Tag: machine-learning

A deep technical comparison of the four major GPU compute platforms—when to use each, their trade-offs, ecosystem maturity, and what 2025–2026 has changed.

Learn how prompt tuning uses soft prompts to specialize large language models for specific tasks at a fraction of the cost of full fine-tuning.

Demystify deep learning: understand neural networks, how training works, key architectures, real-world applications, and common pitfalls to avoid.

A deep dive into the emerging threat landscape of agentic AI systems and what security teams need to know in 2026

Learn how Azure AI Video Indexer uses 30+ AI models to extract insights from videos, enabling deep search, content creation, and accessibility features.

Learn how FalkorDB's graph database leverages sparse matrices and GraphRAG to reduce AI hallucinations by 90% while achieving sub-10ms query latency for production ML workloads.

Master TensorFlow fundamentals with hands-on examples, production patterns, and real-world applications for building ML models.

Master AI testing with proven methodologies covering validation, bias detection, continuous monitoring, and production-ready deployment strategies for 2025.

Master Infrastructure as Code for Azure AI workloads using Bicep to deploy GPU clusters, ML workspaces, and OpenAI services efficiently

Master exact inference algorithms for Bayesian networks including variable elimination and junction trees with practical Python implementations.