Applying CNNs To Images For Computer Vision And Text For NLP

June 11th, 2021

Convolutional Neural Networks (CNNs, ConvNets) have become crucial in artificial intelligence. These networks are reputable for excellently capturing spatial and dependency information in matrices (images, sentences), while effectively reducing their sizes without disca

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Optimization of Edge based Inference Pipeline for Weed Control

May 7th, 2021

This article captures, the optimizations explored for the AI-enabled edge-based weed controller on Nvidia Jetson Xavier AGX. The experiments include TensorRT quantization, calibrations, benchmarking of inference pipeline based on YolactEdge, Bonnetal models, and the enh

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Denoising Images: How to Use Autoencoders to Produce Clearer Images

April 4th, 2021

In this article, we take you into a friendly approach to denoising images and Denoising Autoencoders (DAEs), their architecture, their importance in deep learning models, how to use them with neural networks, and how they improve models’ results. Authors: Melania

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Crop Yield Prediction Using Deep Neural Networks and LSTM

February 28th, 2021

Crop yield prediction using deep neural networks to increase food security in Senegal, Africa. The case study covers leveraging vegetation indices with land cover satellite images from Google Earth Engine and applying deep learning models combined with ground truth data

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Neural Transfer Learning in NLP for Post-Traumatic-Stress-Disorder Assessment

May 11th, 2020

The main goal of the project was to research and prototype technology and techniques suitable to create an intelligent chatbot to mitigate/assess PTSD in low resource settings.   The Problem Statement “The challenge is to build a chatbot where a user can answer s

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