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  2. Softmax function - Wikipedia

    en.wikipedia.org/wiki/Softmax_function

    The Softmax function is a smooth approximation to the arg max function: the function whose value is the index of a vector's largest element. The name "softmax" may be misleading. Softmax is not a smooth maximum (that is, a smooth approximation to the maximum function).

  3. Row- and column-major order - Wikipedia

    en.wikipedia.org/wiki/Row-_and_column-major_order

    To use column-major order in a row-major environment, or vice versa, for whatever reason, one workaround is to assign non-conventional roles to the indexes (using the first index for the column and the second index for the row), and another is to bypass language syntax by explicitly computing positions in a one-dimensional array.

  4. Lookup table - Wikipedia

    en.wikipedia.org/wiki/Lookup_table

    For a trivial hash function lookup, the unsigned raw data value is used directly as an index to a one-dimensional table to extract a result. For small ranges, this can be amongst the fastest lookup, even exceeding binary search speed with zero branches and executing in constant time. [6]

  5. Mean squared error - Wikipedia

    en.wikipedia.org/wiki/Mean_squared_error

    The MSE either assesses the quality of a predictor (i.e., a function mapping arbitrary inputs to a sample of values of some random variable), or of an estimator (i.e., a mathematical function mapping a sample of data to an estimate of a parameter of the population from which the data is sampled).

  6. Aggregate function - Wikipedia

    en.wikipedia.org/wiki/Aggregate_function

    Range; Sum; Others include: Nanmean (mean ignoring NaN values, also known as "nil" or "null") Stddev; Formally, an aggregate function takes as input a set, a multiset (bag), or a list from some input domain I and outputs an element of an output domain O. [1] The input and output domains may be the same, such as for SUM, or may be different ...

  7. Array slicing - Wikipedia

    en.wikipedia.org/wiki/Array_slicing

    If we have a vector containing elements (2, 5, 7, 3, 8, 6, 4, 1), and want to create an array slice from the 3rd to the 6th elements, we get (7, 3, 8, 6). In programming languages that use a 0-based indexing scheme, the slice would be from index 2 to 5. Reducing the range of any index to a single value effectively removes the need for that index.

  8. Sigmoid function - Wikipedia

    en.wikipedia.org/wiki/Sigmoid_function

    This function is unusual because it actually attains the limiting values of -1 and 1 within a finite range, meaning that its value is constant at -1 for all and at 1 for all . Nonetheless, it is smooth (infinitely differentiable, C ∞ {\displaystyle C^{\infty }} ) everywhere , including at x = ± 1 {\displaystyle x=\pm 1} .

  9. Cosine similarity - Wikipedia

    en.wikipedia.org/wiki/Cosine_similarity

    In some contexts, the component values of the vectors cannot be negative, in which case the cosine similarity is bounded in [,]. For example, in information retrieval and text mining , each word is assigned a different coordinate and a document is represented by the vector of the numbers of occurrences of each word in the document.