Python still remains a dominant force in AI development, with more than 275,495 companies using it.
Google was leading with TensorFlow, but Meta’s PyTorch won hearts with the ease of use, and things have stayed that way
Major improvements have been made to Keras as well.
From data visualisation to deep learning libraries, Python is the most valuable language for machine learning.
The library aims to address key engineering challenges in scientific computing through better management and processing of large datasets.
TensorFlow’s team is now introducing Decision Forests 1.0 with the latest release
Preprocessing prevents from overfitting
XLA is a compiler used to accelerate training time of Tensorflow models and reduce memory consumption.
This article mainly focuses on the Tensor2Tensor library and to understand the dynamic abilities to handle and process complex models.
Tensorflow model remediation is a framework used to obtain fairness free and bias free models. It aims to produce robust models that is not affected by sensitive attributes of data.
The weight clustering API is one of the use cases of the Tensorflow model optimization library and it aims to optimize the models developed so that they can be easily integrated into edge devices.
This article has explained the importance of Tensorflow probability and its working principle. It has also explained the working principle of Tensorflow probability and its importance in the context of TensorFlow modelling.