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Artificial intelligence

 

Section 1:

  • Why AI?
  • Introduction
  • How to get data from practice
  • Some Additional Resources!!

Section 2: Reinforcement Learning

  • Fundamentals of Reinforcement Learning

Section 3: Q-Learning Intuition

  • Plan of Attack
  • What is reinforcement learning?
  • The Bellman Equation
  • The “Plan”
  • Markov Decision Process
  • Policy vs. Plan
  • Adding a “Living Penalty”
  • Q-Learning Intuition
  • Temporal Difference
  • Q-Learning Visualization

Section 4: Self-Driving Car (Deep Q-Learning) part 1

  • Part 1 – An Introduction how Self-Driving Car Works (Deep Q-Learning)

Section 5: Deep Q-Learning Intuition

  • Plan of Attack
  • Deep Q-Learning Intuition – Learning
  • Deep Q-Learning Intuition – Acting
  • Experience Replay
  • Action Selection Policies

Section 6: Installation for Part 1

  • Plan of Attack (Practical Tutorials)
  • Where to get the Materials
  • Windows Option 1: End-to-End installation steps
  • Windows Option 2 – Part A: Installing Ubuntu on Windows
  • Windows Option 2 – Part B: Installing PyTorch and Kivy on your Ubuntu VM
  • Mac or Linux: Installing Anaconda
  • Mac or Linux: Installing PyTorch and Kivy
  • Common Debug Tips
  • Getting Started

Section 7: Creating the environment

  • Self Driving Car – (Step 1 to Step 2)

Section 8: Building an AI

  • Self Driving Car – (Step 3 to Step 16)

Section 9: Playing with the AI

  • Self Driving Car – (Level 1 to Level 4)
  • Challenge Solutions

Section 10: Doom (Deep Convolutional Q-Learning)

  • What are convolutional neural networks and Convolutional Q-Learning?

Section 11: Deep Convolutional Q-Learning Intuition

  • Plan of Attack
  • Deep Convolutional Q-Learning Intuition
  • Eligibility Trace

Section 12: Installation for Part 2

  • Where to get the Materials
  • Installing Open AI Gym and ppaquette
  • Installing Open AI Gym Walk through (Mac Version)
  • Installing Open AI Gym Walk through (Ubuntu Version)
  • Common Debug Tips

Section 13: Building an AI

  • Doom – (Step 1 to Step 17)

Section 14: Playing with the AI

  • Watching our AI play Doom

Section 15:  A3C

  • Introduction

Section 16: A3C Intuition

  • Plan of Attack
  • The three A’s in A3C
  • Actor-Critic
  • Asynchronous
  • Advantage
  • LSTM Layer

Section 17: Installation for Part 3

  • Installing OpenCV

Section 18: Building an AI

  • Breakout – (Step 1 to Step 15)

Section 19:  Artificial Neural Networks

  • What is Deep Learning?
  • Plan of Attack
  • The Neuron
  • The Activation Function
  • How do Neural Networks work?
  • How do Neural Networks learn?
  • Gradient Descent
  • Stochastic Gradient Descent
  • Backpropagation

Section 20: Convolutional Neural Networks

  • Plan of Attack
  • What are convolutional neural networks?
  • Step 1 – Convolution Operation
  • Step 1(b) – ReLU Layer
  • Step 2 – Pooling
  • Step 3 – Flattening
  • Step 4 – Full Connection
  • Summary
  • Softmax & Cross-Entropy