For machine learning models to be reliable, they need to generalize to data beyond the train distribution. In addition, ML models should be robust to privacy attacks like membership inference and ...
This project implements a neural network from scratch to classify handwritten digits using the MNIST dataset. The neural network is built using Python and utilizes libraries such as NumPy and ...
Abstract: The present paper investigates the application of TensorFlow Lite to deploy the Convolutional Neural Network on Rasberry Pi for real-time image classification, considering specifically the ...
What if you could turn Excel into a powerhouse for advanced data analysis and automation in just a few clicks? Imagine effortlessly cleaning messy datasets, running complex calculations, or generating ...
In view of the growing volume of data, there is a notable research focus on hardware that offers high computational performance with low power consumption. Notably, neuromorphic computing, ...
Harvard University announced Thursday it’s releasing a high-quality dataset of nearly 1 million public-domain books that could be used by anyone to train large language models and other AI tools. The ...
Abstract: Deep Neural Networks (DNNs) that aim to maximize accuracy and decrease loss can be trained using optimization algorithms. One of the most significant fields of research is the creation of an ...
This paper presents a new dataset of monetary policy shocks for 21 advanced economies and 8 emerging markets from 2000-2022. We use daily changes in interest rate swap rates around central bank ...
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