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Implement diffusion models in PyTorch, including UNet training, classifier-free guidance, exponential moving averages, and conditional CIFAR-10 generation.
Discover how Direct Preference Optimization streamlines LLM training without reward functions, using only preference data for more efficient tuning than PPO methods.
Dive into the mathematical foundations of PPO and GRPO reinforcement learning methods used in LLM alignment, covering derivations from intuitive concepts to final objectives.
Discover how RoPE (Rotary Positional Embeddings) teaches Transformers about data positions in text, images, and videos with clear explanations and PyTorch code implementation.
Dive into Flow Matching's simplified approach to diffusion models through intuitive explanations, mathematical derivations, and hands-on PyTorch implementation for advanced AI image generation.
Explains the intuition, mathematics, architecture, and improvements behind diffusion models for image generation.
Implement VQGAN in PyTorch to generate unseen flower images through a quantized latent space and a two-stage transformer-based training process.
Explore score-based generative models and their connection to diffusion models. Gain intuition on DDPM, DDIM, and EDM through mathematical explanations and visualizations.
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