Amir Khan (Amir22010)

Amir22010

Geek Repo

Location:India

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Amir Khan's repositories

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azure_demo_web_app_deployment

azure_demo_web_app_deployment

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dsgo-dl-workshop-summer-2020

Deep Learning Workshop for Data Science Go Virtual Event Summer 2020

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fastbook

Draft of the fastai book

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GitHubGraduation-2021

Join the GitHub Graduation Yearbook and "walk the stage" on June 5.

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HAQS-2022

Repository containing challenges for the qBraid HAQS 2022 quantum computing hackathon.

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machine-learning-experiments

🤖 Interactive Machine Learning experiments: 🏋️models training + 🎨models demo

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openvino_notebooks

📚 A collection of Python notebooks for learning and experimenting with OpenVINO 👓

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Predicting-cloud-CPU-usage-on-Azure-data

Forecasting future CPU Usage in Azure VM using Deep Learning Models. Compares LSTM , GRU and IndRNN

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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.

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sparseml

Libraries for applying sparsification recipes to neural networks with a few lines of code, enabling faster and smaller models

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YOLOv4-Deepstream

YOLOv4 accelerated wtih TensorRT and multi-stream input using Deepstream

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