Stars
Deploy NVIDIA'S GPU Accelerated AI models as API using Langserve
A comprehensive reference for all topics related to Natural Language Processing
Trankit is a Light-Weight Transformer-based Python Toolkit for Multilingual Natural Language Processing
The Incredible PyTorch: a curated list of tutorials, papers, projects, communities and more relating to PyTorch.
Automatically extract grammatical edits from parallel original and corrected sentences.
ERRor ANnotation Toolkit: Automatically extract and classify grammatical errors in parallel original and corrected sentences.
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
TensorFlow code and pre-trained models for BERT
🤗 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.
Python port of SymSpell: 1 million times faster spelling correction & fuzzy search through Symmetric Delete spelling correction algorithm
jalajthanaki / stanford-cs-229-machine-learning
Forked from afshinea/stanford-cs-229-machine-learningVIP cheatsheets for Stanford's CS 229 Machine Learning
A selection of printable, one-page cheatsheets, generated from Markdown using Pandoc & LaTeX
Studio: Simplify and expedite model building process
VIP cheatsheets for Stanford's CS 229 Machine Learning
📡 All You Need to Know About Deep Learning - A kick-starter
Alphabetical list of free/public domain datasets with text data for use in Natural Language Processing (NLP)
A python library for decision tree visualization and model interpretation.
Using pandas for Better (and Worse) Data Science
Jupyter notebook and datasets from the pandas video series
Quick PyTorch introduction and tutorial. Targets computer vision, graphics and machine learning researchers eager to try a new framework.
Introduction and Career Guide for Data Science enthusiasts
All Algorithms implemented in Python
A set of tutorials to implement the Federated Averaging algorithm on TensorFlow.
Tutorial material and instruction for scipy 2018 jupyterlab tutorial
Cracking the Coding Interview 6th Ed. Python Solutions