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Hexadecimal is a positional numeral system that uses sixteen symbols to represent numbers. Learn how to write, distinguish and convert hexadecimal values in mathematics and computing, and see examples of hexadecimal notation in different contexts.
Learn about the 16-bit binary floating-point computer number format that occupies two bytes in memory and is used for image processing and neural networks. See the IEEE 754 standard, the history, the exponent encoding, and the examples of half-precision values.
Six hexadecimal digits of precision is roughly equivalent to six decimal digits (i.e. (6 − 1) log 10 (16) ≈ 6.02). A conversion of single precision hexadecimal float to decimal string would require at least 9 significant digits (i.e. 6 log 10 (16) + 1 ≈ 8.22) in order to convert back to the same hexadecimal float value.
A comprehensive table of various ways to convert complex objects to sequences of bits, with details on their creators, specifications, standards, and features. See how JSON compares to other formats such as Avro, BSON, CBOR, CSV, XML, and more.
"A base is a natural number B whose powers (B multiplied by itself some number of times) are specially designated within a numerical system." [1]: 38 The term is not equivalent to radix, as it applies to all numerical notation systems (not just positional ones with a radix) and most systems of spoken numbers. [1]
Learn about different methods of encoding binary data in plain text, such as Base58, Base64, Base32, and more. Compare their efficiency, programming language implementations, and uses in various systems and protocols.
Learn how to write large or small numbers in scientific notation, a base ten notation that simplifies arithmetic operations. Find out the history, variations, and applications of scientific notation in science, mathematics, and engineering.
bfloat16 is a 16-bit computer number format that represents a wide dynamic range of numeric values by using a floating radix point. It is a shortened version of the 32-bit IEEE 754 single-precision floating-point format, and is used to accelerate machine learning and near-sensor computing.