursive Autoencoders for Paraphrase Detection
Richard Socher, Eric H. Huang, Jeffrey Pennington, Andrew Y. Ng, and Christopher D. Manning in NIPS 2011.
Semi-Supervised Recursive Autoencoders for Predicting Sentiment Distributions
Richard Socher, Jeffrey Pennington, Eric Huang, Andrew Y. Ng, and Christopher D. Manning in EMNLP 2011.
Text Detection and Character Recognition in Scene Images with Unsupervised Feature Learning
Adam Coates, Blake Carpenter, Carl Case, Sanjeev Satheesh, Bipin Suresh, Tao Wang, David Wu and Andrew Y. Ng in ICDAR 2011.
Parsing Natural Scenes and Natural Language with Recursive Neural Networks
Richard Socher, Cliff Lin, Andrew Y. Ng and Christopher Manning in ICML 2011.
The Importance of Encoding Versus Training with Sparse Coding and Vector Quantization
Adam Coates and Andrew Y. Ng in ICML 2011.
On Optimization Methods for Deep Learning
Quoc V. Le, Jiquan Ngiam, Adam Coates, Abhik Lahiri, Bobby Prochnow and Andrew Y. Ng in ICML 2011.
Learning Deep Energy Models
Jiquan Ngiam, Zhenghao Chen, Pangwei Koh and Andrew Y. Ng in ICML 2011.
Multimodal Deep Learning
Jiquan Ngiam, Aditya Khosla, Mingyu Kim, Juhan Nam, Honglak Lee and Andrew Y. Ng in ICML 2011.
On Random Weights and Unsupervised Feature Learning
Andrew Saxe, Pangwei Koh, Zhenghao Chen, Maneesh Bhand, Bipin Suresh and Andrew Y. Ng in ICML 2011.
Learning Hierarchical Spatio-Temporal Features for Action Recognition with Independent Subspace Analysis
Quoc V. Le, Will Zou, Serena Yeung and Andrew Y. Ng in CVPR 2011.
An Analysis of Single-Layer Networks in Unsupervised Feature Learning
Adam Coates, Honglak Lee and Andrew Ng in AISTATS 14, 2011.
Learning Word Vectors for Sentiment Analysis
Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, and Christopher Potts in ACL 2011.
A Low-cost Compliant 7-DOF Robotic Manipulator
Morgan Quigley, Alan Asbeck and Andrew Y. Ng in ICRA 2011.
Grasping with Application to an Autonomous Checkout Robot
Ellen Klingbeil, Deepak Drao, Blake Carpenter, Varun Ganapathi, Oussama Khatib, Andrew Y. Ng in ICRA 2011.
Autonomous Sign Reading for Semantic Mapping
Carl Case, Bipin Suresh, Adam Coates and Andrew Y. Ng in ICRA 2011.
Learning Continuous Phrase Representations and Syntactic Parsing with Recursive Neural Networks
Richard Socher, Christopher Manning and Andrew Ng in NIPS 2010.
A Probabilistic Model for Semantic Word Vectors
Andrew Maas and Andrew Ng in NIPS 2010.
Tiled Convolutional Neural Networks
Quoc V. Le, Jiquan Ngiam, Zhenghao Chen, Daniel Chia, Pangwei Koh and Andrew Y. Ng in NIPS 2010.
Energy Disaggregation via Discriminative Sparse Coding
J. Zico Kolter and Andrew Y. Ng in NIPS 2010.
Autonomous Helicopter Aerobatics through Apprenticeship Learning
Pieter Abbeel, Adam Coates and Andrew Y. Ng in IJRR 2010.
Autonomous Operation of Novel Elevators for Robot Navigation
Ellen Klingbeil, Blake Carpenter, Olga Russakovsky and Andrew Y. Ng in ICRA 2010.
Learning to Grasp Objects with Multiple Contact Points
Quoc Le, David Kamm and Andrew Y. Ng in ICRA 2010.
Multi-Camera Object Detection for Robotics
Adam Coates and Andrew Y. Ng in ICRA 2010.
A Probabilistic Approach to Mixed Open-loop and Closed-loop Control, with Application to Extreme Autonomous Driving
J. Zico Kolter, Christian Plagemann, David T. Jackson, Andrew Y. Ng and Sebastian Thrun in ICRA 2010.
Grasping Novel Objects with Depth Segmentation
Deepak Rao, Quoc V. Le, Thanathorn Phoka, Morgan Quigley, Attawith Sudsand and Andrew Y. Ng in IROS 2010.
Low-cost Accelerometers for Robotic Manipulator Perception
Morgan Quigley, Reuben Brewer, Sai P. Soundararaj, Vijay Pradeep, Quoc V. Le and Andrew Y. Ng in IROS 2010.
A Steiner Tree Approach to Object Detection
Olga Russakovsky and Andrew Y. Ng in CVPR 2010.
Measuring Invariances in Deep Networks
Ian J. Goodfellow, Quoc V. Le, Andrew M. Saxe, Honglak Lee and Andrew Y. Ng in NIPS 2009.
Unsupervised Feature Learning for Audio Classification Using Convolutional Deep Belief Networks
Honglak Lee, Yan Largman, Peter Pham and Andrew Y. Ng in NIPS 2009.
Convolutional Deep Belief Networks for Scalable Unsupervised Learning of Hierarchical Representations
Honglak Lee, Roger Grosse, Rajesh Ranganath and Andrew Y. Ng in ICML 2009.
Large-scale Deep Unsupervised Learning using Graphics Processors
Rajat Raina, Anand Madhavan and Andrew Y. Ng in ICML 2009.
A majorization-minimization algorithm for (multiple) hyperparameter learning
Chuan Sheng Foo, Chuong Do and Andrew Y. Ng in ICML 2009.
Regularization and Feature Selection in Least-Squares Temporal Difference Learning
J. Zico Kolter and Andrew Y. Ng in ICML 2009.
Near-Bayesian Exploration in Polynomial Time
J. Zico Kolter and Andrew Y. Ng in ICML 2009.
Policy Search via the Signed Derivative
J. Zico Kolter and Andrew Y. Ng in RSS 2009.
Joint Calibration of Multiple Sensors
Quoc Le and Andrew Y. Ng in IROS 2009.
Scalable Learning for Object Detection with GPU Hardware
Adam Coates, Paul Baumstarck, Quoc Le, and Andrew Y. Ng in IROS 2009.
Exponential Family Sparse Coding with Application to Self-taught Learning
Honglak Lee, Rajat Raina, Alex Teichman and Andrew Y. Ng in IJCAI 2009.
Apprenticeship Learning for Helicopter Control
Adam Coates, Pieter Abbeel and Andrew Y. Ng in Communications of the ACM, Volume 52, 2009.
ROS: An Open-Source Robot Operating System
Morgan Quigley, Brian Gerkey, Ken Conley, Josh Faust, Tully Foote, Jeremy Leibs, Eric Berger, Rob Wheeler, and Andrew Y. Ng in ICRA 2009.
High-Accuracy 3D Sensing for Mobile Manipulation: Improving Object Detection and Door Opening
Morgan Quigley, Siddharth Batra, Stephen Gould, Ellen Klingbeil, Quoc Le, Ashley Wellman and Andrew Y. Ng in ICRA 2009.
Stereo Vision and Terrain Modeling for Quadruped Robots
J. Zico Kolter, Youngjun Kim and Andrew Y. Ng in ICRA 2009.
Task-Space Trajectories via Cubic Spline Optimization
J. Zico Kolter and Andrew Y. Ng in ICRA 2009.
Learning Sound Location from a Single Microphone
Ashutosh Saxena and Andrew Y. Ng in ICRA 2009.
Learning 3-D Object Orientation from Images
Ashutosh Saxena, Justin Driemeyer and Andrew Y. Ng in ICRA 2009.
Reactive Grasping Using Optical Proximity Sensors
Kaijen Hsiao, Paul Nangeroni, Manfred Huber, Ashutosh Saxena and Andrew Y. Ng in ICRA 2009。
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