Teleconnection-driven vision transformers for improved long-term forecasting

SOTA Semantic Segmentation Models in PyTorch

Semantic segmentation models with 500+ pretrained convolutional and transformer-based backbones.

Implementation of Segformer, Attention + MLP neural network for segmentation, in Pytorch

Awesome resources on normalizing flows.

Unifying Variational Autoencoder (VAE) implementations in Pytorch (NeurIPS 2022)

Materials for the Hugging Face Diffusion Models Course

👋 Xplique is a Neural Networks Explainability Toolbox

Techniques for deep learning with satellite & aerial imagery

🧮 A collection of resources to learn mathematics for machine learning

Image to prompt with BLIP and CLIP

An ultimately comprehensive paper list of Vision Transformer/Attention, including papers, codes, and related websites

Model interpretability and understanding for PyTorch

A toolbox to iNNvestigate neural networks' predictions!

[CVPR 2021] Official PyTorch implementation for Transformer Interpretability Beyond Attention Visualization, a novel method to visualize classifications by Transformer based networks.

[ICCV 2021- Oral] Official PyTorch implementation for Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers, a novel method to visualize any Transformer-…

CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image

A collection of resources and papers on Diffusion Models

Ready-to-use code and tutorial notebooks to boost your way into few-shot learning for image classification.

Always know what to expect from your data.

A repository listing out the potential sources which will help you in preparing for a Data Science/Machine Learning interview. New resources added frequently.

Machine Learning and Computer Vision Engineer - Technical Interview Questions

A helpful 5-page machine learning cheatsheet to assist with exam reviews, interview prep, and anything in-between.

Resources I used for ML Engineer, Applied Scientist and Quant Researcher interviews.

Some code I wrote in Python (with tests!) to practice for Big N Interviews

Machine Learning Interviews from FAANG, Snapchat, LinkedIn. I have offers from Snapchat, Coupang, Stitchfix etc. Blog: mlengineer.io.

FAIR's research platform for object detection research, implementing popular algorithms like Mask R-CNN and RetinaNet.

Tigramite is a python package for causal inference with a focus on time series data. The Tigramite documentation is at

This is full source code to the article Terraform recipe - How to create AWS ElasticSearch cluster