Utilize well-known frameworks that are Intel-optimized, like as TensorFlow and PyTorch, to make the most of the Intel architecture's full potential and achieve great performance for both training and inference.
Artificial Intelligence
Get the most out of your training, scoring, algorithms and frameworks on Intel® architecture for Deep Learning and Artificial Intelligence.
It's a system that will help identity a particular email is ham or spam, an email spam detection system using Naive Bayes and by training the model.
In this article, we are going to discuss about SYCL, regarding “What it’s about”, “How to implement” and other interesting topics, please give it a read and explore!
Connect with Linux/macOS SSH Client
I Have Developed An Algorithm Which Detects Credit Card Frauds In 8 Seconds And Alerts Blank Servers To Block The Transactions I Have Used R-Language To Develop The Algorithm And R-Studio As IDE Also I Have Used Data Set Which Was Available On Kaggle I Have Provided My Git-Hub Profile Link Were You Can Check Out My Project
You can have custom soap boxes made to match your product. You can print custom soap boxes with your logos and marketing messages. Soap boxes can be customized from various materials, including paperboard, cardboard, and plastic.
A Comparitive Study of Employee Attrition Analysis Using Machine Learning and Deep Learning Techniques - Presented this paper at the 6th International conference on Inventive Communication and Computational Technologies.
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Open Volume Kernel library is a high-performance library for ray sampling of scenes. Open VKL covers Volume Sampling.
Introduction
Arhat is a cross-platform deep learning framework that converts neural network descriptions into lean standalone executable code. Arhat has been designed for deployment of deep learning inference workflows in the cloud and on the edge.
Arhat is a vendor-agnostic tool supporting multiple target platforms. Unlike the conventional deep learning frameworks, Arhat translates neural network descriptions directly into platform-specific executable code. This code inter...
Analysis of large volumes of legal texts represents a common task in the practice of law firms. Handling this task may require enormous investment of time and effort, therefore significant attention is being paid to automate it using the intelligent software solutions. In this post we describe the early results of our project aiming at prototyping a transformer-based NLP solution for analysis of large volumes of legal texts.
We are developing academic research in order to predict the Brazilian stock market using sentiment analysis applied to both social media and news.
Our research is currently under development and our results so far are presented in the following papers.
de Oliveira Carosia, A. E., Coelho, G. P., & da Silva, A. E. A. (2021). Investment strategies applied to the Brazilian stock market: A methodology based on sentiment analysis with deep learning. Expert Systems with Application...
Arhat is a cross-platform framework designed for efficient deployment of deep learning inference workflows in the cloud and at the edge. Arhat translates neural network descriptions directly into lean standalone executable code. Arhat provides interoperability with Intel deep learning software including oneDNN library and OpenVINO toolkit. In this article we discuss design and architecture of Arhat and demonstrate its use for deployment of object detection models for embedded applications.
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