Hebel

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Hebel is a GPU-accelerated deep learning library in Python, designed for building neural networks. It supports various activation functions and training methods like momentum, Nesterov momentum, dropout, and early stopping.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is hebel?

Hebel is an open-source Python library for deep learning, leveraging GPU acceleration through CUDA via PyCUDA. It provides functionalities for implementing neural network models, specifically feed-forward networks for classification and regression. The library offers a range of activation functions and advanced training methods, including momentum, Nesterov momentum, dropout, and early stopping, along with L1 and L2 weight decay for regularization. While no longer actively developed, it served as a foundational tool for researchers and developers working with deep learning models on Linux, Windows, and potentially Mac OS X. Hebel is available on PyPi for easy installation.

Best used for

Ideal for developers and data scientists who need to build and train deep learning models with GPU acceleration, implement various neural network architectures, and experiment with different optimization techniques. Especially valuable for those working with Python and CUDA for high-performance computing in machine learning.

Common actions

accelerate deep learning

build neural networks

implement machine learning models

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Capabilities

Key features

GPU-accelerated deep learning
Neural network implementation
Multiple activation functions
Momentum/Nesterov momentum
Dropout regularization
L1/L2 weight decay

Target Audience

developerdata scientist

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What kind of neural network models does Hebel support?

Hebel primarily implements feed-forward neural networks for both classification and regression tasks. While other models like Autoencoders and Convolutional Neural Networks were planned, the library is no longer actively developed, so these may not be available.

What training methods and regularization techniques are available in Hebel?

Hebel supports stochastic gradient descent (SGD) with regular and Nesterov momentum for optimization. For regularization, it includes dropout, L1 weight decay, and L2 weight decay to help prevent overfitting in neural network models.

Is Hebel actively maintained or recommended for new projects?

Hebel is no longer actively developed by its creator, Hannes Bretschneider. While it remains a functional library, the creator now recommends using other deep learning frameworks like Chainer for new projects due to its discontinued maintenance.

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