So what’s new in AI?
Table of Contents

I graduated with a degree in AI when the cost of the equivalent computational power to an iPhone was $50 million. A lot has changed but surprisingly much is still the same.
Source: towardsdatascience.com

I graduated with a degree in AI when the cost of the equivalent computational power to an iPhone was $50 million. A lot has changed but surprisingly much is still the same.
Source: towardsdatascience.com
In this tutorial, I’m going to show you how word vectors work. This tutorial assumes a good amount of Python knowledge, but even if you’re not a Python expert, you should be able to follow along and make small changes to the examples without too much trouble.
Read More
We use big convolution kernels with large strides of four and above to detect object features on the high-resolution RGB input frame. Convolutions for layers with a small number of channels (as it is the case for the RGB input) are comparably cheap, so using big kernels here has almost no effect on the computational costs.
Read More
A few examples of images from the Google-Landmarks dataset, including landmarks such as Big Ben, Sacre Coeur Basilica, the rock sculpture of Decebalus and the Megyeri Bridge, among others.
Read More