Compute

/ˈprɪn.sə.pəl kəˈpoʊ.nənt əˈnæl.ə.sɪs/

noun … “a way to rotate data until its most important structure faces you.”

/məˈʃiːn ˌlɜːrnɪŋ/

noun … “teaching machines to improve by experience instead of explicit instruction.”

Machine Learning is a branch of computer science focused on building systems that can learn patterns from data and improve their performance over time without being explicitly programmed for every rule or scenario. Rather than encoding fixed logic, a machine learning system adjusts internal parameters based on observed examples, feedback, or outcomes, allowing it to generalize beyond the data it has already seen.

/əˈweɪt/

verb … “to pause execution until an asynchronous operation produces a result.”

/ˈprɒmɪs/

noun … “a construct that represents the eventual completion or failure of an asynchronous operation.”

/ˈeɪ.sɪŋk/

adjective … “executing operations independently of the main program flow, allowing non-blocking behavior.”

Async, short for asynchronous, refers to a programming paradigm where tasks are executed independently of the main execution thread, enabling programs to handle operations like I/O, network requests, or timers without pausing overall execution. This approach allows applications to remain responsive, efficiently manage resources, and perform multiple operations concurrently, even if some tasks take longer to complete.

/ˌoʊ.ɛnˈɛks ˈrʌnˌtaɪm/

noun … “a high-performance engine for executing machine learning models in the ONNX format.”

/ˌoʊ.ɛnˈɛks/

noun … “an open format for representing and interoperating machine learning models.”

/ˌviː.eɪˈiː/

noun … “a probabilistic neural network that learns latent representations for generative modeling.”

/ˌdʒiːˌpiːˈtiː/

noun … “a generative language model that predicts and produces coherent text.”

/ˈɔːtoʊˌɛnˌkoʊdər/

noun … “a neural network that learns efficient data representations by reconstruction.”