Stars
Implementation of popular ML algorithms from scratch
Code samples and other materials for presentations by Bespoke tech team members
Labs and demos for courses for GCP Training (http://cloud.google.com/training).
Best practices for product search in English and Thai using Elasticsearch
This repository contains small projects related to Neural Networks and Deep Learning in general. Subjects are closely linekd with articles I publish on Medium. I encourage you both to read as well …
A booklet on machine learning systems design with exercises. NOT the repo for the book "Designing Machine Learning Systems", which is `dmls-book`
Learn how to design large-scale systems. Prep for the system design interview. Includes Anki flashcards.
Curated coding interview preparation materials for busy software engineers
A middle-to-high level open source algorithm book designed with coding interview at heart!
Turi Create simplifies the development of custom machine learning models.
The Web IR / NLP Group (WING)'s public reading group at the National University of Singapore.
DGLGraph によるグラフ深層学習のチュートリアル
📚 ✏️ 🎓 A collection of textbooks, links and resources during our studying years in NUS SoC
Plugin to integrate Learning to Rank (aka machine learning for better relevance) with Elasticsearch
A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning …
Data Analytics, Statistics, Visualization (R / Python)
Advanced Deep Learning and Reinforcement Learning course taught at UCL in partnership with Deepmind
NBoost is a scalable, search-api-boosting platform for deploying transformer models to improve the relevance of search results on different platforms (i.e. Elasticsearch)
The official repository for ERNIE 4.5 and ERNIEKit – its industrial-grade development toolkit based on PaddlePaddle.
🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.
Build a Search Engine with Python + Elasticsearch
NUS Deep Reinforcement Learning course webpage (CS6101-1820). For Semester II, 2018/2019.