Tobias Sterbak's starred repositories
DeepSpeech
DeepSpeech is an open source embedded (offline, on-device) speech-to-text engine which can run in real time on devices ranging from a Raspberry Pi 4 to high power GPU servers.
NLP-progress
Repository to track the progress in Natural Language Processing (NLP), including the datasets and the current state-of-the-art for the most common NLP tasks.
stanford-cs-229-machine-learning
VIP cheatsheets for Stanford's CS 229 Machine Learning
cookiecutter-pypackage
Cookiecutter template for a Python package.
PyTorch-NLP
Basic Utilities for PyTorch Natural Language Processing (NLP)
mara-pipelines
A lightweight opinionated ETL framework, halfway between plain scripts and Apache Airflow
Attention-Gated-Networks
Use of Attention Gates in a Convolutional Neural Network / Medical Image Classification and Segmentation
UnsupervisedMT
Phrase-Based & Neural Unsupervised Machine Translation
ELMoForManyLangs
Pre-trained ELMo Representations for Many Languages
TernausNet
UNet model with VGG11 encoder pre-trained on Kaggle Carvana dataset
Keras-Project-Template
A project template to simplify building and training deep learning models using Keras.
keras-language-modeling
:book: Some language modeling tools for Keras
data_science_delivered
Observations from Ian on successfully delivering data science products
keras-text
Text Classification Library in Keras
ipypublish
A workflow for creating and editing publication ready scientific reports and presentations, from one or more Jupyter Notebooks, without leaving the browser!
data-science-stack-cookiecutter
🐳📊🤓Cookiecutter template to launch an awesome dockerized Data Science toolstack (incl. Jupyster, Superset, Postgres, Minio, AirFlow & API Star)
mara-example-project-2
An example mini data warehouse for python project stats, template for new projects
Linear-Attention-Recurrent-Neural-Network
A recurrent attention module consisting of an LSTM cell which can query its own past cell states by the means of windowed multi-head attention. The formulas are derived from the BN-LSTM and the Transformer Network. The LARNN cell with attention can be easily used inside a loop on the cell state, just like any other RNN. (LARNN)