And there are reasons to believe that this decline will become more pronounced in the next few years, particularly in the world of Python. Developed by Google, TensorFlow might have been one of ...

A convenient front-end API lets developers build applications using Python or JavaScript, while the underlying platform executes those applications in high-performance C++. TensorFlow also ...

At the same time, TensorFlow started to play better with standard Python infrastructure such as PyPI and pip, and with the NumPy package widely used by the scientific computing community.

TensorFlow has become the most popular tool ... Its integration with Python IDEs such as PyCharm made it accessible to a large number of developers. Tools such as TensorBoard help engineers ...

Greater developer control: Although TensorFlow uses Python as a front-end API for building applications with the framework, it offers wrappers in several other programming languages including C++ ...

These libraries include Microsoft CNTK, Google TensorFlow, Theano, PyTorch, scikit-learn and Caffe. Most neural network libraries are written in C++ for performance but have a Python API for ...

With TensorFlow, that division is gone. TensorFlow delivers a set of modules (providing for both Python and C/C++ APIs) that enable constructing and executing TensorFlow computations, which are ...

"The Python libraries' power comes from setting certain image-smoothing ops, which easily could be implemented in R's Keras wrapper, and for that matter, a pure-R version of TensorFlow could be ...