Amir Khan's repositories
azure_demo_web_app_deployment
azure_demo_web_app_deployment
dsgo-dl-workshop-summer-2020
Deep Learning Workshop for Data Science Go Virtual Event Summer 2020
GitHubGraduation-2021
Join the GitHub Graduation Yearbook and "walk the stage" on June 5.
HAQS-2022
Repository containing challenges for the qBraid HAQS 2022 quantum computing hackathon.
machine-learning-experiments
🤖 Interactive Machine Learning experiments: 🏋️models training + 🎨models demo
openvino_notebooks
📚 A collection of Python notebooks for learning and experimenting with OpenVINO 👓
Predicting-cloud-CPU-usage-on-Azure-data
Forecasting future CPU Usage in Azure VM using Deep Learning Models. Compares LSTM , GRU and IndRNN
predicting-cloud-CPU-utilization-on-Azure-dataset-using-deeplearning
Many companies are utilizing the cloud for their day to day activities. Many big cloud service providers like AWS, Microsoft Azure have been success-fully serving its increasing customer base. A brief understanding of the char-acteristics of production virtual machine (VM) workloads of large cloud pro-viders can inform the providers resource management systems, e.g. VM scheduler, power manager, server health manager. In our project we will be analysing Microsoft Azure’s VM CPU utilization dataset released in October 2017. We predict the VM workload from the CPU usage pattern like mini-mum, maximum and average from the Azure dataset. Different techniques among Deep learning are used for the prediction by considering the history of the workload. By considering real VM traces, we can show that the predic-tion-informed schedules increase utilization and stop physical resource ex-haustion. We can arrive at a conclusion that cloud service providers can use their workloads’ characteristics and machine learning techniques to enhance resource management greatly.
YOLOv4-Deepstream
YOLOv4 accelerated wtih TensorRT and multi-stream input using Deepstream