Pivot, Melt, Stack, and Unstack methods in Pandas

5 mins read Data does not come in a usable format by default; a data science professional has to spend 70–80% of their […]

Recommended tools and environment setup for a Data Scientist

16 mins read Intro and motivation In this post, I would like to describe in detail our setup and development environment (hardware and […]

Python testing tutorial using pytest

18 mins read Testing your code brings a wide variety of benefits. It increases your confidence that the code behaves as you expect and […]

Probability Density Estimation: Maximum Likelihood Estimation (MLE), Maximum A Posteriori (MAP), and Bayesian inference

14 mins read Maximum Likelihood Estimation (MLE) and Maximum A Posteriori (MAP) estimation are methods of estimating parameters of statistical models. Despite a […]

Implicit Recommender Systems with Alternating Least Squares

13 mins read In today’s post, we will explain a certain algorithm for matrix factorization models for recommender systems which goes by the […]

How to determine epsilon and MinPts parameters of DBSCAN clustering

9 mins read Every data mining task has the problem of parameters. Every parameter influences the algorithm in specific ways. DBSCAN (Density-Based Spatial […]

A review of Deep learning based recommendation systems

20 mins read INTRODUCTION The number of research publications on deep learning-based recommendation systems has increased exponentially in the past recent years. In […]

Steps to setup PyTorch with GPU for NVIDIA GTX 960m (Asus VivoBook n552vw) in Ubuntu

3 mins read In this post, I’m gonna describe the steps I used to utilize GPU for the PyTorch Deep Learning framework on […]

Basics of Convolutional Neural Networks (CNN) from Deep Learning specialization

8 mins read These notes are taken from the first two weeks of the Convolutional Neural Networks course (part of Deep Learning specialization) by Andrew Ng […]

Machine Learning From Scratch Series: Linear Regression with Gradient Descent

10 mins read In the following sections, we are going to implement linear regression in a step-by-step fashion using just Python and NumPy. We will […]

Machine Learning From Scratch Series: Logistic Regression

10 mins read In this article, we are going to implement the most commonly used Classification algorithm called Logistic Regression. First, we will […]

Restricted Boltzmann Machines (RBMs) Simply Explained

16 mins read Table of Content: Definition & Structure Reconstructions Probability Distributions Code Sample: Stacked RBMS Parameters & k Continuous RBMs Next Steps […]

Data Representation in NumPy

12 mins read The NumPy package is the workhorse of data analysis, machine learning, and scientific computing in the python ecosystem. It vastly simplifies manipulating […]

A tutorial on the basics of Collaborative Filtering based Recommendations with Python implementation

29 mins read Collaborative Filtering is the most common technique used when it comes to building intelligent recommender systems that can learn to […]

A tutorial on Terminator terminal for Linux

6 mins read Linux and Unix administrators usually prefer to work from a terminal. Hence to manage different tasks, the administrator has to […]

Fix low disk space on the filesystem root error by increasing the disk size in a VirtualBox virtual machine

13 mins read Is your VirtualBox virtual machine starting to run out of disk space? No worries, because VirtualBox allows you to easily […]

Image classification example with Gradio and Keras

12 mins read Image classification is a subset of machine learning that categorizes a group of images into labeled classes. We train an […]

What is the Bias-Variance Trade-off?

9 mins read Whenever you are using a Statistical, Econometrical, or Machine Learning model, no matter how simple the model is, you should […]

Common loss functions for training deep neural networks in PyTorch

17 mins read Neural networks can do a lot of different tasks. Whether it’s classifying data, like grouping pictures of animals into cats […]

Illustrated calculation of cross-entropy for binary, multi-class, and multi-label classification

8 mins read Cross-entropy is a commonly used loss function for classification tasks. Let’s see why and where to use it. We’ll start with […]