Deep learning models have shown very promising results in computer vision and sound recognition. As more and more deep learning based systems get integrated in disparate domains, they will keep affecting the life of people. Autonomous vehicles, medical imaging and banking applications, surveillance cameras and drones, digital assistants, are only a few real applications where deep learning plays a fundamental role. A malfunction in any of these applications will affect the quality of such integrated systems and compromise the security of the individuals who directly or indirectly use them.
In this episode, we explain how machine learning models can be attacked and what we can do to protect intelligent systems from being compromised.
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MLOps: the good, the bad and the ugly (Ep. 153)
MLOps: what is and why it is important Part 2 (Ep. 152)
MLOps: what is and why it is important (Ep. 151)
Can I get paid for my data? With Mike Andi from Mytiki (Ep. 150)
Building high-growth data businesses with Lillian Pierson (Ep. 149)
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Polars: the fastest dataframe crate in Rust - with Ritchie Vink (Ep. 146)
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Concurrent is not parallel - Part 2 (Ep. 143)
Concurrent is not parallel - Part 1 (Ep. 142)
Backend technologies for machine learning in production (Ep. 141)
You are the product (Ep. 140)
How to reinvent banking and finance with data and technology (Ep. 139)
What's up with WhatsApp? (Ep. 138)
Is Rust flexible enough for a flexible data model? (Ep. 137)
Is Apple M1 good for machine learning? (Ep.136)
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