whose artificial intelligence (AI) software is purpose-built for engineers, scientists, and researchers and enables them to innovate and make discoveries faster, announced that it had completed contributions to TensorFlow, the world’s most popular open-source framework for deep learning created by Google. “Part of Noble’s mission is building AI that’s accessible to engineers, scientists and researchers, anytime and anywhere, without needing to learn or re-skill into computer science or AI theory,” said Dr. Matthew C. Levy, Founder and CEO of Noble.AI.
He continued, “The reason why we’re making this symbolic contribution open-source is so people have greater access to tools amenable to R&D problems.”
TensorFlow is an end-to-end open source platform for machine learning originally developed by the Google Brain team. Today it is used by more than 60,000 GitHub developers and has achieved more than 140,000 stars and 80,000 forks of the how to become a computer engineer.
Noble.AI’s specific contribution helps to augment the “sparse matrix” capabilities of TensorFlow. Often, matrices represent mathematical operations that need to be performed on input data, such as in calculating the temporal derivative of time-series data.
In many common physics and R&D scenarios these matrices can be sparsely populated such that a tiny fraction, often less than one percent, of all elements in the matrix are non-zero.
He continued, “The reason why we’re making this symbolic contribution open-source is so people have greater access to tools amenable to R&D problems.”
TensorFlow is an end-to-end open source platform for machine learning originally developed by the Google Brain team. Today it is used by more than 60,000 GitHub developers and has achieved more than 140,000 stars and 80,000 forks of the how to become a computer engineer.
Noble.AI’s specific contribution helps to augment the “sparse matrix” capabilities of TensorFlow. Often, matrices represent mathematical operations that need to be performed on input data, such as in calculating the temporal derivative of time-series data.
In many common physics and R&D scenarios these matrices can be sparsely populated such that a tiny fraction, often less than one percent, of all elements in the matrix are non-zero.
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