https://towardsdatascience.com/programming/home

-

Comments

Post a Comment

Popular posts from this blog

In-Browser object detection using YOLO and TensorFlow.js

-
Image
ffff  ome time ago, I spent several evenings playing around with state of the art object detection model called YOLO, which is certainly known to those who are interested in Machine Learning on a daily basis. Originally written in Darknet  — open source neural network framework — YOLO performs really well in the tasks of locating and recognizing objects on the pictures. Due to the fact that I have been interested in TensorFlow.js for a few weeks now, I decided to check how YOLO will handle the limitations of In-Browser computing. The entire source code as well as my previous TF.js projects can be found on GitHub . If you want to play with the demo version, visit “I Learn Machne Learning” project website . Note: I also recommend reading an article that recently appeared on Towards Data Science, which was the starting point for my project. I will try not to duplicate the information contained in it but rather broaden the topics raised in it and hopefully enri...
Read more

Wordcloud Web App In 40 Lines of R

-
Image
f R is having quite the growth spurt these days . This popularity may seem peculiar given its statistical roots. However, through the amazing work of Hadley Wickham , and the entire R Studio team, this is changing. One example of this newfound power is being able to write crisp web apps in a laughably minuscule amount of code. In this piece, I’ll walk through how to create a simple wordcloud web app above using just R! No Javascript, HTML or CSS whatsoever. Final project Up & Running Simple ‘Hello World’ with R + Shiny The pathway to Hello World is straightforward. You’ll need to install R. For Mac users the simplest way is Homebrew . If not, you can always get it from CRAN . You get an R console by simply typing R at your terminal of choice. Next, you’ll want to create a file called app.R with the code below. The main driver behind everything we will be doing is an R package called Shiny . What exactly is this magical package? S...
Read more

Analyzing my Google Location History

-
Image
I recently read this article on how to create a heat-map from your Google Location History data. Trying it out myself, I got some amazing results: The large red circles represent the cities I have spent a significant amount of time. The purple shade in different places represents places I had travelled to or passed through while on a train journey. My previous phone had some GPS issues, which was leading to my location being shown in Arizona, USA!. Surprisingly (or not?) it even gave a proof of that! All this was really cool to look at, but I really wanted to dive in and get some more insights into my travelling patterns throughout the years. Data Pre-Processing Like most data science problems, data pre-processing was definitely the pain point. The data was in a JSON format where the meaning of different attributes wasn’t very clear. Data Extraction {'timestampMs': '1541235389345', 'latitudeE7': 286648226, 'longitudeE7...
Read more