Generative AI Deep Dive
Understand the technology behind generative AI at a deeper level. This path covers transformer architectures, diffusion models, training methodologies, RLHF, scaling laws, and the technical innovations driving modern AI capabilities.
What You'll Learn
- Understand transformer architecture and attention mechanisms
- Learn how diffusion models generate images and video
- Explore training techniques including pre-training and RLHF
- Understand scaling laws and their implications
- Learn about mixture-of-experts and efficient architectures
- Explore multimodal model design and training
- Understand current research frontiers and open problems
Course Lessons
Transformer Architecture Explained
25 min readDeep dive into the transformer architecture — self-attention, positional encoding, feed-forward layers, and how they combine to process language.
How LLMs Are Trained
22 min readUnderstand the training pipeline from data collection to pre-training, instruction tuning, and RLHF. Learn what makes training expensive and how it's optimized.
Diffusion Models: From Noise to Images
22 min readLearn how diffusion models work — the forward and reverse process, U-Net and DiT architectures, and conditioning mechanisms like classifier-free guidance.
Scaling Laws and Emergent Capabilities
20 min readExplore the scaling laws that predict model performance, emergent capabilities that appear at scale, and the debate about what scaling can and cannot achieve.
Efficient Architectures: MoE and Beyond
20 min readUnderstand mixture-of-experts, sparse attention, quantization, and other techniques that make large models practical to deploy.
Multimodal AI: Unifying Vision and Language
20 min readLearn how models like GPT-5, Gemini, and Claude process multiple modalities — vision encoders, cross-attention, and unified architectures.
Read lesson →Research Frontiers: What's Next for Generative AI
18 min readSurvey active research areas including world models, reasoning improvements, long-context understanding, and the path to more capable AI systems.
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