Data Science Austria Featured

Causal vs. Statistical Inference

The problem of chocolate addicted Nobel prize winners Photo by Joanna Kosinska on Unsplash There is one famous study that showed that there is a strong correlation between a country’s chocolate consumption and the number of Nobel prize winners coming from this country. So would you say that chocolate consumption causes one’s

Hypothesis Testing I: Introduction

Hypothesis are our assumptions about the data which may or may not be true. In this post we’ll discuss about the statistical process of evaluating the truthiness of a hypothesis — this process is known as hypothesis testing. Most of the statistical analysis has its genesis in comparing two types of distributions:

Implementing Mixed Membership Stochastic Blockmodel

MMSB: A probabilistic model useful for learning the structure of a graph. Code written in Julia can be found here (Jupyter notebook with the plots and outputs) or here (.jl file). From social networks to protein interactions, graphs have become ubiquitous. Hello! My name is Saumya Shah. This is an

Spice Up Your Python Visualizations with Matplotlib Animations

Animating the Board The part that we’ve been waiting for — animation! First, we need to get some formalities out of the way. The following lines of code create the matplotlib figure that will display our animation. # Required line for plotting the animation%matplotlib notebook# Initialize the plot of the board that will

Backtesting is a fundamental step in testing the viability of your trading ideas and strategies. Here is a simple backtesting implementation in Python. This article showcases a simple implementation for backtesting your first trading strategy in Python. Backtesting is a vital step when building out trading strategies. The core idea here

An “Equation-to-Code” Machine Learning Project Walk-Through — Part 3 SGD

Detailed explanation to implement Stochastic Gradient Descent (SGD) and Mini-Batch Gradient Descent from scratch in Python from Shutterstock Hi, everyone! This is “Equation-to-Code” walk-through part 3. In the previous articles, we talk about in linear separable problem in part 1, and non-linear separable problem in part 2. This time we will

Text Classification in Python

An end to end Machine Learning project Learn to build a text classification model in Python This article is the first of a series in which I will cover the whole process of developing a machine learning project. In this article we focus on training a supervised learning text classification model in

A new Tool to your Toolkit, KL Divergence at Work

1. Analyzing Dataset The Dataset consists of two latent features ‘f1’ and ‘f2’ and the class to which the data-point belongs to, i.e. the positive class or the negative class. Dataset Dataset Visualisation Visualizing the Data with a scatterplot Code used for Visualisation So, we have data points having two

What I learned in RSNA Radiology in the Age of AI Spotlight Course

AI is the New Electricity: The Disruptive Power of AI Applications in Medical Imaging — Andrew Y. Ng, PhD Can’t believe I actually got to meet Dr. Ng Dr. Ng had a great influence from his father. His father created a machine learning algorithm (way back in the day) that aided doctors. Most of

An End to End Introduction to GANs

Training Understanding how the training works in GAN is essential. And maybe a little interesting too. I start by creating our discriminator and generator using the functions defined in the previous section: discriminator = get_disc_normal(image_shape)generator = get_gen_normal(noise_shape) The generator and discriminator are then combined to create the final GAN. discriminator.trainable R 