Explainable AI (XAI) – Enhancing transparency and interpretability of ML models 🔍 Understanding Explainable AI (XAI) Explainable AI (XAI) refers to methods and techniques in artificial intelligence that make the outputs of machine learning models understandable to humansAs AI ...
MLOps – Operationalizing ML workflows for scalability and efficiency 🔧 Key Components of MLOps Modular Architectures :Adopting microservice-based designs allows for scalable and maintainable ML systems. This approach facilitates independent updates and debugging of in...
AutoML – Automated tools for model selection and hyperparameter tuning. 🔍 What’s New in AutoML for 2025 1. Advanced Hyperparameter Optimization Traditional methods like grid and random search are being overshadowed by more sophisticated techniques such as Bayesian optimi...
Linear Regression Types of Linear Regression Simple Linear Regression : This involves only one independent variable (predictor). The relationship is modeled as a straight line: Y=β0+β1X+ϵY = \beta_0 + \beta_1 X + \epsi...
ROC and AUC 1. ROC Curve: The ROC curve is a graphical representation that shows the diagnostic ability of a binary classification model at various threshold settings. It plots two metrics: True Positive Rate (TP...
Supervised vs Unsupervised Learning 🔍 Supervised Learning Supervised learning is like learning with a teacher. The algorithm is trained on a labeled dataset , meaning each input comes with a correct output. 🧠 How it works: You give the ...
Neural Radiance Fields (NeRF) and 3D Scene Generation Start writing here... Absolutely! Here's a deep dive into Neural Radiance Fields (NeRF) and their exciting role in 3D Scene Generation — a transformative approach to synthesizing highly realistic 3D c...
Diffusion Models and Score-Based Generative Models Start writing here... Absolutely! Here's a complete and accessible breakdown of Diffusion Models and Score-Based Generative Models — two of the most powerful and rapidly evolving approaches in generat...
Vision Transformers (ViT) and Their Variants Start writing here... Absolutely! Here's a thorough yet digestible guide to Vision Transformers (ViTs) and their powerful ecosystem of variants — reshaping the landscape of computer vision. 👁️🗨️ Visi...
Geometric Deep Learning on Graphs and Manifolds Start writing here... Absolutely! Here's a detailed and engaging guide to Geometric Deep Learning — especially as it applies to graphs and manifolds , which are essential for understanding data with n...
Optimization in Non-Convex Landscapes Start writing here... Here’s a rich and structured overview of Optimization in Non-Convex Landscapes — one of the most crucial and fascinating challenges in modern machine learning and deep learning. ...
PAC-Bayes and Generalization Bounds Start writing here... Here’s a comprehensive and intuitive breakdown of PAC-Bayes theory and generalization bounds — a fundamental topic in theoretical machine learning that sheds light on why models ...