8 Alternatives To PyBrain You Should Know To Build Neural Networks

Python-Based Reinforcement Learning, Artificial Intelligence, and Neural Network (PyBrain) offer flexible, easy-to-use and powerful algorithms for Machine Learning tasks with a variety of predefined environments to test and compare your algorithms. In this article, we list down 8 alternatives of PyBrain one must try in 2019. (The list is in alphabetical order)

1| Azure Machine Learning

Azure Mchine Learning is a browser-based workbench for the data science workflow, which includes authoring, evaluating and publishing predictive models. Azure Machine Learning Studio is a collaborative, drag-and-drop tool for building, testing, and deploying predictive analytics solutions on your data. Also, the Azure Machine Learning service provides SDKs and services to quickly prep data, train, and deploy machine learning models. Click here to know more.

2| DatumBox

The Datumbox API is a powerful open-source machine learning framework written in Java. It offers a large number of off-the-shelf Classifiers and Natural Language Processing services which can be used in a broad spectrum of applications including sentiment analysis, topic classification, language detection, subjectivity analysis, spam detection, reading assessment, keyword and text extraction, etc. Click here to know more.

3| Google Cloud Machine Learning

Google Cloud Machine Learning Engine is a managed service which lets developers and data scientists build and run superior machine learning models in production. It supports popular machine learning frameworks and provides built-in tools to understand the machine learning models. Cloud ML Engine offers training and prediction services, which can be used together or individually. Click here to know more.

4| MLlib

MLlib is Apache Spar’s scalable machine learning library. It contains algorithms such as classification, regression, clustering, topic modelling, and other such. The machine learning workflow utilities include feature transformations: standardization, normalization, hashing, machine learning pipeline construction, model evaluation, and hyper-parameter tuning, etc. Click here to know more.

5| OpenCV

OpenCV is the open source library for computer vision, image processing, and machine learning which also features GPU acceleration for real-time operation. It is released under a BSD license and has C++, C, Python and Java interfaces. This library is written in optimised C/C++ and is designed for computational efficiency and with a strong focus on real-time applications. Click here to know more.

6| Sci-Kit Learn

Scikit-learn is a Python module for machine learning built on top of SciPy, NumPy, and Matplotlib which provides a number of supervised as well as unsupervised learning algorithms. The library is mainly focused on data modelling. It can be used for classification, regression, clustering, dimensionality reduction, model selection, preprocessing, etc. Click here to know more.

7| Smile

Statistical Machine Intelligence and Learning Engine (Smile) is a fast and comprehensive machine learning engine. It can write applications quickly in Java, Scala, or any JVM languages and provides hundreds of advanced algorithms with clean interface. Smile covers every aspect of machine learning which includes classification, regression, clustering, association rule mining, feature selection, manifold learning, multidimensional scaling, genetic algorithm, missing value imputation, efficient nearest neighbor search, etc. Click here to know more.

8| Weka

Waikato Environment for Knowledge Analysis (Weka) is a suite of machine learning software written in Java. It is a collection of machine learning algorithms for data mining tasks which contain tools for data preparation, classification, regression, clustering, association rules mining, and visualisation. This system was designed to bring a range of machine learning techniques or schemes under a common interface so that they may be easily applied to this data in a consistent method. It uses a common file format to store its data sets and thus presents the user with a consistent view of the data regardless of what machine learning scheme may be used. Click here to know more.