Tips and tricks from those focused on tuning and testing for performance
URL: https://www.kaggle.com/code/zengamer/induction-motor-faults-detection
This project focuses on detecting faults in induction motors using machine learning and deep learning models. It preprocesses sensor data, trains models like SVM, kNN, and DNN, evaluates performance, and provides predictions with insights into motor health.
Posted:
URL: https://www.kaggle.com/code/zengamer/solar-power-generation-prediction
Predicting Solar Power Generation: A Machine Learning Approach using Historical Weather Data and Time-Series Analysis for Accurate Renewable Energy Forecasting.
Posted:
URL: https://github.com/herumi/xbyak
A C++ JIT assembler for x86 (IA32), x64 (AMD64, x86-64)
Posted:
URL: https://github.com/Ahamedthaiyub/Workfoce-360-based-on-intelex-
WORKFORCE360 is a machine learning model associated with the Intel One API toolkit, designed to predict employee attrition. Its purpose is to help companies retain valuable employees and reduce costs associated with turnover. Our three-tier solution includes resume parsing and predictive analytics.
Posted:
URL: https://github.com/deltatrelabs/deltatre-aiday-2022-demo
In this repository you can find slides and demos for the Optimizing Deep Learning models: theory, tools & best-practices session, presented (in Italian) at AI Day 2022 Conference on November 18th, 2022.
Posted:
URL: https://github.com/intel-sandbox/ICE_dpcpp_sample/tree/master
Non-maximum supression is a algorithm for many detection/tracking use case. But for large data, NMS is time-consuming. So making this algorthm paralleled is necessary.
Posted:
In this project, we aim to find if FPGAs can be considered as a viable option as a hardware accelerator, and if so, how is their performance compared to existing processors like GPGPUs in various types of HPC workloads. We have an opportunity to take benefit of the recent developments in High-Level
Posted:
URL: https://github.com/norhidayahm/integral_dpcpp
This project is modified from the integral project from the course "Fundamentals of Parallelism on Intel Architecture" by Dr. Andrey Vladimirov in Coursera. The codes are converted to C++ and DPC++.
Posted:
