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.
Episode 57: Neural networks with infinite layers
Episode 56: The graph network
Episode 55: Beyond deep learning
Episode 54: Reproducible machine learning
Episode 53: Estimating uncertainty with neural networks
Episode 52: why do machine learning models fail? [RB]
Episode 51: Decentralized machine learning in the data marketplace (part 2)
Episode 50: Decentralized machine learning in the data marketplace
Episode 49: The promises of Artificial Intelligence
Episode 48: Coffee, Machine Learning and Blockchain
Episode 47: Are you ready for AI winter? [Rebroadcast]
Episode 46: why do machine learning models fail? (Part 2)
Episode 45: why do machine learning models fail?
Episode 44: The predictive power of metadata
Episode 43: Applied Text Analysis with Python (interview with Rebecca Bilbro)
Episode 41: How can deep neural networks reason
Episode 40: Deep learning and image compression
Episode 39: What is L1-norm and L2-norm?
Episode 38: Collective intelligence (Part 2)
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