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Reinforcement & Frontierfirst principles.

DEEP LEARNING • FROM NEURONS TO TRANSFORMERS.

Chapter-by-chapter theory notes, step-by-step textbook exercises, video lectures, and SLO-aligned board examination prep for Reinforcement & Frontier (Deep Learning).

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Neural Networks (18)Training Deep Networks (23)Computer Vision (15)Sequences & Transformers (19)Generative Deep Learning (14)Reinforcement & Frontier (12)
Syllabus

Curriculum Syllabus

Course chapter sequence roadmap and learning outcomes.

Reinforcement & Frontier (Deep Learning)

1. Deep Reinforcement Learning

1.1 RL Basics & Environments
1.2 Policy Gradients
1.3 Markov Decision Processes
1.4 Q-Learning & DQN
1.5 Actor-Critic & PPO

2. Frontier Architectures

2.1 Graph Neural Networks
2.2 Mixture of Experts
2.3 State Space Models
2.4 Neural Architecture Search

3. Deploying Deep Learning Models

3.1 Exporting Models
3.2 Serving & Inference Optimization
3.3 On-Device Deep Learning
INTERACTIVE LAB • 18 STEM WORKSTATIONS

Bridge High-School Science to Computational AI

Connect textbook physics, mathematics, and computing to real-world artificial intelligence models and symbolic solvers.

scienceOpen Interactive Laboratories→Board MCQs & Past Papers