Introduction
Gencovery Artificial Intelligence and Analytics (GAIA) brick is a collection of reference ready-to-use and customizable tools for Machine Learning and Deep Learning modeling. It proposes a broad range of AI algorithms, covering among others classification, regression, clustering methods as well as neural networks, to build informative and predictive models from your data.
Acknowledgement
We believe in open innovation and designed our platforms to accelerate the standardisation and integration of open digital resources in biology. GAIA relies on the following open libraries:
- Pandas, the reference Python library for Data analysis and manipulation
- SciPy, the reference Python library for Fundamental algorithms dedicated to scientific computing
- ScikitLearn, the reference Python library for Machine Learning
- TensorFlow, a reference library for machine learning and artificial intelligence, with a particular focus on deep learning methods
- Keras, a reference library that provides a Python interface for the TensorFlow library
References
[1] Pandas, the reference Python library for Data frame manipulation
[2] ScikitLearn, the reference Python library for Machine Learning
[3] Keras, a reference Python library for Deep Learning
Notice
Gencovery Numerical Resources (GNR) refer to the software, librairies and data provided by us through our web services. GNR may be covered by third-party licenses. Gencovery guarantees that GNR are accessible for your commercial and non-commercial use through Gencovery web services. For ad-hoc use of GNR outside Gencovery web services, please check third-party licenses to ensure you are legally authorised. Gencovery does not warrant or assume any legal liability or responsibility for the accuracy, completeness of any information disclosed through Gencovery web services. This is not a legal notice. Please refer to our terms of use for any legal notice about our web services.
