ishandandekar
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Linux, Jenkins, AWS, SRE, Prometheus, Docker, Python, Ansible, Git, Kubernetes, Terraform, OpenStack, SQL, NoSQL, Azure, GCP, DNS, Elastic, Network, Virtualization. DevOps Interview Questions
💻 A fully functional local AWS cloud stack. Develop and test your cloud & Serverless apps offline
Typer, build great CLIs. Easy to code. Based on Python type hints.
Machine Learning Engineering Open Book
Implementation of paper - YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information
A resource for learning about Machine learning & Deep Learning
AWS zero to hero repo for devops engineers to learn AWS in 30 Days. This repo includes projects, presentations, interview questions and real time examples.
This repository is a curated collection of links to various courses and resources about Artificial Intelligence (AI)
😎 A curated list of awesome MLOps tools
A light-weight, flexible, and expressive statistical data testing library
This repository consists of all the material required for cracking the coding rounds and technical interviews during placements.
🪄 Turns your machine learning code into microservices with web API, interactive GUI, and more.
Python binding for curl-impersonate fork via cffi. A http client that can impersonate browser tls/ja3/http2 fingerprints.
🤖Self-Modifying Framework from the Future 🔮 World's First AMS
Basic Utilities for PyTorch Natural Language Processing (NLP)
An unofficial styleguide and best practices summary for PyTorch
Industry-strength Computer Vision workflows with Keras
A simple emoji classifier for humans. 🖖
My implementation of useful data structures, algorithms, as well as my solutions to programming puzzles.
Collection of Algorithms implemented in Python
📦 PyTorch based visualization package for generating layer-wise explanations for CNNs.
Possibly the fastest DataFrame-agnostic quality check library in town.
Highly commented implementations of Transformers in PyTorch
A project template where PyTorch Lightning, Pydantic, and more! being used for training MNIST as an example.
Video Game Recommendation System based on Content Based Filtering using Clustering