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A prompt for a text-to-text language model can be a query, a command, or a longer statement including context, instructions, and conversation history. Prompt engineering may involve phrasing a query, specifying a style, choice of words and grammar, [3] providing relevant context, or describing a character for the AI to mimic. [1]
Ng started the Stanford Engineering Everywhere (SEE) program, which in 2008 published a number of Stanford courses online for free. Ng taught one of these courses, "Machine Learning", which includes his video lectures, along with the student materials used in the Stanford CS229 class. It offered a similar experience to MIT OpenCourseWare, except
A free course can be "upgraded" to the paid version of a course, which includes instructor's feedback and grades for the submitted assignments, and (if the student gets a passing grade) a certificate of completion. [57] [60] Other Coursera courses, projects, specializations, etc. cannot be audited—they are only available in paid versions ...
In 2011, MIT OpenCourseWare introduced the first of fifteen OCW Scholar courses, which are designed specifically for the needs of independent learners. While still publications of course materials like the rest of the site content, these courses are more in-depth and the materials are presented in logical sequences that facilitate self-study.
Course developers could charge licensing fees for educational institutions that use its materials. Introductory or "gateway" courses and some remedial courses may earn the most fees. Free introductory courses may attract new students to follow-on fee-charging classes. Blended courses supplement MOOC material with face-to-face instruction.
It took a long driving layup through plenty of traffic at the buzzer, but Jordan Gainey saved the day for Tennessee on Saturday night. Gainey lifted top-ranked Tennessee to a stunning 66-64 win ...
The reward model is first trained in a supervised manner to predict if a response to a given prompt is good (high reward) or bad (low reward) based on ranking data collected from human annotators. This model then serves as a reward function to improve an agent's policy through an optimization algorithm like proximal policy optimization. [3] [4] [5]
Sports thrill, amaze and sometimes infuriate us. They fill our time and give us immediate connections to people. They teach us lessons in perseverance and cooperation. Mostly, though, sports make ...
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