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15 best data science bootcamps for boosting your career

CIO Business Intelligence

The data science path you ultimately choose will depend on your skillset and interests, but each career path will require some level of programming, data visualization, statistics, and machine learning knowledge and skills. On-site courses are available in Munich. Remote courses are also available. Switchup rating: 5.0 (out

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Why you should care about debugging machine learning models

O'Reilly on Data

If you’re using Python and deep learning libraries, the CleverHans and Foolbox packages can also help you debug models and find adversarial examples. 2] The Security of Machine Learning. [3] Figure 1 illustrates an example adversarial search for an example credit default ML model. If so, have fun debugging! [1]

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Proposals for model vulnerability and security

O'Reilly on Data

Watermarking is a term borrowed from the deep learning security literature that often refers to putting special pixels into an image to trigger a desired outcome from your model. A lot of the contemporary academic machine learning security literature focuses on adaptive learning, deep learning, and encryption.

Modeling 219
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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. They also require advanced skills in statistics, experimental design, causal inference, and so on – more than most data science teams will have. Upcoming events.

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Data Science, Past & Future

Domino Data Lab

He was saying this doesn’t belong just in statistics. It involved a lot of work with applied math, some depth in statistics and visualization, and also a lot of communication skills. and drop your deep learning model resource footprint by 5-6 orders of magnitude and run it on devices that don’t even have batteries.