This article provides general guidelines for connecting any Intel Internet of Things (IoT) devices (that is, devices that support Intel microcontrollers like the Intel® Edison board and the Intel® Curie™ Compute Module) and Intel® IoT Gateways to the Microsoft Azure IoT Suite.
This article provides general guidelines for connecting any Intel Internet of Things (IoT) devices (that is, devices that support Intel microcontrollers like the Intel® Edison board and the Intel® Curie™ Compute Module) and Intel® IoT Gateways to the Microsoft Azure IoT Suite.
This article provides general guidelines for connecting any Intel Internet of Things (IoT) devices (that is, devices that support Intel microcontrollers like the Intel® Edison board and the Intel® Curie™ Compute Module) and Intel® IoT Gateways to the Microsoft Azure IoT Suite.
This article takes a look at a variety of tools available from Intel: Intel® Movidius™ Neural Compute Stick, Intel® Python Distribution for Python™, Intel® Math Kernel DNN Library, Intel® Data Analytics Acceleration Library, Intel Distribution of OpenVINO™ Toolkit
This article also has a practical example for the neural network. You read here what exactly happens in the human brain, while you review the artificial neuron network.
In this article, we build a Retrieval-Augmented Generation (RAG) pipeline using KitOps, integrating tools like ChromaDB for embeddings, Llama 3 for language models, and SentenceTransformer for embedding models.
This paper introduces Intel software tools recently made available to accelerate deep learning inference in edge devices (such as smart cameras, robotics, autonomous vehicles, etc.) incorporating Intel® Processor Graphics solutions across the spectrum of Intel SOCs.
In this blog post, we highlight one particular class of low precision networks named binarized neural networks (BNNs), the fundamental concepts underlying this class, and introduce a Neon CPU and GPU implementation.
The COVID-19 pandemic has accelerated machine learning (ML) adoption in many areas, resulting in firms increasing their ML investment and implementation efforts.
In this post, I will show you how you can get started with OCR using the machine learning platform TensorFlow and the Intel® Distribution of OpenVINO™ Toolkit.
In this article we jump right into setting up an Azure Synapse workspace and Azure Synapse Studio to prepare for our machine learning analysis in the next article in the series.
In this article series, we'll demonstrate how to take use a CI/CD pipeline - a tool usually used by developers and DevOps teams - and demonstrate how to use it to create a complete training, test, and deployment pipeline for AI that meets the requirements of level 2 in the Google MLOps Maturity
In this article, we'll demonstrate building an Arm NN-based application for an IoT device that can perform automatic trash sorting through image analysis.
Using Microsoft Azure to add advanced machine learning capabilities with connected IoT devices, which monitor activities of a baby and his or her environment.
R2 Learn, a SaaS-based end-to-end automated machine learning (AutoML) tool, makes it easy for data scientists or data engineers to get from importing a dataset, to training models and getting predictions in just a few steps.
In this article we show, a high-level, it is possible to create sophisticated AI-enabled applications that run upon memory-constrained, ultra-low power endpoint devices.
Based on the topics covered and the examples cited in this paper, hopefully you are convinced that the technology advancements, especially those emulating the human brain and eye, are evolving at a fast pace and may soon replace the human eye.
The Cisco Container Platform automates the repetitive functions and simplifies the complex ones so everyone can go back to enjoying the magic of containers.
This shop-floor equipment activity monitor 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 development platform, cloud platforms, APIs, and other technologies.
by Nish Nishant, Marcelo Ricardo de Oliveira, Monjurul Habib, Kunal Chowdhury «IN», Shai Raiten
In the summer of 2013, CodeProject celebrated hitting 10 million members and invited various CodeProject members to host get-togethers around the world. Here are some of the goings-on at those celebrations.
CodeProject wants to help women get involved and build careers in programming. What can we do? We asked some prominent female programmers, and this is what we learned.
In this series, I want to show you how to create a simple console-based Turing machine in Python. You can check out the full source code on https://github.com/phillikus/turing_machine. In this part, I will explain the fundamental theory behind Turing machines and set up the project based on that.
In this article series, we'll demonstrate how to take use a CI/CD pipeline - a tool usually used by developers and DevOps teams - and demonstrate how to use it to create a complete training, test, and deployment pipeline for AI that meets the requirements of level 2 in the Google MLOps Maturity
The main objective of this project is to develop an Android Application that uses a built-in camera to capture the objects on a road and use a Machine Learning model to get the prediction and location of the respective objects.
The main objective of this project is to develop a Machine Learning model that detects the objects on the road like pedestrians, cars, motorbikes, bicycles, buses, etc.
In this article, we discuss our teachings about data science in a series of steps so that any product manager or business manager interested in exploring this science will be able take their first step toward becoming a data scientist or at least develop a deeper understanding of this science.
To end off this series, we will present the alternative of adapting a pre-trained CNN to the coin recognition problem we have been examining all along.
In this post, I'll walk you through how to get one of the most popular toolkits up and running on Windows, and run through and explain some fun examples.
To make it easier to deploy BigDL, we created a “Deploy to Azure” button on top of the Linux (Ubuntu) edition of the Data Science Virtual Machine (DSVM)
In this article we explore oneDAL. oneDAL includes machine learning algorithms optimized for a variety of architectures, but with the same API, meaning you can use the same application code for whatever type of system your project requires.
Cisco Meraki devices provide retailers with a way to leverage their existing WiFi infrastructure to obtain insights about what happens from the time a person nears their store to the time they leave.
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.
The availability of low cost sensors for environmental monitoring coupled with the capabilities of the Microsoft Cloud provides a set of enormous opportunities in building a solid infrastructure for smart cities.
By using the Firebase ML Kit, developers save small companies and individuals massive amounts of time and money that would otherwise be spent on making their own ML Model.
In this article, we are going to use all that we’ve learned so far with computer vision in TensorFlow.js to try building a version of this app ourselves.
In this article, we are going to use BodyPix, a body part detection and segmentation library, to try and remove the training step of the face touch detection.
An often neglected — but ultimately fundamental — driver of financial markets is liquidity. Combining data science skills and techniques, the Refinitiv Labs Liquidity Discovery project provides in-depth market liquidity insights to enable more informed trading decisions.
In this article we will build a Fluffy Animal Detector, where I will show you a way to leverage a pre-trained Convolutional Neural Network (CNN) model like MobileNet.
FourDotOne digital transformation solutions running on high-performance Intel architecture are enabling industrial and automotive manufacturers to solve complex production line issues and achieve the benefits of Industry 4.0.
Theano is a Python library developed at the LISA lab to define, optimize, and evaluate mathematical expressions, including the ones with multi-dimensional arrays (numpy.ndarray)
In this article, we will see how to work on Clustering model for predicting the Mobile used by model, Sex, before 2010 and After 2010 using the Clustering model with ML.NET.
Today we’ll take a close look at exactly how retailers are using machine learning technologies to maximize their business. To do so, we’ll talk about the application programming interface (API). If you have a technical background, chances are that you might be familiar with and using this important
This home fall tracker 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 development platform, cloud platforms, APIs, and other technologies.
This smart stove top application is part of a series of how-to Intel IoT code sample exercises using the Intel® IoT Developer Kit, Intel® Edison development platform, cloud platforms, APIs, and other technologies.
This automatic watering system application is part of a series of how-to Intel IoT code sample exercises using the Intel® IoT Developer Kit, Intel® Edison development platform, cloud platforms, APIs, and other technologies.
This access control system application is part of a series of how-to Intel® IoT Technology code sample exercises using theIntel® IoT Developer Kit, Intel® Edison board, cloud platforms, APIs, and other technologies.
This smart alarm clock application is part of a series of how-to Intel® IoT Technology code sample exercises using the Intel® IoT Developer Kit, Intel® Edison board, cloud platforms, APIs, and other technologies.
This smart doorbell application is part of a series of how-to Intel® IoT Technology code sample exercises using the Intel® IoT Developer Kit, Intel® Edison board, cloud platforms, APIs, and other technologies.
This line following robot application is part of a series of how-to Intel® IoT Technology code sample exercises using the Intel® IoT Developer Kit, Intel® Edison board, cloud platforms, APIs, and other technologies.
In this tutorial, we will setup a basic machine learning prediction model to run as an Amazon Web Services (AWS) Lambda function in an AWS Greengrass group.
An implementation of unsupervised watershed algorithm to image segmentation with histogram matching technique for reduce over-segmentation by using openCV.
MXNet is an open-source deep learning framework that allows you to define, train, and deploy deep neural networks on a wide array of devices, from cloud infrastructure to mobile devices.
The Developer's Introduction to Intel MKL-DNN tutorial series examines Intel MKL-DNN from a developer’s perspective. Part 1 identifies informative resources and gives detailed instructions on how to install and build the library components.
In Part 2 we will explore how to configure an integrated development environment (IDE) to build the C++ code example, and provide a code walkthrough based on the AlexNet deep learning topology.
Intel® System Studio 2017 Beta has been released. This is the Beta program page which guides you further on Intel® System Studio 2017 Beta new features and enhanced usability experience.
Now that the eight-week Intel® Ultimate Coder Challenge for IoT is complete, teams continue developing and expanding their projects into the commercial sector.
In this article, we will take photos of different hand gestures via webcam and use transfer learning on a pre-trained MobileNet model to build a computer vision AI that can recognize the various gestures in real time.
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 article we explore how data science and business intelligence teams can use Azure Synapse Analytics data to gain new insight into business processes.
This paper shows how the python API of the Intel® Data Analytics Acceleration Library (Intel® DAAL) tool works. First, we explain how to manipulate data using the pyDAAL programming interface and then show how to integrate it with python data manipulation/math APIs.
This guide will help you to write complex neural networks such as Siamese networks in Keras. It also explains the procedure to write your own custom layers in Keras.
In this article, we will see how you can use a different learning algorithm (plus more cores and a GPU) to train much faster on the mountain car environment.
In this final article in this series, we will look at slightly more advanced topics: minimizing the "jitter" of our Breakout-playing agent, as well as performing grid searches for hyperparameters.
This post features a basic introduction to Machine Learning. This post on Machine Learning will not only help you to understand the latest trends in the Internet industry, but increase your understanding of the technology that plays a major role in many services that make our lives easier.
This post features a basic introduction to machine learning (ML). You don’t need any prior knowledge about ML to get the best out of this article. Before getting started, let’s address this question: "Is ML so important that I really need to read this post?"
With Litmus Automation software running on Intel architecture, manufacturers can access and analyze essential data across both legacy and modern infrastructure.
This paper focuses on the implementation of the Indian Liver Patient Dataset classification using the Intel® Distribution for Python* on the Intel® Xeon® Scalable processor.
Best practice for learning Basic of Machine Learning and Gradient Descent based on Linear Regression. This article will explain step by step computational matters.
Intel is uniquely positioned for AI development—the Intel’s AI Ecosystem offers solutions for all aspects of AI by providing a unified front end for a variety of backend technologies, from hardware to edge devices.
In this article we present MADRaS: Multi-Agent DRiving Simulator. It is a multi-agent version of TORCS, a racing simulator popularly used for autonomous driving research by the reinforcement learning and imitation learning communities
Presents a Maximum Entropy modeling library, and discusses its usage, with the aid of two examples: a simple example of predicting outcomes, and an English language tokenizer.
An end to end IoT system utilising Microsoft Azure Cloud Technology and an embedded device, the Texas Instruments CC3200 LaunchPad (Single Chip Wi-Fi MCU).
The Microsoft Data Science Virtual Machine jump starts your analytics project. It enables you to work on tasks in a variety of languages including R, Python, SQL, and C#.
Intel® Software Innovator Joshua Montgomery, Karl Fezer, and Steve Penrod of the Mycroft team let me pick their brains to learn a bit more about Mycroft.
Nervana is currently developing the Nervana Engine, an application specific integrated circuit (ASIC) that is custom-designed and optimized for deep learning.
The diversity of edge devices makes deploying applications difficult. Nutanix Xi IoT provides the developer with an environment frees them to focus on what they do best, creating applications that transform data into decisions.
This article describes a common type of regression analysis called linear regression and how the Intel® Data Analytics Acceleration Library (Intel® DAAL) helps optimize this algorithm when running it on systems equipped with Intel® Xeon® processors.
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.
‘Indix’ is an open source component written in C for Indian font rendering. Indix is a de facto implementation of the rules of Indian languages by CDAC.
This project describes how to recognize certain types of human physical activities using acceleration data generated from the ADXL345 accelerometer connected to the Intel® Edison board.
This project describes how to recognize certain types of human physical activities using acceleration data generated from the ADXL345 accelerometer connected to the Intel® Edison board.
Check out this article of our series on intelligent apps to learn best practices for transitioning your on-premises or IaaS solutions to intelligent apps.
Integrating Cognitive Services SDKs in a .NET Core based application and exploring how real-world scenarios can be tackled using ML services offered by Microsoft
This article describes how to use a neural network to recognize programming languages, as an entry for CodeProject's Machine Learning and Artificial Intelligence Challenge.
The Intel® AI DevJam Demo GUI uses a Windows application to communicate with a facial recognition classifier and an option of two classifiers trained to detect Invasive Ductal Carcinoma (Breast cancer) in histology images.
Intel’s new Deep Learning tools (with the upcoming integration of Nervana’s cloud stack) are designed to hide/reduce the complexity of strong scaling time-to-train and model deployment tradeoffs on resource-constrained edge devices without compromising the performance need.
In this article, we’ll look at some advantages of serverless computing and then dig into a real-world example using the Microsoft Azure Functions service to build and deploy a sample ML inferencing function.
This article describes how AI can be utilized to make a better team strategy from Manager (or Team Coach) in a live soccer game by utilizing SAP HANA and Amazon Sagemaker capabilities together.
In this article, we will talk about criteria you can use to select correct algorithms based on two real-world machine learning problems that were taken from the well-known Kaggle platform used for predictive modeling and from analytics competitions where data miners compete to produce the best model
Intel has invested in optimizing performance of Python itself, with the Intel® Distribution for Python, and has optimized key data science libraries used with scikit-learn, such as XGBoost, NumPy, and SciPy. This article gives more information on installing and using these extensions.
There are many techniques to predict the stock price variations, but in this project, New York Times’ news articles headlines is used to predict the change in stock prices.
In this four-part series, you’ll learn how to create an Intelligent App with Azure Container Apps. In this third part, you’ll explore how to level up your Intelligent Apps by training a custom model using your own dataset.
In this four-part series, you’ll learn how to create an Intelligent App with Azure Container Apps. In this fourth and final part, you’ll explore how to integrate a custom model into your Intelligent Apps, enhancing the application’s features with specialized AI.
This paper introduces the Artificial Intelligence (AI) community to TensorFlow optimizations on Intel® Xeon® and Intel® Xeon Phi™ processor-based platforms.
This article explores how developers can make deep-learning applications faster and more efficient by taking advantage of tools that optimize deep-learning code.
We will train the Apache MXNet Gluon model in Amazon SageMaker to read handwritten numbers of MNIST dataset and then run the prediction for ten random handwritten numbers on IEI Tank AIoT Developer Kit.
In the previous article we trained a simple machine learning model that identifies when and where a human is present in an image. This article will demonstrate how to test this model and re-train it as necessary.
In this article in the series we start to focus on one particular, more complex environment that PyBullet makes available: Humanoid, in which we must train a human-like agent to walk on two legs.
In this blog post we will explain transfer learning and some of its applications, explain how neon can be used for transfer learning, walk through example code that uses neon for transferring a pre-trained model to a new dataset, and discuss the merits of transfer learning with some results
Dive into the world of machine learning and explore how it empowers businesses to extract valuable insights from vast amounts of data. Discover practical techniques and tools for successful implementation.
To help innovators tackle the complexities of machine learning, we are making performance optimizations available to developers through familiar Intel® software tools, specifically through the Intel® Data Analytics Acceleration Library (Intel® DAAL) and enhancements to the Intel® Math Kernel Library
This article provides general guidelines for connecting any Intel® Internet of Things (IoT) devices (that is, devices that support Intel microcontrollers, such as the Intel® Edison board and the Intel® Curie™ Compute Module) and Intel gateways to the Amazon Web Servives (AWS) IoT platform.
In this article we will train the machine to compare strings using logistic regression applied to the result of using Levenshtein algorithms (adapted) and Jaro-Winkler.
In this article we look at how Refinitiv Labs looks at the real-life challenge faced by equity traders with regards to detecting and responding to unexpected asset price changes.
Exploring how to take one of the pre-trained models for TensorFlow and set it up to be executed in Go - Specifically, detecting multiple objects within any image
This article is the third in the Sentiment Analysis series that uses Python and the open-source Natural Language Toolkit. In this article, we'll look at techniques you can use to start doing the actual NLP analysis.
When you connect Internet of Things (IoT) devices (devices that support Intel microcontrollers such as the Intel® Edison board, Intel® Curie™ Compute Module, and Intel® IoT gateways) to the IBM Watson* IoT Platform, you can rapidly build IoT apps that realize your IoT use case.
In our last post on parsing, we detailed how you can pass URL Toolbox a fully qualified domain name or URL and receive a nicely parsed set of fields that includes the query string, top level domain, subdomains. Today, we are going to doing some analytic arithmetic on those fields.
