A new computational model of the brain based closely on its biology and physiology has not only learned a simple visual ...

WIRED spoke with DeepMind’s Pushmeet Kohli about the recent past—and promising future—of the Nobel Prize-winning research ...

While some AI courses focus purely on concepts, many beginner programs will touch on programming. Python is the go-to ...

Massive computing systems are required to train neural networks. The prodigious amount of consumed energy makes the creation of AI applications significant polluters ...

STM-Graph is a Python framework for analyzing spatial-temporal urban data and doing predictions using Graph Neural Networks. It provides a complete end-to-end pipeline from raw event data to trained ...

We will create a Deep Neural Network python from scratch. We are not going to use Tensorflow or any built-in model to write the code, but it's entirely from scratch in python. We will code Deep Neural ...

Abstract: Deep neural networks (DNNs) have achieved satisfactory performance in multiple fields. However, recent studies have shown that DNNs can be easily fooled by adversarial examples. To mitigate ...

"Scale and normalize to make the magnitude of the features similar. Most popular methods for neumerical data are Min Max normalization and Standard normalization (z score). This will improve the model ...

eSpeaks’ Corey Noles talks with Rob Israch, President of Tipalti, about what it means to lead with Global-First Finance and how companies can build scalable, compliant operations in an increasingly ...