nmakes

releasing-research-code

Tips for releasing research code in Machine Learning (with official NeurIPS 2020 recommendations)

ss-ood

Self-Supervised Learning for OOD Detection (NeurIPS 2019)

gat

Repository for the Gray-box Adversarial Training project presented at ECCV 2018.

pytorch_tutorial

PyTorch Tutorial (1.7)

aima-python

Python implementation of algorithms from Russell And Norvig's "Artificial Intelligence - A Modern Approach"

ICLR2020-OpenReviewData

Script that crawls meta data from ICLR OpenReview webpage. Tutorials on installing and using Selenium and ChromeDriver on Ubuntu.

shubodh.github.io

MP3vec

MP3vec : A Transferable Feature Representation Method for Protein Sequences

Conference-Acceptance-Rate

Acceptance rates for the major AI conferences

salad

A toolbox for domain adaptation and semi-supervised learning. Contributions welcome.

handout

Turn Python scripts into handouts with Markdown and figures

OPDA_BP

awesome_OpenSetRecognition_list

A curated list of papers & resources linked to open set recognition, out-of-distribution, open set domain adaptation and open world recognition

out-of-distribution-detection

The Ultimate Reference for Out of Distribution Detection with Deep Neural Networks

lsc-cnn

nn_robust_attacks

Robust evasion attacks against neural network to find adversarial examples

babyai

BabyAI platform. A testbed for training agents to understand and execute language commands.

awesome-transfer-learning

Best transfer learning and domain adaptation resources (papers, tutorials, datasets, etc.)

awesome-knowledge-distillation

Awesome Knowledge Distillation

awesome-domain-adaptation

A collection of AWESOME things about domian adaptation

papers

Summaries of machine learning papers

Keras-GAN

Keras implementations of Generative Adversarial Networks.

cs231n

Solved Assignments from Stanford CS231n: Convolutional Neural Networks for Visual Recognition

ObjectDetection-XRay-Images

We used Deep Learning to classify the harmful objects using AlexNet model and compared the results from classical Machine Learning algorithms . We achieved an improvement of 0.2% in accuracy from the original paper used as reference.

ObstacleAvoidanceQLearning

Obstacle Avoidance through Q Learning

conv_arithmetic

A technical report on convolution arithmetic in the context of deep learning

awesome-semantic-segmentation

:metal: awesome-semantic-segmentation

data-science-question-answer

A repo for data science related questions and answers

stanford-cs-229-machine-learning

VIP cheatsheets for Stanford's CS 229 Machine Learning

awesome-autonomous-vehicles

Curated List of Self-Driving Cars and Autonomous Vehicles Resources