Showing 37 open source projects for "format"

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  • 1
    captcha_break

    captcha_break

    Identification codes

    ...It supports image verification codes and voice verification codes. We use its function of generating image verification codes. First, we set our verification code format to numbers and capital letters, and generate a string of verification codes. It is well known that tensorflow occupies all video memory by default, which is not conducive to us conducting multiple experiments at the same time, so we can use the following code when tensorflow uses the video memory it needs instead of directly occupying all video memory.
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  • 2
    Easy-TensorFlow

    Easy-TensorFlow

    Simple and comprehensive tutorials in TensorFlow

    The goal of this repository is to provide comprehensive tutorials for TensorFlow while maintaining the simplicity of the code. Each tutorial includes a detailed explanation (written in .ipynb) format, as well as the source code (in .py format). There is a necessity to address the motivations for this project. TensorFlow is one of the deep learning frameworks available with the largest community. This repository is dedicated to suggesting a simple path to learn TensorFlow. In addition to the aforementioned points, the large community of TensorFlow enriches the developers with the answer to almost all the questions one may encounter. ...
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  • 3
    Deepvoice3_pytorch

    Deepvoice3_pytorch

    PyTorch implementation of convolutional neural networks

    An open source implementation of Deep Voice 3: Scaling Text-to-Speech with Convolutional Sequence Learning.
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  • 4
    The GAN Zoo

    The GAN Zoo

    A list of all named GANs

    The GAN Zoo is an open-source repository that compiles a comprehensive list of Generative Adversarial Network models published in research literature. The project began as a community effort to track the rapidly growing number of GAN architectures appearing in machine learning papers. Because new GAN models are frequently introduced in research publications, the repository serves as a convenient catalog that organizes them in one location. The list includes references to many GAN variants...
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  • 5
    3D ResNets for Action Recognition

    3D ResNets for Action Recognition

    3D ResNets for Action Recognition (CVPR 2018)

    We uploaded the pretrained models described in this paper including ResNet-50 pretrained on the combined dataset with Kinetics-700 and Moments in Time. We significantly updated our scripts. If you want to use older versions to reproduce our CVPR2018 paper, you should use the scripts in the CVPR2018 branch.
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  • 6
    auto_ml

    auto_ml

    Automated machine learning for analytics & production

    auto_ml is designed for production. Here's an example that includes serializing and loading the trained model, then getting predictions on single dictionaries, roughly the process you'd likely follow to deploy the trained model. Before you go any further, try running the code. Load up some data (either a DataFrame, or a list of dictionaries, where each dictionary is a row of data). Make a column_descriptions dictionary that tells us which attribute name in each row represents the value we’re...
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  • 7
    Convolution arithmetic

    Convolution arithmetic

    A technical report on convolution arithmetic in deep learning

    ...The code and the images of this tutorial are free to use as regulated by the licence and subject to proper attribution. The animations will be output to the gif directory. Individual animation steps will be output in PDF format to the pdf directory and in PNG format to the png directory. We introduce a guide to help deep learning practitioners understand and manipulate convolutional neural network architectures. The guide clarifies the relationship between various properties (input shape, kernel shape, zero padding, strides and output shape) of convolutional, pooling and transposed convolutional layers, as well as the relationship between convolutional and transposed convolutional layers. ...
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  • 8

    mullpy

    Multilabel-learning library built on python

    Mullpy is a machine-learning library that mainly aim to solve multi-label problems. It is classifier independent, has many ensemble capabilities (diversity methods like bagging, random subspaces, etc.) and automated results presentation (Excel, images as ROC or class-separated info, etc.). It is fully configurable. At the moment supports Neural Networks and classifiers defined in files. It is working on python3.3.
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  • 9
    pyIRDG

    pyIRDG

    IMDb Relational Dataset Generator

    pyIRDG is a program written in Python to generate relational datasets in Prolog format. It uses data from the Internet Movie Database in combination with IMDbPY as backend. A graphical user interface written in pyQt allows the user to link multiple entities together as model for the generation process. The big four entities are Title, Person, Company and Character. Many attributes can be chosen for adding to the output .pl file.
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  • 10
    A Python function library to extract EEG feature from EEG time series in standard Python and numpy data structure. Features include classical spectral analysis, entropies, fractal dimensions, DFA, inter-channel synchrony and order, etc.
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  • 11
    pySPACE

    pySPACE

    Signal Processing and Classification Environment in Python using YAML

    ...Due to its modular architecture, the software can easily be extended with new processing nodes and more general operations. Large scale empirical investigations can be configured using simple text- configuration files in the YAML format, executed on different (distributed) computing modalities, and evaluated using an interactive graphical user interface. For obtaining a zip file of the current state use: https://github.com/pyspace/pyspace/archive/master.zip
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  • 12

    text_summurization_abstractive_methods

    Multiple implementations for abstractive text summurization

    This repo is built to collect multiple implementations for abstractive approaches to address text summarization it is built to simply run on google colab , in one notebook so you would only need an internet connection to run these examples without the need to have a powerful machine , so all the code examples would be in a jupyter format , and you don't have to download data to your device as we connect these jupyter notebooks to google drive
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