gabrieleangeletti/Deep-Learning-TensorFlow
A collection of deep learning algorithm implementations (CNNs, autoencoders, RBMs) using TensorFlow with sklearn-like interface.
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This repository is a collection of deep learning algorithms implemented using TensorFlow, intended as a command-line utility for quickly training and evaluating popular models as benchmarks. It provides implementations of convolutional networks, restricted Boltzmann machines, deep belief networks, stacked denoising autoencoders, and multilayer perceptrons. The package offers a scikit-learn-like interface with fit/predict methods and includes a pip package named yadlt for integration into Python code.
Frequently asked
- What is gabrieleangeletti/Deep-Learning-TensorFlow?
- A collection of deep learning algorithm implementations (CNNs, autoencoders, RBMs) using TensorFlow with sklearn-like interface.
- Is Deep-Learning-TensorFlow open source?
- Yes — gabrieleangeletti/Deep-Learning-TensorFlow is open source, released under the MIT license.
- What language is Deep-Learning-TensorFlow written in?
- gabrieleangeletti/Deep-Learning-TensorFlow is primarily written in Python.
- How popular is Deep-Learning-TensorFlow?
- gabrieleangeletti/Deep-Learning-TensorFlow has 958 stars on GitHub.
- Where can I find Deep-Learning-TensorFlow?
- gabrieleangeletti/Deep-Learning-TensorFlow is on GitHub at https://github.com/gabrieleangeletti/Deep-Learning-TensorFlow.