It’s hard to imagine a modern, tech-literate business that doesn’t use data analysis, data science, machine learning, or artificial intelligence in some form. NumPy is at the core of all of those fields.
Archives for November 2019
n this tutorial, you will learn how to detect fire and smoke using Computer Vision, OpenCV, and the Keras Deep Learning library.
This is a collection of Jupyter notebooks based on different topics in the area of quantitative finance. Wow!
What it says on the tin.
From GPS navigation to network-layer link-state routing, Dijkstra’s Algorithm powers some of the most taken-for-granted modern services. Utilizing some basic data structures, let’s get an understanding of what it does, how it accomplishes its goal, and how to implement it in Python (first naively, and then with good asymptotic runtime!)
TensorFlow Lite is a framework for running lightweight machine learning models, and it’s perfect for low-power devices like the Raspberry Pi! This video shows how to set up TensorFlow Lite on the Raspberry Pi for running object detection models to locate and identify objects in real-time webcam feeds, videos, or images.
This April a 1.5 billion dollar medicare scheme took advantage of hundreds of thousands of seniors in the US. In reality, this is just a small sliver of the billions of dollars healthcare fraud costs both consumers and insurance providers annually.
Healthcare fraud can come from many different directions. Some people might think of the patient who pretends to be injured, but actually, much of fraud is caused by providers(as in the NYT article).
Providers often have financial incentives for increasing performing unnecessary surgeries or claiming work they never even did. This leads to many different flavors of fraud that can all be difficult to detect on a claim by claim basis.
In this tutorial, you will learn how to train your own traffic sign classifier/recognizer capable of obtaining over 95% accuracy using Keras and Deep Learning.
This article summarizes how to clean up messy currency fields and convert them into a numeric value for further analysis. The concepts illustrated here can also apply to other types of pandas data cleanup tasks.