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.
More powerful deep learning with transformers (Ep. 84)
[RB] Replicating GPT-2, the most dangerous NLP model (with Aaron Gokaslan) (Ep. 83)
What is wrong with reinforcement learning? (Ep. 82)
Have you met Shannon? Conversation with Jimmy Soni and Rob Goodman about one of the greatest minds in history (Ep. 81)
Attacking machine learning for fun and profit (with the authors of SecML Ep. 80)
[RB] How to scale AI in your organisation (Ep. 79)
Replicating GPT-2, the most dangerous NLP model (with Aaron Gokaslan) (Ep. 78)
Training neural networks faster without GPU [RB] (Ep. 77)
How to generate very large images with GANs (Ep. 76)
[RB] Complex video analysis made easy with Videoflow (Ep. 75)
[RB] Validate neural networks without data with Dr. Charles Martin (Ep. 74)
How to cluster tabular data with Markov Clustering (Ep. 73)
Waterfall or Agile? The best methodology for AI and machine learning (Ep. 72)
Training neural networks faster without GPU (Ep. 71)
Validate neural networks without data with Dr. Charles Martin (Ep. 70)
Complex video analysis made easy with Videoflow (Ep. 69)
Episode 68: AI and the future of banking with Chris Skinner [RB]
Episode 67: Classic Computer Science Problems in Python
Episode 66: More intelligent machines with self-supervised learning
Episode 65: AI knows biology. Or does it?
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