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 […]

Machine Learning From Scratch Series: Naive Bayes and Gaussian Naive Bayes

16 mins read Introduction Naïve Bayes algorithm is a supervised classification algorithm based on the Bayes theorem with strong (Naïve) independence among features. In machine learning and data […]

The BERT Model

17 mins read The year 2018 has been an inflection point for machine learning models handling text (or more accurately, Natural Language Processing […]

Using BERT for Sentence Sentiment Classification

11 mins read Progress has been rapidly accelerating in machine learning models that process language over the last couple of years. This progress […]

Seq2Seq models, Attention Mechanism, and Transformers Explained

29 mins read Sequence-to-sequence models are deep learning models that have achieved a lot of success in tasks like machine translation, text summarization, […]

A guide on Gradient Boosting models

22 mins read An introduction to additive modeling Before we get into boosting, let’s look at an example of what mathematicians call additive modeling because […]

ARCH and GARCH models for Time Series Prediction in Python

11 mins read A change in the variance or volatility over time can cause problems when modeling time series with classical methods like […]

Finding and removing seasonality in Time-Series Data with Python

17 mins read Seasonality in Time Series Time series data may contain seasonal variation. Seasonal variation, or seasonality, are cycles that repeat regularly […]

ARIMA and SARIMA for Real-World Time Series Forecasting in Python

15 mins read Time series and forecasting have been some of the key problems in statistics and Data Science. Data becomes a time […]

A review of techniques for Time Series prediction

43 mins read Working with time series data? Here’s a guide for you. In this article, you will learn how to compare and […]

Difference between Probability Density and Probability

5 mins read The probability density at x can be greater than one but then, how can it integrate to one? It’s a […]

What is Conjugate Prior?

5 mins read What is Prior? Prior probability is the probability of an event before we see the data. In Bayesian Inference, the prior […]

Important probability distributions for Data Science with Python code

33 mins read For a data scientist aspirant, Statistics is a must-learn thing. It can process complex and challenging problems in the real […]

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 […]

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 […]

Evolution Strategies as a Scalable Alternative to Reinforcement Learning

16 mins read Evolution strategies (ES) is an optimization technique that’s been known for decades, rivals the performance of standard reinforcement learning (RL) techniques on […]