Q-Learning in Practice: Understanding the Update Equation with One Simple ExampleIn the previous article, we built the intuition behind Q-Learning. We learned that an agent improves its decisions through trial and error, stores its experience in a Q-table, and gradually discovers Jul 22 · 8 min readml-diaries-by-fahd.hashnode.dev
Q-Learning: How an Agent Learns From Rewards — Intuition First, No MathIn the previous article of this series, Reinforcement Learning: The Big Picture, we explored how reinforcement learning differs from supervised and unsupervised learning. We discussed the interaction Jul 6 · 10 min readml-diaries-by-fahd.hashnode.dev
Reinforcement Learning: The Big PictureIn the previous posts of this series, we explored Supervised Learning, where models learn from labeled data, and unsupervised learning, where patterns are discovered without labels. But what if there are no labels, no predefined groups, and no clear ...Jan 19 · 4 min readml-diaries-by-fahd.hashnode.dev
Apriori Algorithm — Learning Association Rules the Simple WayAs part of our unsupervised learning series today, we will talk about a famous algorithm of an association type. While exploring unsupervised learning, I came across the Apriori Algorithm. At first, it sounded complex, but once I broke it down with r...Jan 16 · 4 min readml-diaries-by-fahd.hashnode.dev
PCA Explained Simply: Understanding Dimensionality ReductionIn our previous article on unsupervised learning, we covered a clustering algorithm. Today we will be covering a common algorithm that comes under the category of dimensionality reduction. So, in machine learning, having more data is usually helpful ...Jan 12 · 6 min readml-diaries-by-fahd.hashnode.dev
