Implementation and example training scripts of various flavours of graph neural network in TensorFlow 2.0. Much of it is based on the code in the tf-gnn-samples repo. The code is maintained by the ...

Four years ago, UC Santa Cruz's Jason Eshraghian developed a Python library that combines neuroscience with artificial intelligence to create spiking neural networks, a machine learning method ...

Artificial neural networks are a form of deep learning and one of the pillars of modern-day AI. The best way to really get a grip on how these things work is to build one.

Graph neural networks can be trained and inferred on any type of graph-structured data using TF-GNN. TF-GNN benefits from being a part of the TensorFlow ecosystem. These include support for quick ...

SHAP values (SHapley Additive exPlanations) is an awesome tool to understand your complex Neural network models and other machine learning models such as Decision trees, Random forests. Basically, it ...

Using Python and Scikit-learn, you have learned how to build your own – and have learned the basics of TF-IDF and of non-negative matrix factorization in the process. More resources: ...