Our deep learning technology auto-validates large, complex, sparse and noisy datasets, enabling your organisation to easily find outliers and predict missing data. The platform enables guided experiments that can be applied to real-world experiments or expensive computer simulations.
Traditional deep learning requires complete, good quality data, making cutting edge experimental data not suitable for standard deep learning approaches. Our deep learning tool enables knowledge to be shared across an organization, the ability to run virtual experiments and guidance for future experiments.
Intellegens is now applying this generic approach to a wide range of problems with sparse datasets. The Alchemite™ algorithm can be applied to any sparse or noisy data problem including industrial formulations, predictive maintenance, patient analytics, and drug discovery.
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