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OpenNN

A C++ library for implementing neural networks for advanced analytics tasks in various fields.

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Feedforward Neural Networks
Supports feedforward neural networks, a fundamental architecture for various machine learning tasks
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Backpropagation
Implements backpropagation, a training algorithm that adjusts weights to minimize error
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Gradient Descent
Employs gradient descent for optimization, iteratively updating weights to find the minimum of the loss function
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Activation Functions
Provides common activation functions like sigmoid, hyperbolic tangent, and rectified linear unit (ReLU)
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Loss Functions
Provides various loss functions (mean squared error, cross-entropy) for different tasks
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Optimization Algorithms
Users can choose from gradient descent, conjugate gradient, quasi-Newton, and more.
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Regularization
Prevents overfitting by penalizing overly complex models. Includes L1 and L2 regularization to prevent overfitting.
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Dropout
Supports dropout layers, randomly deactivating neurons during training which improves generalization
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Hyperparameter Optimization
Assists in tuning hyperparameters for optimal performance
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Data Preprocessing
Prepare data in a format suitable for neural network training. Handles data scaling, normalization, and missing value imputation
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Model Serialization
Allows saving and loading trained models for deployment
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GPU Acceleration
Users can leverage GPUs for faster training
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Parallelization
Utilizes multi-threading for efficient computations
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Time Series Forecasting
Supports time series prediction using recurrent neural networks. Allows building models specifically for making predictions on sequential data.
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Custom Layers
Users can create custom neural network layers. Provides flexibility to implement specialized functionalities within the network architecture.
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Autoencoders
Enables building autoencoders for dimensionality reduction
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Visualization Tools
Provides visualization of neural network architectures and training progress

Platform

Desktop
Language
C++

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System Requirements

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Ratings

4.30
5

PAT RESEARCH
7.6
10
based on professional's opinion
PAT RESEARCH
9.6
10
based on 5 reviews

Developer

Written in

C++

Initial Release

22 November 2018

Repository

License

Categories