吴恩达的主要成就

吴恩达的人物经历~

吴恩达1976年出生于伦敦，父亲是一位香港医生 ，英文名叫Andrew Ng，吴恩达年轻时候在香港和新加坡度过。1992年吴恩达就读新加坡莱佛士书院，并于1997年获得了卡内基梅隆大学的计算机科学学士学位。之后他在1998年获得了麻省理工学院的硕士学位，并于2002年获得了加州大学伯克利分校的博士学位，并从这年开始在斯坦福大学工作。他（2002年）住在加利福尼亚州的帕洛阿尔托。吴恩达是斯坦福大学计算机科学系和电子工程系副教授，人工智能实验室主任。吴恩达主要成就在机器学习和人工智能领域，他是人工智能和机器学习领域最权威的学者之一。2010年，时任斯坦福大学教授的吴恩达加入谷歌开发团队XLab——这个团队已先后为谷歌开发无人驾驶汽车和谷歌眼镜两个知名项目。吴恩达与谷歌顶级工程师开始合作建立全球最大的“神经网络”，这个神经网络能以与人类大脑学习新事物相同的方式来学习现实生活。谷歌将这个项目命名为“谷歌大脑”。吴恩达最知名的是，所开发的人工神经网络通过观看一周YouTube视频，自主学会识别哪些是关于猫的视频。这个案例为人工智能领域翻开崭新一页。吴恩达表示，未来将会在谷歌无人驾驶汽车上使用该项技术，来识别车前面的动物或者小孩，从而及时躲避。2014年5月16日，百度宣布吴恩达加入百度，担任百度公司首席科学家，负责百度研究院的领导工作，尤其是Baidu Brain计划。 2014年5月19日，百度宣布任命吴恩达博士为百度首席科学家，全面负责百度研究院。这是中国互联网公司迄今为止引进的最重量级人物。消息一经公布，就成为国际科技界的关注话题。美国权威杂志《麻省理工科技评论》(MIT Technology Review)甚至用充满激情的笔调对未来给予展望：“百度将领导一个创新的软件技术时代，更加了解世界。”

吴恩达早期的工作包括斯坦福自动控制直升机项目，吴恩达团队开发了世界上最先进的自动控制直升机之一。
吴恩达同时也是机器学习、机器人技术和相关领域的100多篇论文的作者或合作者，他在计算机视觉的一些工作被一系列的出版物和评论文章所重点引用。 早期的另一项工作是the STAIR (Stanford Artificial Intelligence Robot) project，即斯坦福人工智能机器人项目，项目最终开发了广泛使用的开源机器人技术软件平台ROS。
2011年，吴恩达在谷歌成立了“Google Brain”项目，这个项目利用谷歌的分布式计算框架计算和学习大规模人工神经网络。这个项目重要研究成果是，在16000个CPU核心上利用深度学习算法学习到的10亿参数的神经网络，能够在没有任何先验知识的情况下，仅仅通过观看无标注的YouTube的视频学习到识别高级别的概念，如猫，这就是著名的“Google Cat”。这个项目的技术已经被应用到了安卓操作系统的语音识别系统上。 吴恩达是在线教育平台Coursera的联合创始人，吴恩达在2008年发起了“Stanford Engineering Everywhere”（SEE）项目，这个项目把斯坦福的许多课程放到网上，供免费学习。NG也教了一些课程，如机器学习课程，包含了他录制的视频讲座和斯坦福CS299课程的学生材料。
吴恩达的理想是让世界上每个人能够接受高质量的、免费的教育。今天，Coursera和世界上一些顶尖大学的合作者们一起提供高质量的免费在线课程。Coursera是世界上最大的MOOC平台。 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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胶州市18060007355： 如何评价吴恩达的学术地位 - ？
释岩醋酸： 首先我觉得Andrew Ng是偏工程一点的,你在google学术上搜machine learning,他确实排不到第一页,他在讲课的时候也说过他和他的学生发会议多一点.但是这也绝对不能成为vczh黑他的理由,国内有多少人是通过看斯坦福cs299或者是网易...

胶州市18060007355： 吴恩达在百度是什么职位? - ？
释岩醋酸： 吴恩达(Andrew Ng),华裔美国人,斯坦福大学计算机科学系和电子工程系副教授,人工智能实验室主任,百度公司首席科学家. 吴恩达是人工智能和机器学习领域国际上最权威的学者之一,也是在线教育平台Coursera的联合创始人(...

胶州市18060007355： 现实中如何研发钢铁侠中的人工智能——贾维斯? - ？
释岩醋酸： 传统计算机基本压榨不出甚么果汁了 应当寄托与量子计算机或生物计算机 暂时没有甚么希望了 冬眠去吧

胶州市18060007355： 为什么要学习吴恩达的机器学习 - ？
释岩醋酸： 我个人觉得是因为他讲的比较透彻生动.从数学原理到具体实现算法,然后作业是具体的实际问题提升你的学习兴趣.同时还包括一些必要的基本注意事项,比如归一化,过拟合等问题,适合入门了解.浏览其他的几本书和课程都比较枯燥些.

胶州市18060007355： 如何看待百度吴恩达对深度学习的评价 - ？
释岩醋酸： 1.很大.以CNN为例,训练需要大量数据,大量迭代,对计算能力的要求很高.故GOOGLE有dist-belief, 百度有minwa, 要么是CPU集群要么是GPU集群,计算能力不够根本玩都玩不过来,在IMAGENET上验证下算法就得十天半个月.2.不太了解.不过听说layer-wise的预训练已经没意义了,只要数据够多,直接训练就行.CNN更是没有预训练的步骤.3.我觉得,是这样,一方面效果已经够好了,很多数据集都到人眼或者超过人眼了,算法已经好到公司都愿意投钱商用了.另一方面,理论的验证需要实践啊,算法的发展和折腾实用效果根本就是一回事啊.

胶州市18060007355： 论述吴门四家的绘画艺术 论述明代绘画的发展状况 八大山人艺术特色 论述清代美术的发展状况,出现了哪些名 - ？
释岩醋酸： 明四家,即吴门四家,是指中国画史上沈周、文徵明、唐伯虎、仇英四位明代画家,他们以新颖的绘画风格和杰出的艺术成就而称誉画坛.中国绘画作品从一定意义上是画家的生活态度、性格气质和艺术表现方法的反映,同时由于绘画风格相近...

胶州市18060007355： 吴恩达的深度学习课程值不值得学 - ？
释岩醋酸： ng工业界确实混得不错,学术界虽然没出什么重大的名堂,名声不至于臭了吧- -人家也能算个大牛了. 要说学术界引领deep learning的是 Yann LeCun Geoffrey Hinton Yoshua Bengio

胶州市18060007355： 小度机器人吴恩达是哪里人 - ？
释岩醋酸： 对于小度机器人赢得胜利,百度首席科学家吴恩达则表示,这次人机大战,是顶级的人脸识别选手和擅长棋类游戏的人工智能比拼.

胶州市18060007355： 现在有没有像贾维斯那种人工智能 - ？
释岩醋酸： 没有

胶州市18060007355： 数据挖掘国内外研究比较好的大师有哪些,他们的研究的方向是哪些 - ？
释岩醋酸： 吴恩达 adrew ng 深度学习……