Expand machine learning & AI capabilities using this new released TensorFlow 2.0. It is an open-source machine learning library, using algorithms to build advanced models. It makes the process seamless and simplifies the numeric computation with the help of data flow graphs. 

Last week has been full of updates. We have seen Keras’ new version coming and moving ahead with the new release candidate 2.0.0-rc2 with unprecedented changes. 

According to StackOverflow’s 2019 Developer Survey, it is many more times popular than Torch/PyTorch, and ranked as one of the most loved developer tools.

 

 

TensorFlow 2.0 is integrated with Python deep learning library Keras, with default execution and Phytonic function execution. This update will help Python Developers to make application development easy using TensorFlow. 

According to the TensorFlow team, TensorFlow 2.0 will focus on simplicity and ease of use. 

CEO of Hugging Face,  Clement Delangue said It’s the number one feature that companies asked for since the launch of the library last year. 

TensorFlow engineering director Rajat Monga said The TensorFlow framework has been downloaded more than 40 million times since it was released by the Google Brain team in 2015. 

 

TensorFlow 2.0 presents a comprehensive ecosystem of tools for enterprises, developers, and researchers who want to push the state-of-the-art machine learning and build scalable ML-powered applications. 

Highlights of TensorFlow 2.0 Update

  • Developers can develop in the “TensorFlow 2.0 way” by using tf.keras and eager execution, pre-packaged models, and deployment libraries. 
  • TensorFlow 2.0 offers 8 architectures with over 30 pretrained models, in more than 100 languages. 
  • This robust solution is powerful and concise as Keras. 
  • TensorFlow 2.0 helps you share pretrained models, reducing compute costs and carbon footprint. 
  • It allows you to load a model and pre-process a dataset in less than 10 lines of code
  • You can move a single model between TF2.0/PyTorch frameworks at will with ease.
  • It offers deep interoperability between TensorFlow 2.0 and PyTorch models. 
  • Train a state-of-the-art language model in a single line with the tf.keras fit function. 
  • Using this you can seamlessly pick the right framework for training, evaluation, production
  • Share pretrained models, reducing compute costs and carbon footprint

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