Founder, author
The notebook
Artificial intelligence
Read Grokking AI Algorithms, 2nd Edition
I wrote Grokking Artificial Intelligence Algorithms. The book is a fully-illustrated and interactive tutorial guide to the different approaches and algorithms that underpin AI.
Reinforcement learning with Q-learning
Like other machine learning algorithms, a reinforcement learning model needs to be trained before it can be used. The training phase centers on exploring the environment and receiving feedback, given specific actions performed in specific circumstances or states.
Intuition of reinforcement learning
Reinforcement learning (RL) is an area of machine learning inspired by behavioral psychology. The concept of reinforcement learning is based on cumulative rewards or penalties for the actions that are taken by an agent in a dynamic environment.
Introduction to purpose-specific neural networks
Artificial neural networks (ANNs) are versatile and can be designed to address different problems. Specific architectural styles are useful for solving certain problems. Think of the architecture as being the fundamental configuration of the network.
Intuition of data in artificial neural networks
To explore the workings of multi-node ANNs, consider an example dataset related to car collisions. Suppose that we have data from several cars at the moment that an unforeseen object enters the path of their movement.
Intuition of neurons in artificial neural networks
Artificial neural networks (ANNs) are powerful tools in the machine learning toolkit, used in a variety of ways to accomplish objectives such as image recognition, language processing, and game playing.
ML and decision trees for beginners
Suppose that we have several vehicles that are cars and trucks. We measure the weight of each vehicle and the number of wheels of each vehicle. We also forget for now that cars and trucks look different. How can we make an algorithm tell them apart?
Machine learning flavours
Machine learning is useful only if you have the right data and have questions to ask that it might be able to answer. Machine learning algorithms find patterns in data but cannot do useful things magically.
Machine learning intuition
Machine learning can seem like a daunting concept to learn and apply, but with the right framing and understanding of the process and algorithms, it can be interesting, useful, and fun. Let's explore apartment prices in a city.
Intuition of particle swarm optimization
Swarm intelligence is an amazing phenomena in nature. We see it in flocks of birds, bees in a hive, bacterial growth, and more. The behaviour of these wonderful creatures have been studied and inspired useful algorithms. Here's an introduction to particle swarm optimization.
Learning from ants
Learning from ants: Ant colony optimization algorithms are versatile and useful for several real-world applications. These applications usually center on complex optimization problems.
Ant colony optimization for beginners
A single ant can carry 10 to 50 times its own body weight and run 700 times its body length per minute. These are impressive qualities; however, when acting in a group, that single ant can accomplish much more.
Encoding genetic algorithms
Genetic algorithms are a fascinating technique for solving optimisation problems. If you can create a set of rules that can measure a solution's performance, you can probably use a GA to help solve the problem.
Genetic Algorithms for Beginners
Genetic algorithms are part of the family of optimization algorithms. They operate on the theory of evolution, more particularly, genetic evolution.
Optimization: Finding the best solutions
Imagine how a swarm of bees find food sources. While visiting areas, different bees will find plants of different quality and quantity. Some might be better than others but they gravitate towards the best. Optimisation algorithms in AI work this way too.
Intelligence through evolution
When we look at the world around us, we sometimes wonder how everything we see and interact with came to be. One way to explain this is the theory of evolution. And it's useful in solving computational problems in AI.
Game playing with adversarial algorithms
Do you know how IBM's Deep Blue chess computer controversially beat champion, Gary Kasparov in 1997? It's a search algorithm called min-max. This article describes how it works at a high-level.
Using heuristics for intelligence
When you're deciding if you'd try a specific pizza, you may have some criteria that it passes. The pizza might be made by someone different with a different technique, but as long as it passes your set of rules, you'll try it. This is a heuristic.