My deep learning

Ссылки

Data science блоги

Пит Варден

Лукас Биевальд

Джастин Френсис

Дэвид Анджиевский

Yann LeCun

Alison Gopnik

Инструменты разработки

numpy. Документация.

pandas. Документация.

scipy. Документация.

scikit-learn. Scikit-learn is a Python module for machine learning built on top of SciPy and is distributed under the 3-Clause BSD license. Документация.

scikit-learn-extra. Python module for machine learning that extends scikit-learn. It includes algorithms that are useful but do not satisfy the scikit-learn inclusion criteria, for instance due to their novelty or lower citation number. Документация.

metric-learn. Contains efficient Python implementations of several popular supervised and weakly-supervised metric learning algorithms. Документация.

imbalanced-learn. imbalanced-learn is a python package offering a number of re-sampling techniques commonly used in datasets showing strong between-class imbalance. It is compatible with scikit-learn and is part of scikit-learn-contrib projects. Документация.

categorical-encoding. A library of sklearn compatible categorical variable encoders. Документация.

mimic. Calibration method for binary classification model.

stability-selection. Python implementation of the stability selection feature selection algorithm, first proposed by Meinshausen and Buhlmann. Документация.

polylearn. A library for factorization machines and polynomial networks for classification and regression in Python. Документация.

DESlib. A Python library for dynamic classifier and ensemble selection.

tensorflow. TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries, and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications. Документация.

keras. Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano.

pytorch. Tensors and Dynamic neural networks in Python with strong GPU acceleration. Документация.

kubeflow. Machine Learning Toolkit for Kubernetes.

xgboost. Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Документация.

LightGBM. A fast, distributed, high performance gradient boosting (GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks. Документация.

hyperopt. Python library for serial and parallel optimization over awkward search spaces, which may include real-valued, discrete, and conditional dimensions. Документация.

hyperopt-sklearn. Hyperopt-based model selection among machine learning algorithms in scikit-learn

numpy-ml. Growing collection of machine learning models, algorithms, and tools written exclusively in NumPy and the Python standard library. Документация.

ML-From-Scratch. 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.

auto-sklearn. Auto-sklearn frees a machine learning user from algorithm selection and hyperparameter tuning. It leverages recent advantages in Bayesian optimization, meta-learning and ensemble construction. Документация.

TROT. TPOT is a Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming. Документация.

mixtend. A library of extension and helper modules for Python’s data analysis and machine learning libraries. Документация.

kaggle-api. Official Kaggle API.

pythons API. List of Python API Wrappers and Libraries.

Этот проект поддерживается KonstantinKlepikov