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algolynxathon-c2d

Worked examples of C2D on BCI data

babyagi

balancerpools_model

cadCAD model to simulate Balancer AMMs

bitcoinprice

Bitcoin price predictor.

blockly

The web-based visual programming editor.

brave-browser

Next generation Brave browser for macOS, Windows, Linux, and eventually Android

cortex

Deploy machine learning models in production

cryptocommit

Counts commits across all organisation repos and gives you some pretty charts

deepfakecapsulegan

Using Capsule Networks in GANS to generate very realistic fake images that could perhaps be used for deepfakes

deepfakes

Implementation of Deep Fakes algorithm,in tensorflow

deepfakes-1

This is the code for "DeepFakes" by Siraj Raval on Youtube

deepfakes_faceswap

from deekfakes' faceswap: https://www.reddit.com/user/deepfakes/

df

Larger resolution face masked, weirdly warped, deepfake,

distillation_privileged_information

ICLR 2016 paper.

dnn-model-services

A collection of SingularityNET services using third party DNN models.

example-service

faceswap

Non official project based on original /r/Deepfakes thread. Many thanks to him!

faceswap_pytorch

Deep fake ready to train on any 2 pair dataset.

hyperopt

Distributed Asynchronous Hyperparameter Optimization in Python

improved-gan

code for the paper "Improved Techniques for Training GANs"

langchain

⚡ Building applications with LLMs through composability ⚡

list-purgatory

😈 Lists tracking assets and accounts in Purgatory, which has consequences in the Ocean Market UI.

lm-hackers

Hackers' Guide to Language Models

mantaray_jupyter

The target for the Mantaray IPython scripts, converted to Jupyter Notebook format.

market

🧜‍♀️ THE Data Market

megatron-lm

Ongoing research training transformer language models at scale, including: BERT

multi-spectral-image-synthesis-for-crop-weed-segmentation-in-precision-farming

In this work, we propose an alternative solution with respect to the common data augmentation techniques, applying it to the fundamental problem of crop/weed segmentation in precision farming. Starting from real images, we create semi-artificial samples by replacing the most relevant object classes (i.e., crop and weeds) with synthesized counterparts. To do that, we employ a conditional GAN (cGAN), where the generative model is trained by conditioning the shape of the generated object. Moreover, in addition to RGB data, we take into account also near-infrared information, generating four channel multi-spectral synthetic images.

nautilus

A typescript library helping to navigate the OCEAN 🌊

node-blockly

Blockly for Node.js and Browser via CommonJS module

ocean-academy

Official repo of Ocean Academy