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Data Machinist

Data science blog

  • 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

Optimisation

This blog series is about optimisation, including evolutionary optimisation and Bayesian optimisation.

Part 1 – Optimisation Algorithms

Part 2 – A Simple Genetic Algorithm Implementation

Part 3 – Evolutionary Optimisation Libraries

Blog content

  • About
  • Cheat Sheets
    • Conda cheat sheet
    • Docker cheat sheet
    • Git cheat sheet
    • Linux bash cheat sheet
    • Markdown cheat sheet
    • ROS Cheat Sheet
    • Vim cheat sheet
  • Data Machinist
  • Data Science Dictionary
  • Deep Learning
    • Part 1 – The Simplest ANN
    • Part 2 – Adding a Hidden Layer
    • Part 3 – The Single-layer Perceptron
    • Part 4 – The Multi-layer Perceptron
    • Part 5 – Install TensorFlow 2.0 with GPU support on Ubuntu 18.04 using Docker
  • Optimisation
    • Part 1 – Optimisation algorithms
    • Part 2 – A Simple Genetic Algorithm Implementation
    • Part 3 – Evolutionary Optimisation Libraries
  • Reinforcement Learning
    • Part 1 – What is reinforcement learning?
    • Part 10 – Challenges in reinforcement learning
    • Part 2 – Reinforcement learning algorithms taxonomy
    • 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 – Implementing Custom Virtual Environments
      • 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
  • Supervised Learning
    • Part 1 – Classification
      • Logistic regression from scratch
    • Part 2 – Regression Algorithms
      • Linear regression with one variable
  • 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
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