Under the hood: Facebook Marketplace powered by artificial intelligence

Facebook Marketplace was introduced in 2016 as a place for people to buy and sell items within their local communities. Today in the U.S., more than one in three people on Facebook use Marketplace, buying and selling products in categories ranging from cars to shoes to dining tables. Managing the posting and selling of that volume of products with speed and relevance is a daunting task, and the fastest, most scalable way to handle that is to incorporate custom AI solutions.


How to deliver on Machine Learning projects

As Machine Learning (ML) is becoming an important part of every industry, the demand for Machine Learning Engineers (MLE) has grown dramatically. MLEs combine machine learning skills with software engineering knowhow to find high-performing models for a given application and handle the implementation challenges that come up – from building out training infrastructure to preparing models for deployment. New online resources have sprouted in parallel to train engineers to build ML models and solve the various software challenges encountered. However, one of the most common hurdles with new ML teams is maintaining the same level of forward progress that engineers are accustomed to with traditional software engineering. The most pressing reason for this challenge is that the process of developing new ML models is highly uncertain at the outset. After all, it is difficult to know how well a model will perform by the end of a given training run, let alone what performance could be achieved with extensive tuning or different modeling assumptions.


Measuring Discourse Bias Using Text Network Analysis

In this article I propose a method and a tool to measure the level of bias in discourse based on text network analysis. The measure is based on the structure of text and uses both quantitive and qualitative parameters of a text graph to identify how strongly biased it is. Therefore, it can be used by humans as well as be implemented into various APIs and AI to perform automatic bias analysis.


Why Use Framework for Deep Learning?

You can implement your own deep learning algorithms from scratch using Python or any other programming language. When you start implementing more complex models such as Convolutional Neural Network (CNN) or Recurring Neural Network (RNN) then you will realize that it is not practical to implement very large models from scratch.


Predict-Prescribe-Prevent Analytics Value Cycle

Organizations looking for justification to move beyond legacy reporting, should review this little ditty from the healthcare industry: The Institute of Medicine (IOM) estimates that the United States loses $750 billion annually to medical fraud, inefficiencies, and other siphons in the health-care system. The report identified six major areas of waste: unnecessary services ($210 billion annually); inefficient delivery of care ($130 billion); excess administrative costs ($190 billion); inflated prices ($105 billion); prevention failures ($55 billion), and fraud ($75 billion). Adjusting for some overlap among the categories, the panel settled on an estimate of $750 billion.


Send Desktop Notifications from R in Windows, Linux and Mac

In the age of smartphones, Notifications have become an integral part of life that Smart watches have started popping up to handle our notifications. Desktop Notification is a very good way of letting the user know about a task completion or some information. Say, your model has been running and at the end, you just send a notification of the AUC score. Wouldn’t it be nice to have? With this R package notifier, You can do that.


Semantic Segmentation: Wiki, Applications and Resources

In recent years, machine learning technology centered on deep learning has attracted attention. Self driving cars have inculcated deep learning processes that requires the algorithm to identify and learn from the images fed as raw data. Let’s look at how the need for semantic segmentation has evolved. Initial applications of computer vision required the identification of basic elements such as edges(lines and curves) or, gradients. However understanding an image at pixel level came around only with the coining of full-pixel semantic segmentation. It clusters parts of image together which belong to the same object of interest and hence opens the door to numerous applications.


Community Call – Code Review in the Lab, or … How do you review code that accompanies a research project?

Do you have code that accompanies a research project or manuscript? How do you review and archive that code before you submit a paper? Our next Community Call will present different perspectives on this hot topic, with plenty of time for Q&A.
• What’s the culture of the group around feedback and code collaboration?
• What are the use cases?
• What are some practices that can adopted?


Contingency Analysis using R

In this tutorial, you’ll learn with the help of an example how ‘Contingency Analysis’ or ‘Chi-square test of independence’ works and also how efficiently we can perform it using R.


The Mitchell Paradigm: A Concise Explanation of Learning Algorithms

A single quote from Tom Mitchell can shed light on both the abstract concept and concrete implementations of machine learning algorithms.


r2d3 – R Interface to D3 Visualizations

As part our series on new features in the RStudio v1.2 Preview Release, we’re pleased to announce the r2d3 package, a suite of tools for using custom D3 visualizations with R.


Artificial Neural Networks: Man vs Machine?

Are these hubots something human or some kind of machine? If Human intelligence can quickly tell the difference between the two, machine learning must rely on algorithms like artificial neural networks to make a prediction. Patterned after the structure of the human mind, do ANNs allow machines to think like humans? What exactly are ANNs, how do they work, how do they differ from other machine learning algorithms, and what are their use scenarios in data science?


Get Started with Deep Learning using Keras

Deep Learning is at the forefront of the AI revolution, and for good reasons?-?incredible advances in natural language processing, image recognition, and even computer playing Go, have all been made thanks to the help of deep neural networks.
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