Advanced Artificial Intelligence & Neural Architectures

1. 1

   Foundations of Deep Representation Learning

   • Manifold Hypothesis and Latent Space Topology
   • Information Bottleneck Theory in Neural Networks
   • Advanced Gradient Descent: Sharpness-Aware Minimization
   • Universal Approximation Theorem: Beyond the Basics
2. 2

   Transformer Architectures and Attention Mechanisms

   • Multi-Head Latent Attention and FlashAttention
   • Rotary Positional Embeddings (RoPE) and AliBi
   • Sparse Transformers and Linear Complexity Attention
   • Cross-Modal Cross-Attention in Vision Transformers
   • MoE: Scaling with Mixture of Experts
   • Mechanistic Interpretability of Attention Heads
3. 3

   Generative Modeling and Latent Diffusion

   • Score-Based Generative Modeling
   • Variational Autoencoders: Reparameterization and ELBO
   • Denoising Diffusion Probabilistic Models (DDPM)
4. 4

   Reinforcement Learning from Human Feedback (RLHF)

   • Proximal Policy Optimization (PPO) Deep Dive
   • Reward Model Training and Bradley-Terry Ranking
   • Direct Preference Optimization (DPO) and Alternatives
   • KL-Divergence Constraints in Policy Tuning
   • Constitutional AI and RLAIF
5. 5

   Natural Language Processing Frontiers

   • Retrieval-Augmented Generation (RAG) Architectures
   • Chain-of-Thought and Tree-of-Thought Prompting
   • Tokenization Edge Cases and Byte-Pair Encoding
   • Parameter-Efficient Fine-Tuning (LoRA and QLoRA)
6. 6

   Computer Vision and Multimodal Systems

   • Contrastive Language-Image Pre-training (CLIP)
   • Masked Autoencoders (MAE) for Vision
   • Neural Radiance Fields (NeRF) and 3D Synthesis
   • Zero-Shot Object Detection and Segmentation
   • Temporal Consistency in Video Generation
7. 7

   Optimization, Scaling, and Hardware

   • Distributed Training: Data vs Model Parallelism
   • Quantization: INT8, FP8, and 4-bit Precision
   • Hardware-Aware Neural Architecture Search (NAS)
8. 8

   AI Safety, Ethics, and Governance

   • Formal Verification of Neural Networks
   • Adversarial Attacks and Robustness Training
   • Algorithmic Bias Mitigation and Fairness Metrics
   • AI Alignment: Inner vs Outer Alignment

8 sections · 34 units · 170 levels. Built to play, not to enroll.