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Modular toolkit for Data Processing
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Modular toolkit for Data Processing

Overview
Synopsis

The Modular toolkit for Data Processing (MDP) is a library of widely used data processing algorithms that can be combined according to a pipeline analogy to build more complex data processing software.

Category

Data Mining Software Free

Features

• Modular toolkit for Data Processing (MDP)
• Implementation of new supervised and unsupervised learning algorithms easy and straightforward
• Valid educational tool

License

Open Source

Price

Free

Pricing

Subscription

Free Trial

Available

Users Size

Small (<50 employees), Medium (50 to 1000 employees), Enterprise (>1001 employees)

Company

Modular toolkit for Data Processing

What is best ?

• Modular toolkit for Data Processing (MDP)
• Implementation of new supervised and unsupervised learning algorithms easy and straightforward
• Valid educational tool

What are the benefits ?

• Access simpler data processing steps
• Build more complex data processing software
• Perform parallel implementation of basic nodes and flows
• Perform learning using batches of data
• Integrate nodes automatically

Rating
Our Rating
User Rating
Ease of use
7.6
8.3
Features & Functionality
7.6
8.2
Advanced Features
7.6
8.2
Integration
7.6
8.3
Customer Support
7.6
Performance
7.6
Training
Implementation
Renew & Recommend
Bottom Line

MDP consists of a collection of supervised and unsupervised learning algorithms, and other data processing units (nodes) that can be combined into data processing sequences (flows) and more complex feed-forward network architectures.

7.6
Our Rating
8.3
User Rating
1 rating
You have rated this

The Modular toolkit for Data Processing (MDP) is a library of widely used data processing algorithms that can be combined according to a pipeline analogy to build more complex data processing software. From the user’s perspective, MDP consists of a collection of supervised and unsupervised learning algorithms, and other data processing units (nodes) that can be combined into data processing sequences (flows) and more complex feed-forward network architectures. Given a set of input data, MDP takes care of successively training or executing all nodes in the network. This allows the user to specify complex algorithms as a series of simpler data processing steps in a natural way. The base of available algorithms is steadily increasing and includes, to name but the most common, Principal Component Analysis (PCA and NIPALS), several Independent Component Analysis algorithms (CuBICA, FastICA, TDSEP, and JADE), Slow Feature Analysis, Gaussian Classifiers, Restricted Boltzmann Machine, and Locally Linear Embedding. Particular care has been taken to make computations efficient in terms of speed and memory. To reduce memory requirements, it is possible to perform learning using batches of data, and to define the internal parameters of the nodes to be single precision, which makes the usage of very large data sets possible. From the developer’s perspective, MDP is a framework that makes the implementation of new supervised and unsupervised learning algorithms easy and straightforward. The basic class, ‘Node’, takes care of tedious tasks like numerical type and dimensionality checking, leaving the developer free to concentrate on the implementation of the learning and execution phases. MDP has been written in the context of theoretical research in neuroscience, but it has been designed to be helpful in any context where trainable data processing algorithms are used. Its simplicity on the user side together with the reusability of the implemented nodes make.

Modular toolkit for Data Processing
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1 Reviews
  • Darcey Talkington
    September 10, 2017 at 11:32 am

    Library of widely used data processing algorithms

    Company size

    Small (<50)

    User Role

    Consultant

    User Industry

    Defense

    Rating
    Ease of use8.3

    Features & Functionality8.2

    Advanced Features8.2

    Integration8.3

    ADDITIONAL INFORMATION
    Modular toolkit for Data Processing (MDP) is a Python data processing framework. MDB is basically a library of widely used data processing algorithms that can be combined according to a pipeline analogy to build more complex data processing software. From the user’s perspective, MDP is a collection of supervised and unsupervised learning algorithms and other data processing units that can be combined into data processing sequences, and more complex feed-forward network architectures. From a developer’s perspective, MDP is a modular framework that makes the implementation of new supervised and unsupervised learning algorithms easy and straightforward. The basic class, ‘Node’, works on the tedious tasks like numerical type and dimensionality checking, so the developer can concentrate on the implementation of the learning and execution phases. MDP’s common interface allows new implemented units to be automatically integrated with the rest of the library, and to be used in a network together with other nodes. A node can have multiple training phases and even an indefinite number of phases. This allows the implementation of algorithms that need to collect some statistics on the whole input before proceeding with the actual training, and others that need to iterate over a training phase until a convergence criterion is satisfied. The ability to train each phase using a large volume of input data is maintained if the data batches are generated with iterators. In addition to that, MDB provides crash recovery options, and in case of failure, the current state of the flow is saved for later inspection.

Ease of use
Features & Functionality
Advanced Features
Integration
Customer Support
Performance
Training
Implementation
Renew & Recommend

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