A Machine Learning library based on Theano.

Pylearn2 alternatives and similar packages

Based on the "Machine Learning" category

• tensorflow

  10.0 10.0 L1 Pylearn2 VS tensorflow

  Computation using data flow graphs for scalable machine learning

• TFLearn

  9.5 5.7 L3 Pylearn2 VS TFLearn

  Deep learning library featuring a higher-level API for TensorFlow.

• NuPIC

  9.2 3.1 L3 Pylearn2 VS NuPIC

  Numenta Platform for Intelligent Computing.

• xgboost

  9.1 9.3 L1 Pylearn2 VS xgboost

  A scalable, portable, and distributed gradient boosting library.

• srez

  8.6 0.0 L5 Pylearn2 VS srez

  Image super-resolution through deep learning

• PaddlePaddle

  8.3 9.9 L1 Pylearn2 VS PaddlePaddle

  PArallel Distributed Deep LEarning

• PyBrain

  8.2 0.0 L4 Pylearn2 VS PyBrain

  Another Python Machine Learning Library.

• Surprise

  8.2 4.1 L4 Pylearn2 VS Surprise

  A Python recommender system library aimed towards researchers, teachers and students.

• skflow

  8.0 0.0 L4 Pylearn2 VS skflow

  A simplified interface for TensorFlow (mimicking scikit-learn).

• LightFM

  7.6 4.1 L4 Pylearn2 VS LightFM

  A Python implementation of a number of popular recommendation algorithms.

• MLflow

  7.6 9.4 Pylearn2 VS MLflow

  Open source platform for the machine learning lifecycle

• gensim

  7.4 8.3 L3 Pylearn2 VS gensim

  Topic Modelling for Humans.

• Clairvoyant

  7.2 0.0 L3 Pylearn2 VS Clairvoyant

  Software designed to identify and monitor social/historical cues for short term stock movement

• Prophet

  6.8 8.2 Pylearn2 VS Prophet

  Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.

• python-recsys

  6.7 0.0 L4 Pylearn2 VS python-recsys

  A Python library for implementing a Recommender System.

• pydeep

  6.4 0.0 L3 Pylearn2 VS pydeep

  -Deep learning in python

• Crab

  6.1 0.0 L2 Pylearn2 VS Crab

  A flexible, fast recommender engine.

• hebel

  5.6 0.0 L2 Pylearn2 VS hebel

  GPU-Accelerated Deep Learning Library in Python.

• Keras

  5.5 9.1 L2 Pylearn2 VS Keras

  Minimalist deep learning library for Python, running on top of Theano and Tensorflow.

• awesome-embedding-models

  5.5 4.8 Pylearn2 VS awesome-embedding-models

  A curated list of awesome embedding models tutorials, projects and communities.

• scikit-learn

  5.3 9.8 L3 Pylearn2 VS scikit-learn

  A Python module for machine learning built on top of SciPy.

• Sacred

  5.1 8.8 Pylearn2 VS Sacred

  Sacred is a tool to help you configure, organize, log and reproduce experiments developed at IDSIA.

• Feature Forge

  4.3 0.0 L4 Pylearn2 VS Feature Forge

  A set of tools for creating and testing machine learning features, with a scikit-learn compatible API

• rwa

  4.1 0.0 L5 Pylearn2 VS rwa

  Machine Learning on Sequential Data Using a Recurrent Weighted Average

• SciKit-Learn Laboratory

  3.8 8.8 Pylearn2 VS SciKit-Learn Laboratory

  SciKit-Learn Laboratory (SKLL) makes it easy to run machine learning experiments.

• pdpipe

  3.1 4.9 Pylearn2 VS pdpipe

  Easy pipelines for pandas DataFrames.

• brew

  3.1 0.0 L4 Pylearn2 VS brew

  Multiple Classifier Systems and Ensemble Learning Library in Python.

• CNTK

  2.8 6.9 L1 Pylearn2 VS CNTK

  Microsoft Cognitive Toolkit

• MLP Classifier

  2.8 0.0 L4 Pylearn2 VS MLP Classifier

  A handwritten multilayer perceptron classifier using numpy.

• vowpal_porpoise

  2.4 0.0 L3 Pylearn2 VS vowpal_porpoise

  A lightweight Python wrapper for Vowpal Wabbit.

• seqeval

  2.2 6.8 Pylearn2 VS seqeval

  Well-tested evaluation framework for named-entity recognition.

• tfgraphviz

  1.4 0.0 L5 Pylearn2 VS tfgraphviz

  A simple graphviz wrapper to visualize a graph like TensorFlow & TensorBoard

• nptyping

  1.1 5.0 Pylearn2 VS nptyping

  Type hints for numpy

• neptune-contrib

  0.7 8.1 Pylearn2 VS neptune-contrib

  Tools, helpers and everything else that helps you work with Neptune.

• Simple GAN

  0.6 5.0 Pylearn2 VS Simple GAN

  Attempt at implementation of a simple GAN using Keras

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Popular Comparisons

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