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ai-conference-info

Extensive acceptance rates and information of main AI conferences

analyzekge

Analyzing knowledge graph embedding methods, including TransE, DistMult, CP, SimplE, ComplEx, Quaternion

arxiv-sanity-preserver

Web interface for browsing, search and filtering recent arxiv submissions

awesome-open-problems-for-ml

A curated list of awesome open problems and datasets calling for research using machine learning tools

awesome-quantum-machine-learning

Here you can get all the Quantum Machine learning Basics, Algorithms ,Study Materials ,Projects and the descriptions of the projects around the web

blist

A list-like type with better asymptotic performance and similar performance on small lists

capsnet-keras

A Keras implementation of CapsNet in NIPS2017 paper "Dynamic Routing Between Capsules". Now test error < 0.4%.

char-rnn

Multi-layer Recurrent Neural Networks (LSTM, GRU, RNN) for character-level language models in Torch

citationanalysis

Citation Analysis on the Microsoft Academic Graph Dataset

cofactor

CoFactor: Regularizing Matrix Factorization with Item Co-occurrence

corenlp

Stanford CoreNLP: A Java suite of core NLP tools.

corenlp-examples

Stanford Core NLP API usage examples

covid19inspection

COVID-19 Inspection

cspublicationcrawler

Computer science scholarly data crawler

d3

Bring data to life with SVG, Canvas and HTML. :bar_chart::chart_with_upwards_trend::tada:

data_science_python

Source code for the "Practical Data Science in Python" tutorial

deeplearning4nlp-tutorial

Hands-on tutorial on deep learning with a special focus on Natural Language Processing (NLP)

fasttext

Library for fast text representation and classification.

gensim

Topic Modelling for Humans

hyde_pixyll

Jekyll theme: Hyde sidebar + Pixyll clean style.

icdar2019_ctdar

The ICDAR 2019 cTDaR is to evaluate the performance of methods for table detection (TRACK A) and table recognition (TRACK B). For the first track, document images containing one or several tables are provided. For TRACK B two subtracks exist: the first subtrack (B.1) provides the table region. Thus, only the table structure recognition must be performed. The second subtrack (B.2) provides no a-priori information. This means, the table region and table structure detection has to be done.

interview_problems

Example coding interview problems found through online resources, Cracking the Code book, mentors and colleagues. An additional robust resource for example interview questions and solutions is at https://github.com/mmihaljevic/algortihms_challenges.

java-ml-utils

A collection of useful utilities for machine learning and text mining in Java

kg20c

A Scholarly Knowledge Graph Benchmark Dataset

lda2vec

leela-chess

A chess adaption of GCP's Leela Zero

mei-kge

High-performance implementations of W2V, DistMult, CP, SimplE, ComplEx, RotatE, Quaternion, and MEI. Paper: Multi-Partition Embedding Interaction with Block Term Format for Knowledge Graph Completion (ECAI 2020).

meim-kge

Including W2V, DistMult, CP, SimplE, ComplEx, RotatE, Quaternion, MEI, MEIM. Paper: MEIM: Multi-partition Embedding Interaction Beyond Block Term Format for Efficient and Expressive Link Prediction (IJCAI 2022).

ml_for_hackers

Code accompanying the book "Machine Learning for Hackers"

models

Models built with TensorFlow