A hyperparameter is a setting or configuration value that defines how a machine learning or AI model learns from data. Unlike model parameters, which are learned automatically during training, hyperparameters are manually set before training begins and control aspects such as learning rate, batch size, number of layers, or regularization strength. The correct selection of hyperparameters greatly influences a model’s performance, accuracy, and ability to generalize to new data. Finding the best combination is often achieved through techniques like grid search, random search, or Bayesian optimization. Effective hyperparameter tuning is essential for optimizing AI systems and achieving stable, efficient learning outcomes.
OpenAI has entered into a strategic agreement with AMD to supply high-performance AI chips, diversify its hardware sources, and build next-gen compute capacity.
Opera today introduced Neon, an AI-powered browser that can run tasks and navigate pages autonomously — aiming to shift web browsing from passive to “agentic.”
AI search startup Perplexity has reportedly raised $200 million at a $20 billion valuation, just two months after a $100 million round – signaling its rapid rise as a major rival to Google.