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.
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.
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.
This article is the first in the Sentiment Analysis series that uses Python and the open-source Natural Language Toolkit. This article is a primer on some key NLP concepts and getting started with the Natural Language Toolkit (NLTK) Python library.
This article is the second in the Sentiment Analysis series that uses Python and the open-source Natural Language Toolkit. In this article, we'll look at datasets provided by NLTK, as well as an example of capturing your own textual corpus for analysis.
The Python web app this article creates will be broadly useful to enterprise developers, who can use it as a starting point and swap out the Markdown conversion code for code that converts to whatever output format the business requires.
This article is the sixth in the Sentiment Analysis series that uses Python and the open-source Natural Language Toolkit. In this article let’s look at what a process of annotating our own dataset would entail.
In this article, we focus on developing a computer vision framework that can run the various Machine Learning and neural network models – like SSD MobileNet – on live and recorded vehicle traffic videos.
In this two-part series, you’ll learn how to turn a Raspberry Pi into a flexible and powerful Internet-of-Things device with AWS Greengrass v2, ultimately creating an off-grid remote-tasked data and image collection device.
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In this article, we’ll create Azure Functions in Java or Python, show that deploying them to our Arc-hosted Kubernetes cluster is just as easy as deploying functions directly to Azure, and demonstrate that our Azure Functions are, in fact, running in the cloud, but not on Azure itself.
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.
This article provides a better understanding of the benefits of OpenVINO integration with Tensorflow: how it works, deployments environments, and how OpenVINO integration with TensorFlow differs from using native OpenVINO API
This Plant Lighting 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 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.