Machine learning algorithms are used everywhere from a smartphone to a spacecraft. They tell you the weather forecast for tomorrow, translate from one language into another, and suggest what TV series ...

While it might not be an exciting problem front and center of AI conversations, the issue of efficient hyperparameter tuning for neural network training is a tough one. There are some options that aim ...

Intel Corp. today said that it’s buying Andreessen Horowitz-backed artificial intelligence startup SigOpt Inc. for an undisclosed sum. SigOpt, based in San Francisco, develops a software platform that ...

When it comes to building effective machine learning models, selecting the optimal set of hyperparameters is crucial. Hyperparameters are parameters that govern the behaviour and performance of a ...

In the realm of machine learning, the performance of a model often hinges on the optimal selection of hyperparameters. These parameters, which lie beyond the control of the learning algorithm, dictate ...

Recent benchmarks show that suboptimal hyperparameter choices can slash a model’s accuracy by 20%. This critical insight inspired a comprehensive review co-authored by Mr. Ikenna Odezuligbo, published ...

A value that directs the machine learning process and is adjusted throughout the training process. Selected by the neural network designer, hyperparameters are chosen before any training is done.