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Adversarial Examples, Explained
Deep neural networks—the kind of machine learning models that have recently led to dramatic performance improvements in a wide range of applications—are vulnerable to tiny perturbations of their inputs. We investigate how to deal with these vulnerabilities.
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Machine Reading Comprehension: Learning to Ask & Answer
Investigating the dual ask-answer network, covering the embedding, encoding, attention and output layer, as well as the loss function, with code examples to help you get started.
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 10 Best Mobile Apps for Data Scientist / Data Analysts
A collection of useful mobile applications that will help enhance your vital data science and analytic skills. These free apps can improve your listening abilities, logical skills, basic leadership qualities and more.
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 How To Learn Data Science If You’re Broke
A first-hand account on how to learn data science on a budget, with advice covering useful resources, a recommended curriculum, typical concepts, building a portfolio and more.
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BIG, small or Right Data: Which is the proper focus?
For most businesses, having and using big data is either impossible, impractical, costly to justify, or difficult to outsource due to the over demand of qualified resources. So, what are the benefits of using small data?
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Semantic Segmentation: Wiki, Applications and Resources
An extensive overview covering the features of Semantic Segmentation and possible uses for it, including GeoSensing, Autonomous Drive, Facial Recognition and more.
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Top 3 Trends in Deep Learning
We investigate the intermediate stage of deep learning, and the trends that are emerging in response to the challenges at this stage, including Interoperability and the multi-deployment options.
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How to Create a Simple Neural Network in Python
The best way to understand how neural networks work is to create one yourself. This article will demonstrate how to do just that.
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Recent Advances for a Better Understanding of Deep Learning
A summary of the newest deep learning trends, including Non Convex Optimization, Overparametrization and Generalization, Generative Models, Stochastic Gradient Descent (SGD) and more.
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Visualising Geospatial data with Python using Folium
Folium is a powerful data visualization library in Python that was built primarily to help people visualize geospatial data. With Folium, one can create a map of any location in the world if its latitude and longitude values are known. This guide will help you get started.
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10 Best Mobile Apps for Data Scientist / Data Analysts
How To Learn Data Science If You’re Broke
Recent Advances for a Better Understanding of Deep Learning