Showing 4 open source projects for "deep learning with python"

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    MATLAB Deep Learning Model Hub

    MATLAB Deep Learning Model Hub

    Discover pretrained models for deep learning in MATLAB

    Discover pre-trained models for deep learning in MATLAB. Pretrained image classification networks have already learned to extract powerful and informative features from natural images. Use them as a starting point to learn a new task using transfer learning. Inputs are RGB images, the output is the predicted label and score.
    Downloads: 6 This Week
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  • 2
    Armadillo

    Armadillo

    fast C++ library for linear algebra & scientific computing

    * Fast C++ library for linear algebra (matrix maths) and scientific computing * Easy to use functions and syntax, deliberately similar to Matlab / Octave * Uses template meta-programming techniques to increase efficiency * Provides user-friendly wrappers for OpenBLAS, Intel MKL, LAPACK, ATLAS, ARPACK, SuperLU and FFTW libraries * Useful for machine learning, pattern recognition, signal processing, bioinformatics, statistics, finance, etc. * Downloads:...
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    Downloads: 2,694 This Week
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  • 3
    Ymap - Yeast Mapping Analysis Pipeline

    Ymap - Yeast Mapping Analysis Pipeline

    Pipeline for large-scale genome changes analysis of genome datasets.

    The active use repository has migrated over to: https://github.com/darrenabbey/ymap The repository here was errantly created with some large binary files included. Attempts to extract the files from the history here have failed. A copy of the history was successfully scrubbed and then hosted at github. -------- Eukaryotic pathogens have complicated and dynamic genomes. To facilitate analysis of copy number variations (CNV), single nucleotide polymorphisms (SNPs), and loss of...
    Downloads: 0 This Week
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  • 4

    LWPR

    Locally Weighted Projection Regression (LWPR)

    Locally Weighted Projection Regression (LWPR) is a fully incremental, online algorithm for non-linear function approximation in high dimensional spaces, capable of handling redundant and irrelevant input dimensions. At its core, it uses locally linear models, spanned by a small number of univariate regressions in selected directions in input space. A locally weighted variant of Partial Least Squares (PLS) is employed for doing the dimensionality reduction. Please cite: [1] Sethu...
    Downloads: 0 This Week
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