Skip to content
Data Machinist
Data science blog
Menu
Reinforcement Learning
Part 1 – What is reinforcement learning?
Part 2 – Reinforcement learning algorithms
Part 3 – Reinforcement learning for robotics applications
Part 4 – Learning to use OpenAI Gym
Part 5 – Q-learning to solve the taxi problem
Part 6 – Q learning for continuous state problems
Part 7 – Deep Q Learning
Part 8 – Virtual environments for reinforcement learning
Part 8.1 – Registering a Custom Gym Environment
Part 8.2 – Implementing a Simple Gym Environment – Tic-Tac-Toe
Part 8.3 – Getting Started With Pybullet
Part 8.4 – Implementing a Gym environment with Pybullet
Part 9 – Reinforcement learning libraries
Part 10 – Challenges in reinforcement learning
Data Science Dictionary
Deep Learning
Part 1 – The simplest ANN
Part 2 – Adding a hidden layer
Part 3 – Single-layer perceptron
Part 4 – Multi-layer perceptron
Part 5 – Install TensorFlow 2.0 using Docker with GPU support on Ubuntu 18.04
Optimisation
Part 1 – Optimisation algorithms
Part 2 – A Simple Genetic Algorithm Implementation
Part 3 – Evolutionary Optimisation Libraries
Supervised Learning
Part 1 – Classification Algorithms
Logistic regression from scratch
Part 2 – Regression Algorithms
Linear regression with one variable
Cheat Sheets
Linux bash cheat sheet
Docker cheat sheet
Conda cheat sheet
Git cheat sheet
Markdown cheat sheet
Vim cheat sheet
ROS Cheat Sheet
About
Anomaly detection for time series
Part 1 – Classification