Real-time detection and defense against malicious network activity and policy violations (exploits, port-scanners, advertising, telemetry, state surveillance, etc.)
Application of SocketPro onto various databases for continuous inline request/result batching and real-time stream processing with bi-directional asynchronous data transferring
This article is the first in the Data Cleaning with Python and Pandas series that helps working developers get up to speed on data science tools and techniques.
In this second part of the Data Cleaning with Python and Pandas series, now that we have a Jupyter Notebook set up and some basic libraries initialized, we need to load some data. To do this, we’ll load data from a CSV file, as well as from a local SQLite database.
How to use CrashRptEx, to avoid some of the pitfalls of crash reporting in MFC apps or if you want the ability to continue your application after a crash
This tutorial based on a docker image will guide through the development of a search engine service based on Strus and its Python Bindings within the Tornado web-framework.
This article is the first in a series of seven articles in which we will explore the value of ONNX with respect to three popular frameworks and three popular programming languages.
In this series, we’ll use a pretrained model to create an iOS application that will detect multiple persons and objects in a live camera feed rather than in a static picture.
This is a probability based simulation that demonstrates 'Swapping' is considered the best option! We also look at the effect of multi-threading and multi-process in Python.
This is the first in a series of articles on using TensorFlow Lite on Android to bring the power of machine learning and deep neural networks to mobile application
This article series will show you how to build a reasonably accurate traffic speed detector using nothing but Deep Learning, and run it on an edge device like a Raspberry Pi.
This series of articles will guide you through the steps necessary to develop a fully functional time series forecaster and anomaly detector application with AI.
Using SQLite, leverage the create_aggregate(), and SQL's Between Operator to create a Normal Probability Distribution Histogram, or what is more commonly referred to as a Bell Curve.
This access control system application is part of a series of how-to Intel® Internet of Things (IoT) code sample exercises using the Intel® IoT Developer Kit, Intel® Edison board, Intel® IoT Gateway, cloud platforms, APIs, and other technologies.
This article describes different methods to detect outliers in the data and how the Intel® Data Analytics Acceleration Library (Intel® DAAL) helps optimize outlier detection when running it on systems equipped with Intel® Xeon® processors.
Submit an article or tip
