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Concept 1: Introduction to Reinforcement Learning

1.5 hour comprehensive concept

Concept 2: Q-Learning and Value Functions

1.5 hour comprehensive concept

Concept 3: Deep Q-Networks (DQN)

1.5 hour comprehensive concept

Concept 4: Policy Gradient Methods

1.5 hour comprehensive concept

Concept 5: Actor-Critic Methods

1.5 hour comprehensive concept

Concept 6: Proximal Policy Optimization (PPO)

1.5 hour comprehensive concept

Concept 7: Multi-Armed Bandits and Exploration

1.5 hour comprehensive concept

Concept 8: RL in Practice - Debugging and Deployment

1.5 hour comprehensive concept

Concept 9: Introduction to Generative Models

1.5 hour comprehensive concept

Concept 10: Variational Autoencoders (VAEs)

1.5 hour comprehensive concept

Concept 11: Generative Adversarial Networks (GANs)

1.5 hour comprehensive concept

Concept 12: Advanced GAN Architectures

1.5 hour comprehensive concept

Concept 13: Diffusion Models

1.5 hour comprehensive concept

Concept 14: Transformer Architectures for Generation

1.5 hour comprehensive concept

Concept 15: Large Language Models (LLMs) Fundamentals

1.5 hour comprehensive concept

Concept 16: Reinforcement Learning from Human Feedback (RLHF)

1.5 hour comprehensive concept

Concept 17: Multi-Modal AI - Vision and Language

1.5 hour comprehensive concept

Concept 18: The Future of AI - Integration and Ethics

1.5 hour comprehensive concept

Course Concepts

Concept 1: Introduction to Reinforcement Learning

Concept 2: Q-Learning and Value Functions

Concept 3: Deep Q-Networks (DQN)

Concept 4: Policy Gradient Methods

Concept 5: Actor-Critic Methods

Concept 6: Proximal Policy Optimization (PPO)

Concept 7: Multi-Armed Bandits and Exploration

Concept 8: RL in Practice - Debugging and Deployment

Concept 9: Introduction to Generative Models

Concept 10: Variational Autoencoders (VAEs)

Concept 11: Generative Adversarial Networks (GANs)

Concept 12: Advanced GAN Architectures

Concept 13: Diffusion Models

Concept 14: Transformer Architectures for Generation

Concept 15: Large Language Models (LLMs) Fundamentals

Concept 16: Reinforcement Learning from Human Feedback (RLHF)

Concept 17: Multi-Modal AI - Vision and Language

Concept 18: The Future of AI - Integration and Ethics

Course Concepts

Concept 1: Introduction to Reinforcement Learning

Concept 2: Q-Learning and Value Functions

Concept 3: Deep Q-Networks (DQN)

Concept 4: Policy Gradient Methods

Concept 5: Actor-Critic Methods

Concept 6: Proximal Policy Optimization (PPO)

Concept 7: Multi-Armed Bandits and Exploration

Concept 8: RL in Practice - Debugging and Deployment

Concept 9: Introduction to Generative Models

Concept 10: Variational Autoencoders (VAEs)

Concept 11: Generative Adversarial Networks (GANs)

Concept 12: Advanced GAN Architectures

Concept 13: Diffusion Models

Concept 14: Transformer Architectures for Generation

Concept 15: Large Language Models (LLMs) Fundamentals

Concept 16: Reinforcement Learning from Human Feedback (RLHF)

Concept 17: Multi-Modal AI - Vision and Language

Concept 18: The Future of AI - Integration and Ethics