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Object detection-the computer vision task dealing with detecting instances of objects of a certain class (e.g., ‘car’, ‘plane’, etc.) in images-attracted a lot of attention from the community during the last 5 years. This strong interest can be explained not only by the importance this task has for many applications but also by the phenomenal advances in this area since the arrival of deep convolutional neural networks (DCNN). This article reviews the recent literature on object detection with deep CNN, in a comprehensive way, and provides an in-depth view of these recent advances. The survey covers not only the typical architectures (SSD, YOLO, Faster-RCNN) but also discusses the challenges currently met by the community and goes on to show how the problem of object detection can be extended. This survey also reviews the public datasets and associated state-of-the-art algorithms. Recent Advances in Object Detection in the Age of Deep Convolutional Neural Networks

ISA Mapper: A Compute and Hardware Agnostic Deep Learning Compiler

I’ve written a bit about automated machine learning in the past. I won’t recap any of the introductory material what I have already covered, but if interested in running down the main points, as well as a foray into the practical, feel free to peruse these articles before moving on.

I was wasting some time on the internet and came across some Palko bait from the website Axios: “Elon Musk says Boring Company’s first tunnel to open in December,” with an awesome quote from this linked post:

At: Monash UniversityLocation: Melbourne, Australia Web: www.monash.eduPosition: Academic Opportunities in Dialogue Research

Halloween is coming..! Halloween is just around the corner, I am still trying to decide which candies to purchase this year for trick-or-treaters.

Introduction to Geospatial Data in Python