Common mistakes and best practices on creating a Dockerfile

8 mins read We work with Dockerfiles on a daily basis; all the code we run for ourselves and for our customers, we […]

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 […]

Guide to different types of Docker Volumes

6 mins read The data generated and used by containers are not persisted after we restart or remove containers. So, we can use Docker […]

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 […]

Fundamentals of statistics for Data Scientists and Analysts with Python Code

36 mins read As Karl Pearson, a British mathematician once stated, Statistics is the grammar of science and this holds especially for Computer and Information […]

Inverse CDF Transform Sampling

6 mins read Overview Inverse transform sampling is a method for generating random numbers from any probability distribution by using its inverse cumulative […]

Monte Carlo Simulation Explained

29 mins read Monte Carlo Methods: I Am Feeling (Un-)Lucky! In short, Monte Carlo methods refer to a series of statistical methods essentially […]

Python command-line interface with Click library

7 mins read Python click tutorial shows how to create command-line interfaces with the click module. Python click Python click module is used to create […]

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 […]

Prune unused Docker objects to alleviate low disk space on the filesystem root issues

4 mins read You can alleviate low disk space on filesystem root issues by pruning redundant docker objects. Docker takes a conservative approach […]

Comparing Python Command-Line Parsing Libraries: Argparse, Docopt, and Click

23 mins read This article uses the following versions of the libraries: (Ignore invoke for now, it’s a special surprise for later!) Command-Line Example 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 […]

Best storage formats to save Pandas dataframes

6 mins read When working on data analytical projects, I usually use Jupyter notebooks and a great pandas library to process and move my data around. It […]

Double DQN and Dueling DQN in Reinforcement Learning

9 mins read In this article, we will see two algorithms that improve upon DQN. These are named Double DQN and Dueling DQN. But first, let’s […]

SumTree data structure for Prioritized Experience Replay (PER) explained with Python Code

14 mins read Weighted sampling from a list-like collection is an important activity in many applications. Weighted sampling involves selecting samples randomly from […]

Shannon entropy and its properties

25 mins read Suppose you are talking with three patients in the waiting room of a doctor’s office. All three of them have […]

Understanding Attention Mechanism in Sequence 2 Sequence Machine Translation

39 mins read Introduction Recurrent Neural Networks (or more precisely LSTM/GRU) have been found to be very effective in solving complex sequence-related problems […]