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Generative modeling is currently one of the most thrilling domains in deep learning research. Traditional models like Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) have already demonstrated impressive capabilities in synthetically generating realistic data, such as images and text. However, diffusion models is swiftly gaining prominence as a powerful model in the arena of high-quality and stable generative modeling. This blog explores diffusion models, examining their operational mechanisms, architectural designs, training processes, sampling methods, and the key advantages that position them at the forefront of generative AI....

As machine learning systems are increasingly used in critical areas like finance, employment, and criminal justice, it’s essential to ensure these models are fair and do not discriminate against certain groups. In this post, I will explore the concept of fairness in machine learning. Defining Fairness Fairness in machine learning can be understood in several ways: Group Fairness: This implies equal treatment or outcomes for different groups categorized by sensitive attributes like race or gender....