Digests » 3
With the World Cup 2018 coming up this summer in Russia, every soccer fan around the world is eager to make his prediction on what team will win this year. Another looming question for the fans is how their favorite national teams should line up: What formation should be used? Which players should be chosen? Which ones should be left on the bench or eliminated from the tournament?
Whether you are an established company or working to launch a new service, you can always leverage text data to validate, improve, and expand the functionalities of your product. The science of extracting meaning and learning from text data is an active topic of research called Natural Language Processing (NLP).
I’m just very tired of the same implementation everywhere in the internet. Though it is from scratch, here I don’t explain the theory because you can get many better explanations online with visualizations too. However, Execution and CNNs are briefly explained.
In this blog, I gonna share with you my path to becoming a machine learning engineer or how to learn machine learning the hard way.
Deep Learning, to a large extent, is really about solving massive nasty optimization problems. A Neural Network is merely a very complicated function, consisting of millions of parameters, that represents a mathematical solution to a problem. Consider the task of image classification. AlexNet is a mathematical function that takes an array representing RGB values of an image, and produces the output as a bunch of class scores.