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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 Enterprise (>1001 employees)
• 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
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Ease of use
7.6
8.3
Features & Functionality
7.6
8.2
Advanced Features
7.6
8.2
Integration
7.6
8.3
Performance
7.6
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Customer Support
7.6
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Implementation
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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
Editor Rating
8.3
Aggregated User Rating
2 ratings
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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.
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.
Library of widely used data processing algorithms
Small (<50)
Consultant
Defense
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.