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Andrew Y. Ng
Researcher at Stanford University
Publications - 356
Citations - 184387
Andrew Y. Ng is an academic researcher from Stanford University. The author has contributed to research in topics: Deep learning & Supervised learning. The author has an hindex of 130, co-authored 345 publications receiving 164995 citations. Previous affiliations of Andrew Y. Ng include Max Planck Society & Baidu.
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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,Andrew Y. Ng +7 more
TL;DR: In this paper, a 9-layered locally connected sparse autoencoder with pooling and local contrast normalization was used to train a face detector without having to label images as containing a face or not.
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Deep Speech: Scaling up end-to-end speech recognition
Awni Hannun,Carl Case,Jared Casper,Bryan Catanzaro,Greg Diamos,Erich Elsen,Ryan Prenger,Sanjeev Satheesh,Shubho Sengupta,Adam Coates,Andrew Y. Ng +10 more
TL;DR: Deep Speech, a state-of-the-art speech recognition system developed using end-to-end deep learning, outperforms previously published results on the widely studied Switchboard Hub5'00, achieving 16.0% error on the full test set.
Proceedings ArticleDOI
Feature selection, L1 vs. L2 regularization, and rotational invariance
TL;DR: A lower-bound is given showing that any rotationally invariant algorithm---including logistic regression with L1 regularization, SVMs, and neural networks trained by backpropagation---has a worst case sample complexity that grows at least linearly in the number of irrelevant features.
Proceedings ArticleDOI
Self-taught learning: transfer learning from unlabeled data
TL;DR: An approach to self-taught learning that uses sparse coding to construct higher-level features using the unlabeled data to form a succinct input representation and significantly improve classification performance.
Journal ArticleDOI
Cardiologist-Level Arrhythmia Detection and Classification in Ambulatory Electrocardiograms Using a Deep Neural Network
Awni Hannun,Pranav Rajpurkar,Masoumeh Haghpanahi,Geoffrey H. Tison,Codie Bourn,Mintu P. Turakhia,Mintu P. Turakhia,Andrew Y. Ng +7 more
TL;DR: It is demonstrated that an end-to-end deep learning approach can classify a broad range of distinct arrhythmias from single-lead ECGs with high diagnostic performance similar to that of cardiologists.