Abhishek Rana
| Experience | Education | Projects |
Projects
Full Stack
Tech stack for microservices based application (Github)
- Modern tech stack for microservices with support for local development/deployment on kubernetes.
| Service | Technologies |
|---|---|
| Frontend | Vue3 - Typescript, Pinia |
| Backend | Python - FastAPI, Pydantic, SQLAlchemy (PostgreSQL), Beanie (MongoDB) |
| Golang - Chi | |
| Infrastructure | PostgreSQL, MongoDB, Redis |
| Deployment | Kubernetes (k3d), Helm |
Skills: Python, Golang, Vue.js, Javascript, HTML, CSS, FastAPI, Pydantic, SQLAlchemy, Docker, PostgreSQL, MongoDB, Redis, Helm, Kubernetes.
Backend
Image to text using AWS Serverless (Lambda) and SAM (Github)
- Image to text service using AWS Lambda function and AWS SAM(Serverless Application Model).
Skills: Python, AWS Lambda, AWS Serverless, SAM.
Frontend
Project Manager (Github)
- Application to manage projects within a team.
Skills: Vue, Vuetify, Typescript, Javascript, HTML, CSS, Vite
Tic Tac Toe (Github)
- Tic Tac Toe game
Skills: React, Javascript, HTML, CSS, Vite
Deep Learning & Computer Vision
Apparel detection using deep learning (Github)
- Detect apparels in images using deep learning classification and regression model trained on clothing images.
Skills: Python, Keras, Augmented Reality, Computer Vision, Deep Learning.
Guitar fretboard detection and notes projection (Github)
- Train and predict guitar fretboard using deep learning model (UNet).
- Project a mesh on the detected fretboard (using Homography).
- Display the notes to play on the projected mesh.
Skills: Python, Keras, Augmented Reality, Computer Vision, Deep Learning.
ISIC 2018 - Skin disease classification challenge (Github)
- Train and predict skin cancer using Cascaded DenseNets for Multi-Class Skin Lesion Classification and ISIC 2018 - Skin Lesion Classification for Melanoma Detection dataset.
Skills: Python, Tensorflow, Computer Vision, Deep Learning.
Image generation using Generative Adversarial Networks (GAN) (Github)
- Generate synthetic/fake images using DCGAN (Deep Convolutional Generative Adversarial Networks) and small NORB dataset.
Skills: Python, Pytorch, Computer Vision, Deep Learning.