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An in-depth exploration of variational autoencoders (VAEs) and latent variable models, covering key concepts, mathematical foundations, and applications in generative modeling.
Explore the two main approaches to representation learning: generative models and self-supervised learning. Understand their key differences and applications in machine learning.
An in-depth exploration of conditional GANs, image-to-image translation, and applications of adversarial networks in machine learning.
An in-depth look at diffusion models, covering Denoising Diffusion Probabilistic Models (DDPMs) and Denoising Diffusion Implicit Models (DDIMs), their key concepts, training procedures, and applications.
An in-depth exploration of Generative Adversarial Networks (GANs), covering the mathematical foundations, architecture, and practical implementation details.
An in-depth exploration of Generative Adversarial Networks (GANs), covering the mathematical foundations, optimization techniques, and practical implementation considerations.
An in-depth exploration of F-divergences, their properties, and how they are used in generative adversarial networks (GANs) for estimating and sampling from unknown probability distributions.
An in-depth exploration of the Transformer architecture that powers large language models, covering key concepts like attention mechanisms, tokenization, and parallelization.
An in-depth exploration of how transformer models work, focusing on the attention mechanism and its role in processing text and other data types.