The Qualcomm® AI Hub Models are a collection of state-of-the-art machine learning models optimized for performance (latency, memory etc.) and ready to deploy on Qualcomm® devices.

Companion Jupyter Notebooks for the RFSoC-Book.

A PyTorch library and evaluation platform for end-to-end compression research

🔀 Neural Network (NN) Streamer, Stream Processing Paradigm for Neural Network Apps/Devices.

OnlyFans Automation Tool

In this repository, I will share some useful notes and references about deploying deep learning-based models in production.

WebRTC and ORTC implementation for Python using asyncio

Sionna: An Open-Source Library for Next-Generation Physical Layer Research

GFPGAN aims at developing Practical Algorithms for Real-world Face Restoration.

Open source SDR 4G software suite from Software Radio Systems (SRS) https://docs.srsran.com/projects/4g

cuSignal - RAPIDS Signal Processing Library

Blosm addon for Blender. A few clicks import of Google 3D cities, OpenStreetMap, terrain. Global coverage. Source code is in the branch 'release'.

A simulation framework of adaptive video streaming over wireless home networks using ns-3. The final project for CPSC 8510 Software Systems for Data Communication.

ns-3 integration of the quasi deterministic ray tracer https://github.com/signetlabdei/qd-realization

ML-based compensation of RFFE distortion in communication systems

NYU Tandon Machine Learning Summer Program 2020

pyforest - feel the bliss of automated imports

A C++ standalone library for machine learning

An implementation of a traffic model for VR applications on ns-3

FEDML - The unified and scalable ML library for large-scale distributed training, model serving, and federated learning. FEDML Launch, a cross-cloud scheduler, further enables running any AI jobs o…

FedML - The Research and Production Integrated Federated Learning Library: https://fedml.ai

An open-source framework for machine learning and other computations on decentralized data.

YOLOv3 in PyTorch > ONNX > CoreML > TFLite

TensorFlow for macOS 11.0+ accelerated using Apple's ML Compute framework.

NYU Prince set up

A quick reference to access NYU High Performance Computing

NYU Deep Learning Spring 2020