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Deep learning has achieved remarkable success in a wide range of applications such as computer vision, natural language processing, and speech recognition. However, deep neural networks are often criticized for being black-box models that provide overconfident predictions without quantifying the uncertainty in their outputs. Uncertainty quantification is crucial for deploying

Diffusion models are taking the world of AI generation by storm. In this in-depth post, we'll unpack how these models work, their key innovations, and why they are outperforming other generative models like GANs. What is Diffusion? In physics, diffusion is the spread of particles from high to

Fraud is a major issue that costs companies billions of dollars each year. Detecting fraud accurately and in real-time is critical for reducing losses from fraudulent activities. Machine learning provides a powerful set of techniques that can be used to build highly accurate fraud detection systems. In this technical writeup,

XGBoost is a popular gradient-boosting library for building regression and classification models. The core of XGBoost is an ensemble of decision trees. In this post, we'll look at how to visualize and interpret individual trees from an XGBoost model. Let's start by loading a simple sample

LightGBM is a popular and effective gradient boosting framework that is widely used for tabular data and competitive machine learning tasks. However, like all machine learning models, LightGBM has several hyperparameters that can significantly impact model performance. Tuning these hyperparameters is essential for building high-quality LightGBM models. In this comprehensive

Image classification is a fundamental task in computer vision, where the goal is to assign a label or category to an image based on its content. TensorFlow, an open-source machine learning framework developed by Google, provides powerful tools and libraries for building and training image classification models. In this comprehensive