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.
Tensorflow lattice modelling aims to obtain a more reliable and generic model which perfroms phenomanally when taken up for testing for similar kind of data it is trained upon.
Do you want to know how kernel regularizers adds penalty terms to the network weights and optimize performance. Here is the answer.
How to develop deep learning models in edge devices? Here is the answer
A detailed implementation of usage of Comet platform for deploying and monitoring a model.
This article briefs about the various methods to serialize and deserialize Scikit Learn and Tensorflow models for production
On-device machine learning uses a simplified version of cloud-based machine learning.
Sonnet creates high-level networks that are easier to train and test with multiple applications.
It works by using a model to embed the search query into a high-dimensional vector representing the semantic meaning of the query.
The main highlights of this release are performance enhancement with oneDNN and the release of a new API for model distribution, called DTensor