unverciftci

OpenBot

OpenBot leverages smartphones as brains for low-cost robots. We have designed a small electric vehicle that costs about $50 and serves as a robot body. Our software stack for Android smartphones supports advanced robotics workloads such as person following and real-time autonomous navigation.

MathToolsforNeuroscience

Materials for Mathematical Tools for Neuroscience course at Harvard (Neurobio 212)

deep_kolmogorov

Numerically Solving Parametric Families of High-Dimensional Kolmogorov Partial Differential Equations via Deep Learning (NeurIPS 2020)

gradslam

gradslam is an open source differentiable dense SLAM library for PyTorch

BDS

Code and examples from Business Data Science

stat451-machine-learning-fs20

STAT 451: Intro to Machine Learning @ UW-Madison (Fall 2020)

Deep-learning-in-cloud

List of Deep Learning Cloud Providers

proxemo

Emotion-based navigation for social robots

training-data-analyst

Labs and demos for courses for GCP Training (http://cloud.google.com/training).

pifuhd

High-Resolution 3D Human Digitization from A Single Image.

dvc

🦉 ML Experiments and Data Management with Git

FrEIA

Framework for Easily Invertible Architectures

practical-python

Practical Python Programming (course by @dabeaz)

bumblebee

🚕 A spreadsheet-like data preparation web app that works over Optimus (Pandas, Dask, cuDF, Dask-cuDF, Spark and Vaex)

XLM

PyTorch original implementation of Cross-lingual Language Model Pretraining.

mathematicalpython

Introduction to Mathematical Computing with Python and Jupyter

amazon-sagemaker-nvidia-ngc-examples

Examples showing use of NGC containers and models withing Amazon SageMaker

dl3d-seminar

Course webpage for COMP 790, (Deep) Learning from Limited Labeled Data

autodist

Simple Distributed Deep Learning on TensorFlow

robustness

A library for experimenting with, training and evaluating neural networks, with a focus on adversarial robustness.

tensor-house

A collection of reference Jupyter notebooks and demo AI/ML applications for enterprise use cases: marketing, pricing, supply chain, smart manufacturing, and more.

Learning-Data-Distributions-with-Deep-Generative-Models

Using Normalizing Flows and VAEs to learn data distributions of datasets.

torchdyn

A PyTorch library entirely dedicated to neural differential equations, implicit models and related numerical methods

AutoDL

Automated Deep Learning without ANY human intervention. 1'st Solution for AutoDL challenge@NeurIPS.

PyMesh

Geometry Processing Library for Python

wtfpython

What the f*ck Python? 😱

deepul

destructive-deep-learning

Destructive deep learning estimators and functions that are compatible with scikit-learn.

UMNN

Implementation of Unconstrained Monotonic Neural Network and the related experiments. These architectures are particularly useful for modelling monotonic transformations in normalizing flows.

autoregressive-energy-machines

Autoregressive Energy Machines