Tensorflow Model Remediation- A framework for responsible AI
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.
Tensorflow weight clustering API – An optimization toolkit for heavy-weight models
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.
TensorFlow Probability – A tool to build deep probabilistic models
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- A framework for monotonic models with varying data
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.
How do Kernel Regularizers work with neural networks?
Do you want to know how kernel regularizers adds penalty terms to the network weights and optimize performance. Here is the answer.
How to condense deep learning models for edge devices using quantization?
How to develop deep learning models in edge devices? Here is the answer
How to deploy and monitor your Keras model with Comet?
A detailed implementation of usage of Comet platform for deploying and monitoring a model.
Methods to Serialize and Deserialize Scikit Learn and Tensorflow models for production
This article briefs about the various methods to serialize and deserialize Scikit Learn and Tensorflow models for production
Build your first text-to-image searcher with TensorFlow Lite Model Maker
On-device machine learning uses a simplified version of cloud-based machine learning.
What is special in DeepMind’s Sonnet library for constructing neural nets?
Sonnet creates high-level networks that are easier to train and test with multiple applications.
TensorFlow adds a new library for on-device text-to-image search
It works by using a model to embed the search query into a high-dimensional vector representing the semantic meaning of the query.
TensorFlow v2.9 released: Major highlights
The main highlights of this release are performance enhancement with oneDNN and the release of a new API for model distribution, called DTensor
