of machine learning and deep learning algorithms prepared on the basis of the current state of knowledge and the individual Customer needs. with the best analytical practises. with the help of GPU graphic accelerators and high-tech optimization tools. both during data preprocessing and while building predictive algorithms. of machine learning and deep learning algorithms prepared on the basis of the current state of knowledge and the individual Customer needs. with the best analytical practises. with the help of GPU graphic accelerators and high-tech optimization tools. both during data preprocessing and while building predictive algorithms. Advantages of Aigorithmics' algorithms
Proprietary constructions
In-depth data preprocessing
Optimizing the speed of data processing
Using classical methods as a support for deep learning methods
Advantages of Aigorithmics' algorithms
Proprietary constructions
In-depth data preprocessing
Optimizing the speed of data processing
Using classical methods as a support for deep learning methods