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modernist

Modernist is a Jekyll theme for GitHub Pages

msg-stylegan-tf

MSG StyleGAN in tensorflow

myweather

My Weather Android App

ncsn

Noise Conditional Score Networks

netron

Visualizer for deep learning and machine learning models

neuralbabytalk

Pytorch code of for our CVPR 2018 paper "Neural Baby Talk"

nevergrad

A Python toolbox for performing gradient-free optimization

nlp

Natural Language Processing Best Practices & Examples

nlp-project-code

nvidia-docker

Build and run Docker containers leveraging NVIDIA GPUs

object-detection-python

🐍 Run Object Detection Inferences in Python

object-detection-react

⚛️ Real-Time Custom Object Detection with TensorFlow.js

object_recognition_drone

obstructionremoval

[CVPR 2020] Learning to See Through Obstructions

ocean_observation_facetview

This is a FacetView setup for ocean observation Crawled Data.

oft

omid.github.io

test project

openpnp

Open Source SMT Pick and Place Hardware and Software

pix2pixhd

Synthesizing and manipulating 2048x1024 images with conditional GANs

porcupine

On-device wake word detection powered by deep learning.

pseudo3d.js

A simple pseudo-3D renderer built with vanilla JS

pybreak

A Python3 tool that brings StackOverflow to your terminal when your Python script breaks!

python-machine-learning-book-2nd-edition

The "Python Machine Learning (2nd edition)" book code repository and info resource

python-speech-recognition

Speech Recognition with Python examples

pythonrobotics

Python sample codes for robotics algorithms.

ravens

Train robotic agents to learn pick and place with deep learning for vision-based manipulation in PyBullet. Transporter Nets, CoRL 2020.

recommenders

Best Practices on Recommendation Systems

recommenders_engine_example_layout

An example cross-platform movie recommendation application built using Xamarin.Forms and Azure ML service

rich

Rich is a Python library for rich text and beautiful formatting in the terminal.

robotics-object-pose-estimation

A complete end-to-end demonstration in which we collect training data in Unity and use that data to train a deep neural network to predict the pose of a cube. This model is then deployed in a simulated robotic pick-and-place task.