IEEE

Quantum Machine Learning: Recent Advances, Challenges, and Perspectives

Abstract: This study presents a comprehensive survey on Quantum Machine Learning (QML) along with its current status, challenges, and perspectives. QML combines quantum computing and machine learning ...
Military Times

Army stands up AI, machine-learning career field for officers

U.S. Army Gen. James J. Mingus speaks with soldiers assigned to the Artificial Intelligence Integration Center, July 2024. (Spc. Rebeca Soria/Army) The Army on Tuesday announced that it is standing up ...
InfoWorld

AI and machine learning outside of Python

In some ways, Java was the key language for machine learning and AI before Python stole its crown. Important pieces of the data science ecosystem, like Apache Spark, started out in the Java universe.
GitHub

Conical-Flask/Practical-Machine-Learning

knitr::opts_chunk$set(echo = TRUE, warning=FALSE, message=FALSE, fig.width=10, fig.height=5) options(width=120) library(lattice) library(ggplot2) library(plyr ...
Morningstar

Machine Learning Can Forecast Which Stock Factors Will Outperform

A recent study, “Picking Winners in Factorland: A Machine Learning Approach to Predicting Factor Returns,” set out to answer a critical question: Can machine learning techniques improve the prediction ...
National Academies of Sciences%2c Engineering%2c and Medicine

Making Machine Learning Safer in High-Stakes Settings

Machine learning, a key enabler of artificial intelligence, is increasingly used for applications like self-driving cars, medical devices, and advanced robots that work near humans — all contexts ...
GitHub

Dockerize and deploy machine learning model as REST API using Flask

A simple Flask application that can serve predictions machine learning model. Reads a pickled sklearn model into memory when the Flask app is started and returns predictions through the /predict ...
TheServerSide

AWS Machine Learning Associate Practice Exams

Community driven content discussing all aspects of software development from DevOps to design patterns. Over the past few months, I have been helping data engineers, developers, and machine learning ...
Scientific Research Publishing

Doshi-Velez, F. and Kim, B. (2017) Towards a Rigorous Science of Interpretable Machine Learning. arXiv: 1702.08608.

ABSTRACT: Purpose: The purpose of this study is to develop a scalable, risk-aware artificial intelligence (AI) framework capable of detecting financial fraud in high-throughput digital transaction ...
IEEE

A Review on Quantum Machine Learning in Applied Systems and Engineering

Abstract: Quantum Machine Learning (QML) has emerged as a promising frontier within artificial intelligence, offering enhanced data-driven modeling through quantum-augmented representation, ...
TheServerSide

The difference between precision vs. recall in machine learning

Community driven content discussing all aspects of software development from DevOps to design patterns. The key difference between recall and precision is that precision accounts for false positives, ...