A Simple Introduction to Complex Stochastic Processes – Part 2
In my first article on this topic (see here) I introduced some of the complex stochastic processes used by Wall Street data scientists, using a simple approach that can ...
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A Simple Introduction to Complex Stochastic Processes – Part 2
In my first article on this topic (see here) I introduced some of the complex stochastic processes used by Wall Street data scientists, using a simple approach that can b...
Credit Risk Prediction Using Artificial Neural Network Algorithm
1 Introduction Credit risk or credit default indicates the probability of non-repayment of bank financial services that have been given to the customers. Credit risk has ...
18 Great Articles About Predictive Analytics
This resource is part of a series on specific topics related to data science: regression, clustering, neural networks, deep learning, Hadoop, decision trees, ensembles, ...
Neural network classification of data using Smile
Data classification is the central data-mining technique used for sorting data, understanding of data and for performing outcome predictions. In this small blog we will u...
AI Transforms Industrial IoT
If you’ve been studying artificial intelligence and its growth, you’ll know that the industry is well past its nascent stage now. There is significant maturity in its...
Debunking Google's Death AI
Having my newsfeed cluttered with articles about Google creating an AI that beats hospitals by predicting death with 95% accuracy (or some other erroneous claim), I dug u...
Exploring Kaggle Titanic data with R Packages naniar and UpSetR
Recently (6/8/2018), I saw a post about a new R package “naniar”, which according to the package documentation, “provides data structures and functions ...
Is it still possible today to become a self-taught data scientist?
If you are an engineer working for a company like Boeing, have processed and leveraged data extensively over years of professional experience, used data science tools and...
Uncovering Anxious Deep Learning for Ease
Deep Learning is an algorithm which has no theoretical limitations of what it can learn; the more data you give and the more computational time you give, the better it is...