The TensorFlow project announced the release of version 2.3.0, featuring new mechanisms for reducing input pipeline bottlenecks, Keras layers for pre-processing, and memory profiling.

Overview Python continues to dominate as the most popular coding language for AI development.Emerging AI development languages like Julia and R are gaining trac ...

When deploying large-scale deep learning applications, C++ may be a better choice than Python to meet application demands or to optimize model performance. Therefore, I specifically document my recent ...

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The Python APIs have been redesigned and are now closer in structure to NumPY, which improves stability and simplifies the process of incorporating TensorFlow with existing Python applications ...

Google could put that know-how to work making TensorFlow easier to use, or lowering the hardware demands so that AI runs more smoothly on phones and computers.