repo_description: Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep learning.
repo_topics: data-mining data-science deep-learning deep-reinforcement-learning genetic-algorithm machine-learning machine-learning-from-scratch
code: from setuptools import setup, find_packages
  from codecs import open
  from os import path
  
  __version__ = '0.0.4'
  
  here = path.abspath(path.dirname(__file__))
  
  # get the dependencies and installs
  with open(path.join(here, 'requirements.txt'), encoding='utf-8') as f:
      all_reqs = f.read().split('\n')
  
  install_requires = [x.strip() for x in all_reqs if 'git+' not in x]
  dependency_links = [x.strip().replace('git+', '') for x in all_reqs if x.startswith('git+')]
  
  setup(
      name='mlfromscratch',
      version=__version__,
      description='Python implementations of some of the fundamental Machine Learning models and algorithms from scratch.',
      url='https://github.com/eriklindernoren/ML-From-Scratch',
      download_url='https://github.com/eriklindernoren/ML-From-Scratch/tarball/master',
      license='MIT',
      packages=find_packages(),
      include_package_data=True,
      author='Erik Linder-Noren',
      install_requires=install_requires,
      setup_requires=['numpy>=1.10', 'scipy>=0.17'],
      dependency_links=dependency_links,
      author_email='[REDACTED_EMAIL]'
  )