Once the computer finds the best model from training on the initial data, we can use that model to predict values for new data. If the data tends to change over time, we may have to retrain the ...

TensorFlow's mobile and IoT toolkit, TensorFlow Lite, supports post-training quantization of models, which can reduce model size up to 4x and increase inference speed up to 1.5x.

It's possible to create neural networks from raw code. But there are many code libraries you can use to speed up the process. These libraries include Microsoft CNTK, Google TensorFlow, Theano, PyTorch ...

The tool converts a trained model's weights from floating-point representation to 8-bit signed integers. ... InfoQ Homepage News Google Releases Post-Training Integer Quantization for TensorFlow Lite.

But when TensorFlow was released to the public in November, it didn’t support distributed training. And within less than 24 hours, people pointed it out as a GitHub issue .

Strong Compute wants to speed up your ML model training. Frederic Lardinois. 8:50 AM PST · March 9, 2022. ... “PyTorch is beautiful and so is TensorFlow. These toolkits are amazing, ...