Metadata-Version: 2.1
Name: mlmodels
Version: 0.34.1
Summary: Generic model API, Model Zoo in Tensorflow, Keras, Pytorch, Hyperparamter search
Home-page: https://github.com/arita37/mlmodels
Author: Kevin Noel
Author-email: brookm291@gmail.com
License: UNKNOWN
Description: 
        
        mlmodels
        ```
        
        
        This repository is the Model ZOO for Pytorch, Tensorflow, Keras, Gluon, LightGBM, Keras, Sklearn models etc with Lightweight Functional interface to wrap access to Recent and State of Art Deep Learning, ML models and Hyper-Parameter Search, cross platforms that follows the logic of sklearn, such as fit, predict, transform, metrics, save, load etc. Now, more than 60 recent models (> 2018) are available in those domains :
        
        Time Series,
        Text classification,
        Vision,
        Image Generation,Text generation,
        Gradient Boosting, Automatic Machine Learning tuning,
        Hyper-parameter search.
        With the goal to transform Script/Research code into re-usable batch/code with minimal code change, we used functional interface instead of pure OOP. This is because functional reduces the amount of code needed which is good to scientific computing. Thus, we can focus on the computing part than design. Also, it is easy to maintain for medium size project.
        
        A collection of Deep Learning and Machine Learning research papers is available in this repository.
        
        alt text alt text alt text
        
        Benefits :
        Having a standard framework for both machine learning models and deep learning models, allows a step towards automatic Machine Learning. The collection of models, model zoo in Pytorch, Tensorflow, Keras allows removing dependency on one specific framework, and enable richer possibilities in model benchmarking and re-usage. Unique and simple interface, zero boilerplate code (!), and recent state of art models/frameworks are the main strength of MLMODELS. Emphasis is on traditional machine learning algorithms but recent state of art Deep Learning algorithms. Processing of high-dimensional data is considered very useful using Deep Learning. For different applications, such as computer vision, natural language processing, object detection, facial recognition and speech recognition, deep learning created significant improvements and outstanding results.
        
        Here you can find usages guide
        
        
        
        
        
        
        
        
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Keywords: Machine Learning Interface library
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: End Users/Desktop
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Environment :: Console
Classifier: Environment :: Web Environment
Classifier: Operating System :: POSIX
Classifier: Operating System :: MacOS :: MacOS X
Requires-Python: >=3.6
Description-Content-Type: text/markdown
