iNeural VS Pylearn2

Compare iNeural vs Pylearn2 and see what are their differences.

iNeural

A library for creating Artificial Neural Networks, for use in Machine Learning and Deep Learning algorithms. (by fkkarakurt)

Pylearn2

Warning: This project does not have any current developer. See bellow. (by lisa-lab)
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iNeural Pylearn2
4 1
5 2,752
- 0.0%
0.0 0.0
over 1 year ago over 2 years ago
C++ Python
GNU Affero General Public License v3.0 BSD 1-Clause 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.

iNeural

Posts with mentions or reviews of iNeural. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-12-24.

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.

What are some alternatives?

When comparing iNeural and Pylearn2 you can also consider the following projects:

fann - Official github repository for Fast Artificial Neural Network Library (FANN)

Keras - Deep Learning for humans

GPBoost - Combining tree-boosting with Gaussian process and mixed effects models

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

command - Command, ::process::Command like syscalls in C++.

tensorflow - An Open Source Machine Learning Framework for Everyone

Seayon - Open source Neural Network library in C++

PyBrain

Nerve - This is a basic implementation of a neural network for use in C and C++ programs. It is intended for use in applications that just happen to need a simple neural network and do not want to use needlessly complex neural network libraries.

scikit-learn - scikit-learn: machine learning in Python

armnn - Arm NN ML Software. The code here is a read-only mirror of https://review.mlplatform.org/admin/repos/ml/armnn

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