YOLO-NAS: The State of the Art in Machine Vision, with Harpreet Sahota

Deci's YOLO-NAS architecture provides today's state of the art in Machine Vision, specifically the key task of Object Detection. Harpreet Sahota joins us from Deci today to detail YOLO-NAS as well as where Computer Vision is going next.

Harpreet:
• Leads the deep learning developer community at Deci AI, an Israeli startup that has raised over $55m in venture capital and that recently open-sourced the YOLO-NAS deep learning model architecture.
• Through prolific data science content creation, including The Artists of Data Science podcast and his LinkedIn live streams, Harpreet has amassed a social-media following in excess of 70,000 followers.
• Previously worked as a lead data scientist and as a biostatistician.
• Holds a master’s in mathematics and statistics from Illinois State University.

Today’s episode will likely appeal most to technical practitioners like data scientists, but we did our best to break down technical concepts so that anyone who’d like to understand the latest in machine vision can follow along.

In the episode, Harpreet details:
• What exactly object detection is.
• How object detection models are evaluated.
• How machine vision models have evolved to excel at object detection, with an emphasis on the modern deep learning approaches.
• How a “neural architecture search” algorithm enabled Deci to develop YOLO-NAS, an optimal object detection model architecture.
• The technical approaches that will enable large architectures like YOLO-NAS to be compute-efficient enough to run on edge devices.
• His “top-down” approach to learning deep learning, including his recommended learning path.

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

Deep Learning for Machine Vision

In the latest SuperDataScience episode, the brilliant A.I. researcher Deblina Bhattacharjee fills me in on the state of the art in machine vision. From predicting earthquakes to 3D VR experiences based on ordinary 2D art, she's seemingly in on it all!

We also covered:

• Automatic detection of biological cells in medical images

• The typical workday of, and software tools used by, A.I. researchers working at the cutting edge

• The critical math subjects necessary to be an outstanding machine learning practitioner (to my delight, she reeled off the subjects covered in my ML Foundations curriculum)

• The ever-increasing utility of unsupervised learning approaches

• Her top productivity tips (Deblina is unbelievably productive so I greatly value her two cents on this!)

• Prog rock music

• The intelligence of plants (yep, plants!)

This is an outstanding episode. I learned a ton while filming and had a lot of laughs too, so I think you'll enjoy it too.

Listening/viewing options, as well as full transcript, available here.