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Learn to implement and train diffusion models in PyTorch, covering unconditional and conditional versions, classifier-free guidance, and exponential moving average. Gain hands-on experience with state-of-the-art generative AI techniques.
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.
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.
Discover how RoPE (Rotary Positional Embeddings) teaches Transformers about data positions in text, images, and videos with clear explanations and PyTorch code implementation.
Comprehensive explanation of diffusion models, covering theory, architecture, math derivation, algorithms, improvements, and results. Includes comparisons to other methods and references to state-of-the-art applications.
Implement Vector Quantized Generative Adversarial Networks (VQGAN) using PyTorch, covering encoder, decoder, codebook, discriminator, and transformer stages for high-resolution image synthesis.
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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