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: … Read moreHypothesis Testing I: Introduction
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 … Read moreImplementing Mixed Membership Stochastic Blockmodel
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 … Read moreSpice Up Your Python Visualizations with Matplotlib Animations
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 … Read moreBacktesting Your First Trading Strategy
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 … Read moreAn “Equation-to-Code” Machine Learning Project Walk-Through — Part 3 SGD
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 … Read moreText Classification in Python
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 … Read moreA new Tool to your Toolkit, KL Divergence at Work
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 … Read moreWhat I learned in RSNA Radiology in the Age of AI Spotlight Course
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 … Read moreAn End to End Introduction to GANs
It’s hard not to love Saturn Cloud. Saturn Cloud video via YouTube (GIF via GIPHY) I recently started using a hosted Jupyter service called Saturn Cloud, and it’s amazing. Saturn Cloud makes life simple. With Saturn Cloud, it’s unbelievably easy to run cloud-hosted Jupyter Notebooks. You click a few buttons … Read moreHow to Effortlessly Create, Publish, and Even Share Cloud-Hosted Jupyter Notebooks