Sayak Paul's Projects
Starter repository for Manning LP: Use Machine Learning to Detect Phishing Websites
Implementation of MAXIM in TensorFlow.
A very popular technique for Hierarchical Clustering
https://www.kaggle.com/c/microsoft-malware-prediction
TensorFlow Lite models for MIRNet for low-light image enhancement.
This repository contains notebooks showing how to perform mixed precision training in tf.keras 2.0
Contains materials from the facilitation sessions conducted for the ML Bootcamp India (2022) organized by Google DevRel team.
Contains notebooks prepared for ML Bootcamp organized by Google Developers Launchpad.
This project shows how to serve an ONNX-optimized image classification model as a web service with FastAPI, Docker, and Kubernetes.
Implements MLP-Mixer (https://arxiv.org/abs/2105.01601) with the CIFAR-10 dataset.
Contains data, notebooks and other files of FloydHub's mini-series on machine learning project structuring, model debugging, various tips and tricks and more
This repository hosts code for converting the original MLP Mixer models (JAX) to TensorFlow.
Shows how to bulk generate model cards for models on 🤗 Hub.
Shows an implementation of model soups (https://arxiv.org/abs/2203.05482) for text classification models.
This repository shows how to implement a basic model for multimodal entailment.
Neural Arithmetic Logic Units by Trask et al.
Holds code for near-duplicate image parser using optimized image classifiers.
Notebooks using the Hugging Face libraries 🤗
Contains notebooks for the PAR tutorial at CVPR 2021.
Contains exercises to strengthen my parallel programming skills
Shows how to do parameter ensembling using differential evolution.
Deep learning experiments to design a model to predict Parkinson's diseases with the images of Spiral/Wave test
Contains my experiments for ZS's hiring hackathon (II).
Minimal implementation of PAWS (https://arxiv.org/abs/2104.13963) in TensorFlow.
Experiments to detect phishing websites using neural networks
Personal site of Sayak Paul. Deployed here 👉
Probing the representations of Vision Transformers.
Python wrapper to Philipp Krähenbühl's dense (fully connected) CRFs with gaussian edge potentials.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent