Introduction to Pseudo-Labelling : A Semi-Supervised learning technique

We have made huge progress in solving Supervised machine learning problems. That also means that we need a lot of data to build our image classifiers or sales forecasters. The algorithms search patterns through the data again and again. But, that is not how human mind learns. A human brain does not require millions of data for training with multiple iterations of going through the same image for understanding a topic. All it needs is a few guiding points to train itself on the underlying patterns. Clearly, we are missing something in current machine learning approaches. Thankfully, there is a line of research which specifically caters to this question. Can we build a system capable of requiring minimal amount of supervision which can learn majority of the tasks on its own. In this article, I would like to cover one such technique called pseudo-labelling. I will give an intuitive explanation of what pseudo-labelling is and then provide a hands-on implementation of it.

Introducing: Unity Machine Learning Agents

Our two previous blog entries implied that there is a role games can play in driving the development of Reinforcement Learning algorithms. As the world’s most popular creation engine, Unity is at the crossroads between machine learning and gaming. It is critical to our mission to enable machine learning researchers with the most powerful training scenarios, and for us to give back to the gaming community by enabling them to utilize the latest machine learning technologies. As the first step in this endeavor, we are excited to introduce Unity Machine Learning Agents.

GAN-Collection: Collection of various GAN models implemented in torch7

Torch implementation of various types of GANs (e.g. DCGAN, ALI, Context-encoder, DiscoGAN, CycleGAN).

AI industry overview: Revisiting Big Data, Big Model, and Big Compute in the AI era

AI Conference chairs Ben Lorica and Roger Chen reveal the current AI trends they’ve observed in industry.

Monte Carlo Simulations & the “SimDesign” Package in R

Monte Carlo simulations (MCSs) provide important information about statistical phenomena that would be impossible to assess otherwise. This article introduces MCS methods and their applications to research and statistical pedagogy using a novel software package for the R Project for Statistical Computing constructed to lessen the often steep learning curve when organizing simulation code. A primary goal of this article is to demonstrate how well-suited MCS designs are to classroom demonstrations, and how they provide a hands-on method for students to become acquainted with complex statistical concepts. In this article, essential programming aspects for writing MCS code in R are overviewed, multiple applied examples with relevant code are provided, and the benefits of using a generate-analyze-summarize coding structure over the typical “for-loop” strategy are discussed.

Answer probability questions with simulation (part-2)

This is the second exercise set on answering probability questions with simulation. Finishing the first exercise set is not a prerequisite. The difficulty level is about the same – thus if you are looking for a challenge aim at writing up faster more elegant algorithms. As always, it pays off to read the instructions carefully and think about what the solution should be before starting to code. Often this helps you weed out irrelevant information that can otherwise make your algorithm unnecessarily complicated and slow.

Enterprise-ready dashboards with Shiny and databases

Inside the enterprise, a dashboard is expected to have up-to-the-minute information, to have a fast response time despite the large amount of data that supports it, and to be available on any device. An end user may expect that clicking on a bar or column inside a plot will result in either a more detailed report, or a list of the actual records that make up that number. This article will cover how to use a set of R packages, along with Shiny, to meet those requirements.

Networks with R

In order to practice with network data with R, we have been playing with the Padgett (1994) Florentine’s wedding dataset (discussed in the lecture).

Comparing Trump and Clinton’s Facebook pages during the US presidential election, 2016

R has a lot of packages for users to analyse posts on social media. As an experiment in this field, I decided to start with the biggest one: Facebook. I decided to look at the Facebook activity of Donald Trump and Hillary Clinton during the 2016 presidential election in the United States. The winner may be more famous for his Twitter account than his Facebook one, but he still used it to great effect to help pick off his Republican rivals in the primaries and to attack Hillary Clinton in the general election. For this work we’re going to be using the Rfacebook package developed by Pablo Barbera, plus his excellent how-to guide.