Project Overview This project aims to build a high-accuracy digit recognizer that can identify handwritten numbers regardless of variations in writing style. It applies Convolutional Neural Networks ...

Considering that use case specification often plays the most important role in requirements engineering, transitioning from a design on paper to an editable, standard-compliant model is a fitting ...

Abstract: Considering that use case specification often plays the most important role in requirements engineering, transitioning from a design on paper to an editable, standard-compliant model is a ...

In the final article of a four-part series on binary classification using PyTorch, Dr. James McCaffrey of Microsoft Research shows how to evaluate the accuracy of a trained model, save a model to file ...

Because machine learning with deep neural techniques has advanced quickly, our resident data scientist updates binary classification techniques and best practices based on experience over the past two ...

Description of the MNIST Handwritten Digit. The MNIST Handwritten Digit is a dataset for evaluating machine learning and deep learning models on the handwritten digit classification problem, it is a ...