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。