Automatic neural network visualizations generated in your browser!

Weapon to fight against conflicts in Vim.

Master programming by recreating your favorite technologies from scratch.

Improved Wave-U-Net implemented in Pytorch

🤖💤 High-contrast, Futuristic & Vibrant Coloursheme for Neovim

Poimandres colorscheme for Neovim written in Lua

Synthwave x Fluoromachine port for Neovim

This repository implements the Wave-U-net architecture in TensorFlow 2

Implementation of the Wave-U-Net for audio source separation

Papers and Codes for the deep learning in hyperbolic space

🏄 Scalable embedding, reasoning, ranking for images and sentences with CLIP

A project using a machine learning model to determine if an NHL player is overpaid or underpaid.

🌒 Nord for Neovim, but warmer and darker. Supports a variety of plugins and other platforms.

GitHub's Neovim themes

Library that contains implementations of machine learning components in the hyperbolic space

SMS-WSJ: Spatialized Multi-Speaker Wall Street Journal database for multi-channel source separation and recognition

NumPy implementation of Poincaré Embeddings for Learning Hierarchical Representations (Facebook Research)

Source code for the ICML'18 paper "Hyperbolic Entailment Cones for Learning Hierarchical Embeddings", https://arxiv.org/abs/1804.01882

Source code for the paper "Hyperbolic Neural Networks", https://arxiv.org/abs/1805.09112

Real-time Markdown previewer

EAF, an extensible framework that revolutionizes the graphical capabilities of Emacs

This repo hosts the code and model of "Separate What You Describe: Language-Queried Audio Source Separation", Interspeech 2022

Audio source seperation using DeepLabv3+ Segmentation Model link:https://colab.research.google.com/drive/1azCXH4udXSTkEf3JLihdMTPoCKB7IETq

Code for the paper: Explaining Deep Learning Embeddings for Speech Emotion Recognition by Predicting Interpretable Acoustic Features

Evaluate EfficientAT models on the Holistic Evaluation of Audio Representations Benchmark.

A Mel-frequency cepstrum core in FPGA

Environmental Sound Classification on Microcontrollers using Convolutional Neural Networks

Explanation of 1D CNN

Easy to use stem (e.g. instrumental/vocals) separation from CLI or as a python package, using a variety of amazing pre-trained models (primarily from UVR)