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We describe our progression from a traditional ASR system to one with an Encoder-Attention-Decoder architecture, and discuss

Learn how we created a recurrent neural network that detects anomalous events in Windows*. We also explore how different neur

Machine learning models can seem complex when trying to understand why a model predicts what it does. This article describes

After participating in a contest to train a generative adversarial network (GAN) on the Stanford dog dataset to generate new

Image segmentation is often done using Mask-RCNN, U-Net, and other techniques. This article describes how image segmentation

Google* recently released their Research Football Environment. This article breaks down its complexities and summarizes how w

Vehicle Detection involves finding whether there is vehicle present or not secondly which type of vehicle is present and how

The motive of this article is to demonstrate the idea of distributed computing in the context of training large scale deep le

Recurrent Neural Networks have been the recent state-of-the-art methods for various problems whose available data is sequenti

When Artificial Intelligence involves some type of sensitive data, the problem is how to maintain the data privacy and securi

Cash Recognition for the Visually Impaired: A project to make monetary transactions easier for visually impaired Nepalese with machine learning and AI

Cash Recognition for the Visually Impaired: A project to make monetary transactions easier for visually impaired Nepalese with machine learning and AI