Master machine learning with articles covering supervised and unsupervised learning, model optimization, feature engineering, and ML deployment strategies. Dive deep into algorithms, frameworks, and practical implementations that power predictive analytics, recommendation systems, and intelligent automation.
A technical exploration of Graph Neural Networks (GNNs), explaining how they model complex relationships and their superior performance compared to traditional ML models for network and financial fraud detection.
A practical guide on model quantization (QLoRA, GGML/GGUF) and the benefits of running large language models locally for privacy, cost efficiency, and development workflow.
