Towards Data Science highlights this talk from the Toronto Machine Learning Summit, which introduces differential privacy and its use cases, discuss the new component of the TensorFlow Privacy library, and offer real-world scenarios for how to apply the tools.

In recent years, the world has become increasingly data-driven and individuals and organizations have developed a stronger awareness and concern for the privacy of their sensitive data. It has been shown that it is impossible to disclose statistical results about a private database without revealing some information. In fact, the entire database could be recovered from a few query results. Following research on the privacy of sensitive databases, a number of big players such as Google, Apple, and Uber have turned to differential privacy to help guarantee the privacy of sensitive data.

In this video, learn how you can use Azure Event Grid and Azure Machine Learning to trigger and consume machine learnings events. We talk about why eventing is important and how you can enable scenarios such as run failure alerts and retraining models.

Jump To:

  • [00:50] What is Event Grid?
  • [01:32] Why is this useful?
  • [02:32] Demo – How to set up an event subscription
  • [03:40] Demo – How to filter events
  • [05:30] Demo – Logic app example

Links:

Machine Learning with Phil has a great tutorial on how to do Deep Q Learning in PyTorch.

The PyTorch deep learning framework makes coding a deep q learning agent in python easier than ever. We’re going to code up the simplest possible deep Q learning agent, and show that we only need a replay memory to get some serious results in the Lunar Lander environment from the Open AI Gym. We don’t really need the target network, though it has been known to help the deep Q learning algorithm with convergence.