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PODCAST: Making AI Real – Episode 5: Debunking AI Myths

bridgei2i

He has over a decade of experience in the field of AI, which he started with his paper on machine learning in 2009. Anil: Deep learning systems are essentially large networks with many layers constituting an artificial neuron that fires when a certain set of its input neurons fire. Divyansh: Absolutely. Thanks a lot.

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Adding Common Sense to Machine Learning with TensorFlow Lattice

The Unofficial Google Data Science Blog

TF Lattice offers semantic regularizers that can be applied to models of varying complexity, from simple Generalized Additive Models, to flexible fully interacting models called lattices, to deep models that mix in arbitrary TF and Keras layers. The drawback of GAMs is that they do not allow feature interactions.

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Themes and Conferences per Pacoid, Episode 9

Domino Data Lab

For example, in the case of more recent deep learning work, a complete explanation might be possible: it might also entail an incomprehensible number of parameters. Jupyter Book: Interactive books running in the cloud ” by Chris Holdgraf (2019-03-27). Generally, you cannot get both.

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Explaining black-box models using attribute importance, PDPs, and LIME

Domino Data Lab

The interest in interpretation of machine learning has been rapidly accelerating in the last decade. This can be attributed to the popularity that machine learning algorithms, and more specifically deep learning, has been gaining in various domains. Methods for explaining Deep Learning. Ribeiro, M.

Modeling 139
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Data Science at The New York Times

Domino Data Lab

When he retired in 2009 he had some time on his hands. Here is a picture of The New York Times on its birthday in 1851, and for the vast majority of its lifespan this is pretty much what the user experience of interacting with The New York Times looks like. Editors can interact with this bot. Please help us make sense of it.”