The goal of this blog post is to give you a hands-on introduction to deep learning. To do this, we will build a Cat/Dog image classifier using a deep learning algorithm called convolutional neural network. This post is divided into 2 main parts. The first part covers some core concepts behind deep learning, while the second part is structured in a hands-on tutorial format. We will use some Python code and a popular open source deep learning framework called Caffe to build the classifier.
In this blog post, I will introduce the building blocks for creating a simple IoT application. To do this, I will use an Arduino microcontroller with a photocell (light intensity sensor), a node.js server for capturing and transferring the data, and a cloud service called plotly to visualise the data. By the end of this tutorial, we will have a functioning IoT application that you can customise to other use-cases.
In this tutorial, I will combine Coursera course catalogue together with social media data to assess the popularity of courses. To to this, I will use the Coursera API to retrieve the course catalogue, I will use the sharecount.com API to get social media metrics for each course, and I will use python's pandas library to query and order the courses by popularity. The technique introduced in this tutorial can be leveraged to other use cases that require a popularity ranking system for measuring the relevance of a list of links.
In this post, I will explain about Schelling's segregation model, implement the same in Python programming language, and explain the power of agent based simulations for understanding complex phenomenon.
Sabermetrics is the apllication of statistical analysis to baseball data in order to measure in-game activity. In this post, I will use Lahman’s Baseball Database and Python programming language to explain some of the techniques used in Sabermetrics.