Every developer needs access to some servers for example to check the application logs. Usually, this is done using public-private key encryption where each developer generates their own public-private key pair. The public keys of each developer are added to the authorized_keys file on each server they should have access to. Painful manual changes So far so good. However, what happens when one developer leaves the company? In that case, the public keys of that developer should be removed from all servers.
Today’s paper: End-to-End object detection with transformers by Carion et al. This is the second paper of the new series Deep Learning Papers visualized and it’s about using a transformer approach (the current state of the art in the domain of speech) to the domain of vision. More specifically, the paper is concerned with object detection and here is the link to the paper of Carion et al. on arxiv.
New blog series: Deep Learning Papers visualized This is the first post of a new series I am starting where I explain the content of a paper in a visual picture-based way. To me, this helps tremendously to better grasp the ideas and remember them and I hope this will be the same for many of you as well. Today’s paper: Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour by Goyal et al.
Recently Swift for Tensorflow has picked up some steam, so I wanted to explore the Swift programming language a bit. The main advantage over Python for Swift is that Swift is very fast by directly using the LLVM compiler infrastructure. Python itself relies a lot on C to make code run fast, but if you write Python code you can get very slow code if it’s not optimized. However, the main disadvantage for Swift is that it’s ecosystem when it comes to machine learning and data processing libraries is currently a lot less powerful than Python’s ecosystem.
As a data scientist, you are dealing a lot with linear algebra and in particular the multiplication of matrices. Important properties of a matrix are its eigenvalues and corresponding eigenvectors. So let’s explore those a bit to get a better intuition of what they tell you about the transformation. We will just need numpy and a plotting library and create a set of points that make up a rectangle (5 points, so they are visually connected in the plot):
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