It can also train and run deep neural networks for word embedding, sequence to sequence models for machine translation, partial differential equation-based simulations, and natural language processing. TensorFlow competes with other frameworks like Apache MXNet and PyTorch.
The models can be trained using high-level APIs that make for easy debugging and immediate model iteration. You can efficiently train and deploy these models on-premise, on the cloud, the browser, or on a device, irrespective of the language you use. TensorFlow can help you build ML models with intuitive APIs like Keras. It offers a bundle of workflows with high-level APIs that work wonders for beginners as well as experts. The simple and flexible architecture enables you to build state-of-the-art models and publications faster.
The platform allows you to take new ideas from concept to code. From fast numerical computing to creating deep learning models, this foundation library simplifies the process built on top of TensorFlow. With this, there is less human thought overhead with Python and a simpler interface for experimentation can take place.
It aids performance by programming the vital parts in C++ even though it uses Python. Although originally developed to resolve large numerical computations, TensorFlow is primarily used for deep learning applications.
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