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seq2cause: Turns any discrete sequence of events into a causal graph using autoregressive models (LLaMA, GPT, RNN, Mamba).
pyCausalFS：A Python Library of Causality-based Feature Selection for Causal Structure Learning and Classification
RWKV (pronounced RwaKuv) is an RNN with great LLM performance, which can also be directly trained like a GPT transformer (parallelizable). We are at RWKV-7 "Goose". So it's combining the best of RN…
An "Awesome" list of code review resources - articles, papers, tools, etc
This is a list of interesting papers and projects about TinyML.
Overview of Modern Deep Learning Techniques Applied to Natural Language Processing
List of Computer Science courses with video lectures.
RGBD Semantic Segmentation on NYUv2, SUN RGBD and CityScapes (pretraining with SceneNet RGB-D)
Keras implementation of Deeplab v3+ with pretrained weights
Solution to homework problems 2021 of Chalmers Neural Network courses
A french sequence to sequence pretrained model
A Python toolkit for Reservoir Computing and Echo State Network experimentation based on pyTorch. EchoTorch is the only Python module available to easily create Deep Reservoir Computing models.
12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all
Katana project is a FastAPI template for ASAP 🚀 ML API deployment
DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphic…
Uplift modeling and causal inference with machine learning algorithms
T81-558: Keras - Applications of Deep Neural Networks @Washington University in St. Louis
Slides and Jupyter notebooks for the Deep Learning lectures at Master Year 2 Data Science from Institut Polytechnique de Paris
TensorFlow for macOS 11.0+ accelerated using Apple's ML Compute framework.
Django REST Framework integration with SQLAlchemy
Dockerized Django Back-end API using DRF with Angular Front-end Tutorial