TensorFlow eases the process of acquiring data, training models, serving predictions, and refining results, making implementing machine learning less daunting than before.
TensorFlow bundles machine learning and deep learning models and algorithms, uses Python or JavaScript for a convenient API, and executes applications in high-performance C++.
TensorFlow can train and run various types of deep neural networks for tasks like image recognition, machine translation, natural language processing, and simulations. It supports production prediction at scale with the same models used for training.
Developers can create dataflow graphs in TensorFlow, with nodes representing mathematical operations and edges linking multidimensional data arrays. TensorFlow applications can be run on various targets like local machines, cloud clusters, iOS, Android devices, CPUs, or GPUs.
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