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120-data-science-interview-questions

Answers to 120 commonly asked data science interview questions.

arm

I'm now interested in Learning to Adapt to Domain Shift

attention

This repository will house a visualization that will attempt to convey instant enlightenment of how Attention works to someone not working in artificial intelligence, with 3Blue1Brown as inspiration

awesome-llm-uncertainty-reliability-robustness

Awesome-LLM-Robustness: a curated list of Uncertainty, Reliability and Robustness in Large Language Models

awesome-masked-autoencoders

A collection of literature after or concurrent with Masked Autoencoder (MAE) (Kaiming He el al.).

byol-pytorch

Usable Implementation of "Bootstrap Your Own Latent" self-supervised learning, from Deepmind, in Pytorch

cracking-the-data-science-interview

Resources for Oreilly's "Cracking the Data Science Interview" video series.

dcpmi

[NAACL24] Official Implementation of Mitigating Hallucination in Abstractive Summarization with Domain-Conditional Mutual Information

domainbed

DomainBed is a suite to test domain generalization algorithms

few-shot-meta-baseline

Meta-Baseline: Exploring Simple Meta-Learning for Few-Shot Learning, in ICCV 2021

fl_partial_personalization

Federated Learning with Partial Model Personalization

fortuna

A Library for Uncertainty Quantification.

from-0-to-research-scientist-resources-guide

Detailed and tailored guide for undergraduate students or anybody want to dig deep into the field of AI with solid foundation.

google-image-scraper

A library to scrap google images

google-research

Google Research

gsds3_algo

GSDS3 알고리즘 스터디

hallucination

howtotrainyourmamlpytorch

The original code for the paper "How to train your MAML" along with a replication of the original "Model Agnostic Meta Learning" (MAML) paper in Pytorch.

incremental_learning.pytorch

A collection of incremental learning paper implementations including PODNet (ECCV20) and Ghost (CVPR-W21).

interview_chat_assistant

kalman-and-bayesian-filters-in-python

Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filters, and more. All exercises include solutions.

leo

Implementation of Meta-Learning with Latent Embedding Optimization

oyster

Implementation of Efficient Off-policy Meta-learning via Probabilistic Context Variables (PEARL)

pytorch

Deep Learning Zero to All - Pytorch

pytorch-classification-uncertainty

This repo contains a PyTorch implementation of the paper: "Evidential Deep Learning to Quantify Classification Uncertainty"

pytorch-leo

Pytorch Implemtation of Meta-Learning with Latent Embedding Optimization

pytorch-tutorial

PyTorch Tutorial for Deep Learning Researchers

pytorch_cifar10

Pretrained TorchVision models on CIFAR10 dataset (with weights)

qqplot.github.io

qqplot's blog

rotta

[CVPR 2023] Robust Test-Time Adaptation in Dynamic Scenarios. https://arxiv.org/abs/2303.13899