- Guías de estudio, Notas de estudios & Resúmenes

These notes focus on understanding and working with numbers in scientific notation. They cover how to convert standard numbers to scientific notation and vice versa, as well as how to perform operations like multiplying, dividing, adding, and subtracting numbers in scientific notation. Key rules for adjusting exponents and keeping numbers in proper scientific form are explained. Example problems show step-by-step solutions to help reinforce the concepts.

These notes explain the steps and strategies for solving different types of inequalities. They cover solving one-step, two-step, and multi-step inequalities, including how to handle inequalities with variables on both sides. Important rules, like flipping the inequality sign when multiplying or dividing by a negative number, are emphasized. The notes also include how to write solutions using inequality notation, graph solutions on a number line, and check answers. Example problems are included t...

These notes cover the fundamental steps and techniques for solving different types of equations. They include methods for solving one-step, two-step, and multi-step equations, as well as tips for dealing with variables on both sides. Special cases like no solution and infinite solutions are also explained. Key strategies such as isolating the variable, using inverse operations, and checking solutions are highlighted. Example problems are provided for each type of equation to show step-by-step so...

The vast majority of the popular English named entity recognition (NER) datasets contain American or British English data, despite the existence of many global varieties of English. As such, it is unclear whether they generalize for analyzing use of English globally.

The vast majority of the popular English named entity recognition (NER) datasets contain American or British English data, despite the existence of many global varieties of English. As such, it is unclear whether they generalize for analyzing use of English globally.

The vast majority of the popular English named entity recognition (NER) datasets contain American or British English data, despite the existence of many global varieties of English. As such, it is unclear whether they generalize for analyzing use of English globally.

The vast majority of the popular English named entity recognition (NER) datasets contain American or British English data, despite the existence of many global varieties of English. As such, it is unclear whether they generalize for analyzing use of English globally.

The vast majority of the popular English named entity recognition (NER) datasets contain American or British English data, despite the existence of many global varieties of English. As such, it is unclear whether they generalize for analyzing use of English globally.

The vast majority of the popular English named entity recognition (NER) datasets contain American or British English data, despite the existence of many global varieties of English. As such, it is unclear whether they generalize for analyzing use of English globally.

The vast majority of the popular English named entity recognition (NER) datasets contain American or British English data, despite the existence of many global varieties of English. As such, it is unclear whether they generalize for analyzing use of English globally.