Pylearn2 VS scikit-learn

Compare Pylearn2 vs scikit-learn and see what are their differences.

Pylearn2

Warning: This project does not have any current developer. See bellow. (by lisa-lab)

scikit-learn

scikit-learn: machine learning in Python (by scikit-learn)
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Pylearn2 scikit-learn
1 30
2,716 48,629
0.1% 1.1%
1.2 9.9
5 months ago 3 days ago
Python Python
BSD 3-clause "New" or "Revised" License BSD 3-clause "New" or "Revised" License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

Pylearn2

Posts with mentions or reviews of Pylearn2. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-12-08.
  • iNeural : Update (8.12.21)
    3 projects | dev.to | 8 Dec 2021
    It is developed by taking inspiration from libraries such as iNeural, FANN, pylearn2, EBLearn, Torch7. Written mostly in C++, iNeural also leverages the power of Python. The biggest reason for its development is that it needs very few dependencies. For this reason, it is expected to be suitable for working in systems with limited system requirements.

scikit-learn

Posts with mentions or reviews of scikit-learn. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-12-17.

What are some alternatives?

When comparing Pylearn2 and scikit-learn you can also consider the following projects:

Keras - Deep Learning for humans

Surprise - A Python scikit for building and analyzing recommender systems

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

tensorflow - An Open Source Machine Learning Framework for Everyone

gensim - Topic Modelling for Humans

PyBrain

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

seqeval - A Python framework for sequence labeling evaluation(named-entity recognition, pos tagging, etc...)

MLflow - Open source platform for the machine learning lifecycle

H2O - H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.

xgboost - Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow

CNTK - Microsoft Cognitive Toolkit (CNTK), an open source deep-learning toolkit