Author: Deep Learner

Tool

Utilities and information for the signals.numer.ai tournament

Utilities and information for the signals.numer.ai tournament using eodhistoricaldata.com eodhistoricaldata.com provides excellent historical price coverage for the signals universe. There are two main challenges with it: Ticker mapping from bloomberg to eod tickers Lack of coverage for Japan, Czech Republic and New Zealand Building the ticker map To build the mapping from bloomberg_ticker to eodhd, use: python build_eodhd_map.py This will retrieve: live_universe (a small 40 KB file just listing the ~5,340 tickers in current round) historical_targets (a large 150 MB […]

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Bot

An open source bot worked on by many people to create a good and safe moderation bot for all

Greetings, I see you have stumbled upon project glow. Project glow is an open source bot worked on by many people to create agood and safe moderation bot for all. Adding to the bot is simple! First create a fork of the bot by clicking on the fork button in the top right coner. Then, create a clone by installing git into your pc. After this is done and setup, run git clone https://github.com/Glowstik-YT/projglow in whatever directory you wish to […]

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Transformer

StARformer: Transformer with State-Action-Reward Representations

This repository contains the PyTorch implementation for our paper titled StARformer: Transformer with State-Action-Reward Representations.We learn local State-Action-Reward representations (StAR-representations) to improve (long) sequence modeling for reinforcement learning (and imitation learning). Results Installation Dependencies can be installed by Conda: conda env create -f my_env.yml And install Atari ROMs. Datasets Please follow this instruction for datasets. Example usage See run.sh or below:

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Attributes

FACIAL: Synthesizing Dynamic Talking Face With Implicit Attribute Learning

PyTorch implementation for the paper: FACIAL: Synthesizing Dynamic Talking Face with Implicit Attribute Learning Chenxu Zhang,Yifan Zhao,Yifei Huang,Ming Zeng,Saifeng Ni,Madhukar Budagavi,Xiaohu Guo ICCV 2021 Run demo on Google Colab Requirements conda create -n audio_face conda activate audio_face sudo apt-get install ffmpeg pip install -r requirements.txt you may add opencv by conda. Citation

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Attention

AASIST: Audio Anti-Spoofing using Integrated Spectro-Temporal Graph Attention Networks

This repository provides the overall framework for training and evaluating audio anti-spoofing systems proposed in ‘AASIST: Audio Anti-Spoofing using Integrated Spectro-Temporal Graph Attention Networks’ Getting started requirements.txt must be installed for execution. We state our experiment environment for those who prefer to simulate as similar as possible. pip install -r requirements.txt Our environment (for GPU training) Based on a docker image: pytorch:1.6.0-cuda10.1-cudnn7-runtime GPU: 1 NVIDIA Tesla V100 About 16GB is required to train AASIST using a batch size of 24 […]

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Tool

Github dorking tool with python

Supply a list of dorks and, optionally, one of the following: a user (-u) a file with a list of users (-uf) an organization (-org) a file with a list of organizations (-of) a repo (-r) You can also pass: an output directory to store results (-o) a filename to store valid items, if your users or org file may contain nonexistent users/orgs (-vif) All input files (dorks, users, or orgs) should be newline-separated. Usage Clone the repository, then runpip […]

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