1. Welcome to the course!

•    Welcome Challenge!
•    Why AI
•    Course Structure
•    Get the PDF Handbook here
•    EXTRA: Use ChatGPT to Build AI More Efficiently  

2.Fundamentals of Reinforcement Learning

3. Learning Institution

• Plan of Attack
• What is reinforcement learning?
• The Bellman Equation
• The "Plan"
• Markov Decision Process
• policy vs Plan
• Q-Learning Intuition
• Temporal Difference

4. Learning Implimentation

• A Q-Learning Implementation for Process Optimization

5. Deep Q-Learning

6.  Deep Q-Learning Institution

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

7.Deep Q-Learning Implimentation

8.Deep Convolutional Q-Learning

9.Deep Convolutional Q-Learning Institution

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

10.Deep Convolutional Q-Learning Implementation

11. A3 Institution

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

12.A3C Implementation

13. PPO and SAC

• Build and Train the PPO and SAC models for a Self-Driving Car! Theory included.

14.Intro to Large Language Models (LLMs)

15.LLMs Institution

• Introduction to LLMs
• Ingredients of an LLM
• Who invented LLMs?
• How LLMs generate text
• Inside an LLM - Under the Hood
• LLM Parameters
• LLM Context Window
• Fine-Tuning LLMs

16. LLMs Implimentation

• Fine-Tuning LLMs with Hugging Face

17. Artificial Neural Network

• 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

18. Convolutional Neural Network

• 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.