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Understanding Floating-Point Number Representations in Computing

This guide explores the representation of floating-point numbers in computing, including normalized and denormalized values, as well as special cases like Infinity and NaN (Not a Number). It explains how numbers close to zero are represented, the significance of the exponent and mantissa, and the implications of these representations for numerical computations. Gaining an understanding of floating-point formats is crucial for developers and engineers dealing with precision and performance in applications that involve mathematical calculations.

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Understanding Floating-Point Number Representations in Computing

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  1. 1. Normalized s  0 &  255 f 2. Denormalized s 0 0 0 0 0 0 0 0 f 3a. Infinity s 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3b. NaN s 1 1 1 1 1 1 1 1  0

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