MIT Introduction to Deep Learning 6.S191: Lecture 6 with Ava Soleimany.

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Lecture Outline

  • 0:00 – Introduction
  • 0:58 – Course logistics
  • 3:59 – Upcoming guest lectures
  • 5:35 – Deep learning and expressivity of NNs
  • 10:02 – Generalization of deep models
  • 14:14 – Adversarial attacks
  • 17:00 – Limitations summary
  • 18:18 – Structure in deep learning
  • 22:53 – Uncertainty & bayesian deep learning
  • 28:09 – Deep evidential regression
  • 33:08 – AutoML
  • 36:43 – Conclusion

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:

Powerpoint Designer utilizes machine learning to provide users with redesigned slides to maximize their engagement and visual appeal.

Up to 4.1 million Designer slides are created daily and the Designer team is adding new types of content continuously.

Time Index:

  • [02:39] Demo – PowerPoint suggests design ideas to help users build memorable slides effortlessly
  • [03:28] A behind-the-scenes look at how PowerPoint was built to make intelligent design recommendations
  • [04:47] AI focused on intelligently cropping images in photos and centering the objects, positioning the images, and even using multi-label classifiers to determine the best treatment.
  • [06:00] How PowerPoint is solving for Natural Language Processing (NLP).
  • [07:32] Providing recommendations when image choices don’t meet the users’ needs.
  • [09:30] How Azure Machine Learning helps the dev team scale and increase throughput for data scientists.
  • [11:10] How distributed GPUs helps the team work more quickly and run multiple models at once.

Jon Wood shows the new models you can build with the updated ML.NET Model Builder in this video.

Related Links:

QuickLogic Corporation and Antmicro jointly-announced QuickFeather, a small form factor development board designed to enable the next generation of low-power Machine Learning  capable IoT devices.

The QuickFeather board is powered by QuickLogic’s EOS™ S3, the first FPGA-enabled SoC to be fully supported in the Zephyr RTOS, with flexible eFPGA logic integrated with an Arm Cortex®-M4F MCU and functionality such as:

This is the final, part 4 of a four-part series that breaks up a talk that Seth Juarez gave at the Toronto AI Meetup.

Part 1, Part 2 and Part 3 were all about the foundations of machine learning, optimization, models, and even machine learning in the cloud.

In this video Seth shows an actual machine learning problem (see the GitHub repo for the code) that does the important job of distinguishing between tacos and burritos.

The primary concepts included is MLOps both on the machine learning side as well as the deliver side in Azure Machine Learning and Azure DevOps respectively.