Month: October 2018

Qt Design Studio 1.0 Released

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AI Knowledge Map: how to classify AI technologies

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I. Introductory thoughts Ihave been in the space of artificial intelligence for a while, and I am aware that multiple classifications, distinctions, landscapes, and infographics exist to represent and track the different ways to think about AI. However, I am not a big fan of those categorization exercises, mainly because I tend to think that […]

[Announce] Qt Creator 4.7.2 released

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Qt Creator 4.7.2 released  

Simple Method of Creating Animated Graphs

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[A – CORRIGER –> Add plugging for source codes] Data Science Toolkit Introduction After the publication of one of my latest articles, many people asked me for tips on how to create animated charts in Python. Indeed, there are often situations when a static chart is no longer sufficient and in order to illustrate the problem we […]

An Introduction to GPU Programming in Julia

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[A-FORMATTER] How does the GPU work This article aims to give a quick introduction about how GPUs work and specifically give an overlook of the current Julia GPU ecosystem and how easy it is to get simple GPU programs running. To make things easier, you can run all the code samples directly in the article […]

Time Series Analysis using R

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Learn Time Series Analysis with R along with using a package in R for forecasting to fit the real-time series to match the optimal model. Time Series is the measure, or it is a metric which is measured over the regular time is called as Time Series. Time Series Analysis example are Financial, Stock prices, […]

Machine Learning Basics

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We have all been associated with learning since the time we have come into this world. From learning to talk, walk and eat to learning skills like cooking, dancing or singing, we never stop learning! But in today’s world, learning is not just limited to humans. As machines have taken up many of the manual […]

An Introduction to Causal Inference with Gaussian Processes, Part I

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[TEST-ONLY] Introduction Recently, we developed a novel approach for causal inference in time series data [Ambrogioni et al., 2017]. We call this method ‘GP CaKe’, which stands for Gaussian Processes with Causal Kernels, and it does not only have a tasty acronym, but also provides an elegant combination of the attractive features of vector autoregression models […]

Cosine Similarity – Understanding the math and how it works (with python codes)

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[TEXT-ONLY] Cosine similarity is a metric used to measure how similar the documents are irrespective of their size. Mathematically, it measures the cosine of the angle between two vectors projected in a multi-dimensional space. The cosine similarity is advantageous because even if the two similar documents are far apart by the Euclidean distance (due to […]

How to autogenerate forms in React and Material-UI with MSON

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Implementing great forms can be a real time-waster. With just a few lines of JSON, you can use MSON to generate forms that perform real-time validation and have a consistent layout. And, MSON comes with a bunch of cool stuff like date pickers, masked fields and field collections. Disclaimer: this post is geared towards those wishing to use Material-UI […]