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	Goodfellow, Ian     Options: All Media Types Journals Proceedings Div Books Whole Books Other Date Reviewed Title Author Publisher Published Date Descending Ascending Date Reviewed     1 - 5 of 5 reviews       Making machine learning robust against adversarial inputs Goodfellow I., McDaniel P., Papernot N. Communications of the ACM 61(7): 56-66, 2018.  Type: Article Machine learning (ML) has become ubiquitous in recent times. It is used in numerous (important) applications and its use will seemingly only increase. The current ML state of the art can be attributed to “nearly 50 years of r...... Oct 15 2018     Deep learning Goodfellow I., Bengio Y., Courville A., The MIT Press, Cambridge, MA, 2016. 800 pp.  Type: Book (978-0-262035-61-3), Reviews: (4 of 4) Deep learning (DL) is an area of artificial intelligence (AI) that carries out machine learning in terms of a hierarchy of concepts. DL enables a machine to build complex concepts by combining simpler ones, and thus relieves a human op...... Aug 16 2017      Deep learning Goodfellow I., Bengio Y., Courville A., The MIT Press, Cambridge, MA, 2016. 800 pp.  Type: Book (978-0-262035-61-3), Reviews: (3 of 4) Deep learning is currently the most popular (and maybe hyped) discipline within artificial intelligence (AI). It is a key component of current speech recognition systems and it has allowed computers to reach human-level performance in ...... Jul 21 2017     Deep learning Goodfellow I., Bengio Y., Courville A., The MIT Press, Cambridge, MA, 2016. 800 pp.  Type: Book (978-0-262035-61-3), Reviews: (2 of 4) Deep learning is used everywhere, from environment perception in self-driving vehicles to natural language understanding, to fraud and real-time threat detection, to name only a few examples. This book introduces both the fundamental a...... Jul 12 2017     Deep learning Goodfellow I., Bengio Y., Courville A., The MIT Press, Cambridge, MA, 2016. 800 pp.  Type: Book (978-0-262035-61-3), Reviews: (1 of 4) Neural net technology, like some teenagers, has grown by fits and starts. Originating as a biological model [1], it was first promoted as a computational tool, the “perceptron,” by Frank Rosenblatt [2]. Minsky and P...... Jun 21 2017
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