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  2. Google JAX - Wikipedia

    en.wikipedia.org/wiki/Google_JAX

    It is designed to follow the structure and workflow of NumPy as closely as possible and works with various existing frameworks such as TensorFlow and PyTorch. [5] [6] The primary functions of JAX are: [2] grad: automatic differentiation; jit: compilation; vmap: auto-vectorization; pmap: Single program, multiple data (SPMD) programming

  3. Convolution - Wikipedia

    en.wikipedia.org/wiki/Convolution

    The convolution of two finite sequences is defined by extending the sequences to finitely supported functions on the set of integers. When the sequences are the coefficients of two polynomials, then the coefficients of the ordinary product of the two polynomials are the convolution of the original two

  4. Multidimensional discrete convolution - Wikipedia

    en.wikipedia.org/wiki/Multidimensional_discrete...

    This vector length is equivalent to the dimensions of the original matrix output , making converting back to a matrix a direct transformation. Thus, the vector, Z ″ {\displaystyle Z''} , is converted back to matrix form, which produces the output of the two-dimensional discrete convolution.

  5. Tensor (machine learning) - Wikipedia

    en.wikipedia.org/wiki/Tensor_(machine_learning)

    In machine learning, the term tensor informally refers to two different concepts (i) a way of organizing data and (ii) a multilinear (tensor) transformation. Data may be organized in a multidimensional array (M-way array), informally referred to as a "data tensor"; however, in the strict mathematical sense, a tensor is a multilinear mapping over a set of domain vector spaces to a range vector ...

  6. Circular convolution - Wikipedia

    en.wikipedia.org/wiki/Circular_convolution

    Circular convolution, also known as cyclic convolution, is a special case of periodic convolution, which is the convolution of two periodic functions that have the same period. Periodic convolution arises, for example, in the context of the discrete-time Fourier transform (DTFT).

  7. Convolution theorem - Wikipedia

    en.wikipedia.org/wiki/Convolution_theorem

    In mathematics, the convolution theorem states that under suitable conditions the Fourier transform of a convolution of two functions (or signals) is the product of their Fourier transforms. More generally, convolution in one domain (e.g., time domain ) equals point-wise multiplication in the other domain (e.g., frequency domain ).

  8. Wikibooks - Wikipedia

    en.wikipedia.org/wiki/Wikibooks

    Growth of the eight largest Wikibooks sites (by language), July 2003–January 2010. Wikibooks (previously called Wikimedia Free Textbook Project and Wikimedia-Textbooks) is a wiki-based Wikimedia project hosted by the Wikimedia Foundation for the creation of free content digital textbooks and annotated texts that anyone can edit.

  9. Overlap–save method - Wikipedia

    en.wikipedia.org/wiki/Overlap–save_method

    When the DFT and IDFT are implemented by the FFT algorithm, the pseudocode above requires about N (log 2 (N) + 1) complex multiplications for the FFT, product of arrays, and IFFT. [ E ] Each iteration produces N-M+1 output samples, so the number of complex multiplications per output sample is about :