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Seamless interleave of machine learning objects with your business analytics implemented in object-oriented languages. It's like Tensorflow for C++... but 6-9 times faster on a CPU!
MatLogica delivers a significant bump in performance and decreases the hardware/cloud bill. The actual development stays within the traditional object-oriented code.
For problems requiring up to 1000 inputs, AADC by far outperforms tools like Python, Tensorflow, and others. Python-based tools are starting to catch up for very large problems (such as computer vision and linguistics) where we are limited by the memory bandwidth. See the results of the ADBench benchmark below:
You can easily calibrate complex multi-asset models relying on Monte-Carlo and avoid using inflexible and difficult to derive analytical approximations.
Use real-time data to recalibrate your models and get an accurate and up-to-date information.
Efficiently differentiate the custom function definitions as if they were there from the start.