I needed a bad random number generator for an illustration, and chose RANDU, possibly the worst random number generator that…

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## Seeding Viral Growth: An Application of Graph Embedding

Seeding Viral Growth: An Application of Graph EmbeddingSimulating different seeding techniques to maximize information diffusion in a networkShaw LuBlockedUnblockFollowFollowingApr 15Are Influencers…

Continue Reading## Can You Tell Random and Non-Random Apart?

We need some harder evidence. This is where this easy method I eluded to earlier comes into play. If you…

Continue Reading## Probabilistic Graphical Models: Bayesian Networks

The venue, cuisine, distance from home, pricing etc. In general, we can write a custom program to answer our query…

Continue Reading## Google Adiantum and the ChaCha RNG

The ChaCha cryptographic random number generator is in the news thanks to Google’s Adiantum project. I’ll discuss what’s going on,…

Continue Reading## Real-Time Streaming and Anomaly detection Pipeline on AWS

Real-Time Streaming and Anomaly detection Pipeline on AWSSharmistha ChatterjeeBlockedUnblockFollowFollowingFeb 25Streaming Data is data that is generated continuously by thousands of data…

Continue Reading## How I Built a Simple Command Line App in Ruby with ActiveRecord

puts "SUCCESS" else puts "ERROR: EMPTY DATA" end else puts "ERROR: INVALID DATA" end rescue RestClient::ExceptionWithResponse => e err =…

Continue Reading## Explaining Feature Importance by example of a Random Forest

Source: https://unsplash. com/photos/BPbIWva9BgoExplaining Feature Importance by example of a Random ForestEryk LewinsonBlockedUnblockFollowFollowingFeb 11In many (business) cases it is equally important to…

Continue Reading## The importance of context in data sets: A short experiment

The importance of context in data sets: A short experimentUsing four forecasting methods in the same time series to show…

Continue Reading## Statistics is the Grammar of Data Science — Part 4/5

Statistics is the Grammar of Data Science — Part 4/5Statistics refresher to kick start your Data Science journeySemi KoenBlockedUnblockFollowFollowingFeb 10This is the 4th article…

Continue Reading## How to label text for sentiment analysis — good practises

If you haven’t, here’s a great chance of discovering how hard the task is. I am sure that if you…

Continue Reading## Building an Experimentation Framework for Composite Algorithms

", false));For example, lets run different combination of supervised algorithms on the Ionosphere dataset (https://archive. ics. uci. edu/ml/datasets/Ionosphere!!)AdaBoostM1 / MultiBoostAB…

Continue Reading## Probability — Fundamentals of Machine Learning (Part 1)

By plugging this into the chain rule, we find that in this scenario we get P(x, y) = P(x|y) ⋅…

Continue Reading## How to produce meaningful datasets using only SQL

Now we have something we can use in multiple scenarios. Let the real fun begin!Random boolean valuesWith our new randomNumber function,…

Continue Reading## Everything You Need to Know About Decision Trees

Everything You Need to Know About Decision TreesIntro to decision trees, random forests, bagging, boosting, and the underlying theoryMarco PeixeiroBlockedUnblockFollowFollowingJan 16Photo…

Continue Reading## Understanding Generative Adversarial Networks (GANs)

In this post, we will see that adversarial training is an enlightening idea, beautiful by its simplicity, that represents a…

Continue Reading## Django exception archaeology

$ pip install Django==1.7.1 django-celery==3.1.16 gunicorn==19.1.1 SQLAlchemy==0.9.8 python-logstash==0.4.2 $ django-admin startproject faulty $ cd faulty $ django-admin startapp example I…

Continue Reading## IP Address lookups using Python

There was a script for the C-based lookup, pure python-based lookup and redis-based lookup: c_based.py: from random import randint import…

Continue Reading## An Introduction to Random Forest

They can also be more interpretable than other complex models such as neural networks.The content is organized as follows.What is…

Continue Reading## Random Forest

They can also be more interpretable than other complex models such as neural networks.The content is organized as follows.What is…

Continue Reading## Random forests explained intuitively

One quick example, I use very frequently to explain the working of random forests is the way a company has…

Continue Reading## Using fixed and random effects models for panel data in Python

Specifically, researchers often must decide whether to use a fixed or random effects approach in an analysis like this.In this…

Continue Reading## Curiosity in Deep Reinforcement Learning

Curiosity in Deep Reinforcement LearningUnderstanding Random Network DistillationMichael KlearBlockedUnblockFollowFollowingDec 3Learning to play Montezuma’s Revenge, a previously difficult deep RL task,…

Continue Reading## Building a Random Forest from Scratch & Understanding Real-World Data Products (ML for Programmers – Part 3)

Introduction As data scientists and machine learning practitioners, we come across and learn a plethora of algorithms..It’s important to examine…

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