Ryan Paul Gozum's repositories
kedro
A Python framework for creating reproducible, maintainable and modular data science code.
BentoML
Unified Model Serving Framework 🍱
flink-training
Apache Flink Training Excercises
word-counter
An API which counts how many times a word exists in the webpage source.
lightning-flash
Collection of tasks for fast prototyping, baselining, finetuning and solving problems with deep learning.
onnx
Open standard for machine learning interoperability
tpot
A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
convolution-visualizer
Convolution visualizations
sparkit-learn
PySpark + Scikit-learn = Sparkit-learn
simpletransformers
Transformers for Classification, NER, QA, Language Modelling, Language Generation, T5, Multi-Modal, and Conversational AI
regex-notebooks
Sample usage of the Python module for regular expressions.
hey
HTTP load generator, ApacheBench (ab) replacement, formerly known as rakyll/boom
python-orb
Reusable Python orb for your CircleCI pipeline.
yolov5
YOLOv5 in PyTorch > ONNX > CoreML > TFLite
tf-quant-finance
High-performance TensorFlow library for quantitative finance.
lazynlp
Library to scrape and clean web pages to create massive datasets.
Orb-Project-Template
A starter template for orb projects. Build, test, and publish orbs automatically on CircleCI.
txtai
AI-powered search engine
quant-trading
Python trading and backtesting platform.
SentAugment
SentAugment is a data augmentation technique for NLP that retrieves similar sentences from a large bank of sentences. It can be used in combination with self-training and knowledge-distillation, or for retrieving paraphrases.
faiss
A library for efficient similarity search and clustering of dense vectors.
dexpression-pytorch
A PyTorch implementation of DeXpression for facial expression recognition.
spark_submit_airflow
Simple repo to demonstrate how to submit a spark job to EMR from Airflow
next-word-prediction
Generative Pretrained Transformer 2 (GPT-2) for Language Modeling using the PyTorch-Transformers library.
tokenizers
💥Fast State-of-the-Art Tokenizers optimized for Research and Production
transformers
🤗Transformers: State-of-the-art Natural Language Processing for Pytorch and TensorFlow 2.0.
datasets
🤗 Fast, efficient, open-access datasets and evaluation metrics for Natural Language Processing and more in PyTorch, TensorFlow, NumPy and Pandas