Intro to Vectors and Matrices in Machine Learning
Programming is a great way to get insights about math concepts. You’ll see here tips and tricks to learn math, more specifically linear algebra, from a coding perspective. You’ll see the relationship between Numpy functions and linear algebra abstract concepts. At the end of this mini-tutorial, you’ll know what... Read more
Autograd is PyTorch’s automatic differentiation package. Thanks to it, we don’t need to worry about partial derivatives, chain rule, or anything like it. To illustrate how it works, let’s say we’re trying to fit a simple linear regression with a single feature x, using Mean Squared Error (MSE) as... Read more
Be or Not to be an Anomaly?
An outlier may be defined as an object that is out of ordinary, which differs significantly from the norm. In day to day examples, it could be a baby panda among adult pandas, a champion breaking a world record, or fraud emails in your inbox. Why even bother to... Read more
Continuous Delivery for Machine Learning
Why is bringing machine learning code into production hard? Machine Learning applications are becoming popular in all industries, however, the process for developing, deploying, and continuously improving them is more complex compared to more traditional software, such as a web service or a mobile application. They are subject to... Read more
What is Pruning in Machine Learning?
Pruning is an older concept in the deep learning field, dating back to Yann LeCun’s 1990 paper Optimal Brain Damage. It has recently gained a lot of renewed interest, becoming an increasingly important tool for data scientists. The ability to deploy significantly smaller and faster models has driven most of... Read more
Reinforcement Learning with Ray RLlib
Why Reinforcement Learning? In reinforcement learning (RL), an agent tries to maximize a reward while interacting with an environment. The agent observes the state of the environment, takes an action and observes the reward received (if any) and the new state. Then the agent takes the next action, and... Read more
Best Practices for Dealing with Concept Drift
You trained a machine learning model, validated its performance across several metrics which are looking good, you put it in production, and then something unforeseen happened (a pandemic like COVID-19 arrived) and the model predictions have gone crazy. Wondering what happened? You fell victim to a phenomenon called concept drift.... Read more
Essential Programming | Time Complexity
In computer programming, as in other aspects of life, there are different ways of solving a problem. These different ways may imply different times, computational power, or any other metric you choose, so we need to compare the efficiency of different approaches to pick up the right one. Now,... Read more
Retrieving Webpages Through Python Programming
The internet and the World Wide Web (WWW), is probably the most prominent source of information today. Most of that information is retrievable through HTTP. HTTP was invented originally to share pages of hypertext (hence the name Hypertext Transfer Protocol), which eventually started the WWW.   This process occurs every time we request a web page through our devices. The... Read more
Introduction to Bayesian Deep Learning
Bayes’ theorem is of fundamental importance to the field of data science, consisting of the disciplines: computer science, mathematical statistics, and probability. It is used to calculate the probability of an event occurring based on relevant existing information. Bayesian inference meanwhile leverages Bayes’ theorem to update the probability of... Read more