Basic feature engineering tasks for numeric and categorical data with Python code

34 mins read Machine learning pipelines Any intelligent system basically consists of an end-to-end pipeline starting from ingesting raw data and leveraging data […]

Understanding Expectation-Maximization (EM) algorithm

18 mins read The EM algorithm is often used in machine learning as an algorithm for data clustering.​​ Sometimes, one of the clustering problems […]

A guide to different Cross-Validation methods in Machine Learning

19 mins read In machine learning (ML), generalization usually refers to the ability of an algorithm to be effective across various inputs. It […]

Understanding the Dummy Variable Trap with example

4 mins read Linear regression is a method we can use to quantify the relationship between one or more predictor variables and a response variable. […]

Understanding Alternating Least Squares algorithm for implicit collaborative filtering recommendations

23 mins read Overview We’re going to write a simple implementation of an implicit (more on that below) recommendation algorithm. We want to […]

Implementing Attention Mechanism in Python

7 mins read The attention mechanism was introduced to improve the performance of the encoder-decoder model for machine translation. The idea behind the […]

Understanding AdaBoost algorithm and its mathematics

14 mins read If you’re going through this tutorial, you’ve probably heard of XGBoost, LightGBM, or something of those sorts before. These are […]

An illustrated guide to Attention Mechanism in Sequence Models with PyTorch code

22 mins read In this article, I will be covering the main concepts behind Attention, including the implementation of a sequence-to-sequence Attention model, […]

Understanding Self-Attention in Transformers with example

10 mins read What do BERT, RoBERTa, ALBERT, SpanBERT, DistilBERT, SesameBERT, SemBERT, SciBERT, BioBERT, MobileBERT, TinyBERT and CamemBERT all have in common? And […]

Theory of Generalization: growth function, dichotomies, and break points

15 mins read The size of our data set N plays a major role when it comes to the reliability of the generalization Ein […]

Mathematical view of Bias-Variance trade-off

6 mins read The bias-variance trade-off is an important concept in statistics and machine learning. This is used to get better performance out […]

Walk-forward optimization for algorithmic trading strategies on cloud architecture

11 mins read Table of Contents: Introduction Terminology Walk-forward Optimization Design of walk-forwards The Architecture Configuring cloud machines using Ansible Docker Swarm Optimization […]

Understanding Gaussian Process

79 mins read Gaussian Process is a machine learning technique. You can use it to do regression, classification, among many other things. Being […]

Solving six problems with Bayesian statistics

8 mins read 1) The first one is a warm-up problem. Suppose there are two full bowls of cookies. Bowl #1 has 10 […]

Bahdanau and Luong Attention Mechanisms explained

11 mins read Conventional encoder-decoder architectures for machine translation encoded every source sentence into a fixed-length vector, irrespective of its length, from which […]

Difference between Maximum Likelihood Estimation (MLE) and Maximum A Posteriori (MAP)

4 mins read Maximum Likelihood Estimation (MLE) and Maximum A Posteriori (MAP), are both methods for estimating variable from probability distributions or graphical […]

Understanding Expectation-Maximization (EM) algorithm with an example in Python

7 mins read Suppose we have some data sampled from two different groups, red and blue: Here, we can see which data point […]

Importance Sampling in Reinforcement Learning

5 mins read Motivation Importance sampling plays a key role in sampling inferencing and reinforcement learning RL. In RL, importance sampling estimates the […]

REINFORCE Algorithm explained in Policy-Gradient based methods with Python Code

17 mins read Policy gradients Policy gradients is a family of algorithms for solving reinforcement learning problems by directly optimizing the policy in […]

Deep Reinforcement Learning: Using policy-based methods to play Pong from pixels

34 mins read This is a long-overdue blog post on Reinforcement Learning (RL). RL is hot! You may have noticed that computers can […]