in Data Science
t-SNE and UMAP: The Dark Arts of the Dimensionality Mages
In Part 1 we met the honest mages — those who practised PCA and SVD. Their maps were truthful. Their maps were also, at times, uninspiring.
in Data Science
PCA and SVD: Ancient Arts of the Dimensionality Mages
In the kingdom of Vectoria, all knowledge was stored in the Great Archive — an infinite library where every citizen was described by thousands of scrolls. Height, weight, spending habits, favorite spells, number of dragons owned. The Archive was complete. The Archive was perfect. The Archive was completely unusable.
From Theory to Practice
When I think of Reinforcement Learning I usually think of an agent or robot traveling through a maze, avoiding traps, collecting supplies. In each step it observes its state, tries to estimate what will be the best action to take based on all the experience it gained. The way I visualize it, in each state, the robot scans through a database, looking for all the valid actions it can take in that state, and picks the one with the best chance of being the optimal action - Q Learning is a fundamental Reinforcement Learning algorithm that works similar to this. This post is dedicated to the Q Learning algorithm. By the end of this post you will be able to write your own Q Learning agent and test it in an interactive environment.
A Framework to Learn Reinforcement Learning
A while ago I went to a Meetup about Reinforcement Learning (RL), I got into a conversation with some one that sat next to me. He asked me several question about the subject - What is the difference between RL and supervised/unsupervised learning? What is the difference between several types of algorithms? When would you choose this framework over another one?
A Quick Start Guide to Multi Armed Bandits
Decisions are hard, they have always been. And when you finally find something you like, there is always that thought, in the back of your head - “can I find something better?”.