lgmars Goto Github PK

aespayload

AES payload injector

alacritty

A cross-platform, GPU-accelerated terminal emulator

asus-wmi-sensors

Linux HWMON (lmsensors) sensors driver for various ASUS Ryzen and Threadripper motherboards

awesome-deep-learning-papers

The most cited deep learning papers

awesome-mlops

A curated list of references for MLOps

baselines

OpenAI Baselines: high-quality implementations of reinforcement learning algorithms

bitbar

Free app to put the output from any script in your Mac OS X Menu Bar

capstone

Capstone disassembly/disassembler framework: Core (Arm, Arm64, Mips, PPC, Sparc, SystemZ, X86, X86_64, XCore) + bindings (Python, Java, Ocaml)

chiaki

Free and Open Source PS4 Remote Play Client

class-dump

Generate Objective-C headers from Mach-O files.

configs

my config files and scripts

convnet-benchmarks

Easy benchmarking of all publicly accessible implementations of convnets

create_os_x_vm_install_dmg

darwin-xnu

The Darwin Kernel (mirror)

deep-learning-papers-reading-roadmap

Deep Learning papers reading roadmap for anyone who are eager to learn this amazing tech!

deepmind-research

This repository contains implementations and illustrative code to accompany DeepMind publications

deeprlhacks

Hacks for training RL systems from John Schulman's lecture at Deep RL Bootcamp (Aug 2017)

determined

Determined: Deep Learning Training Platform

diehard

DieHard: An error-resistant memory allocator for Windows, Linux, and Mac OS X

dopamine

Dopamine is a research framework for fast prototyping of reinforcement learning algorithms.

dqn_zoo

drammer

Native binary for testing Android phones for the Rowhammer bug

drk

The DrK Attack - Proof of concept

elf

An Extensive, Lightweight and Flexible Research Platform for Real-time Strategy Games

filebrowser

iOS 7 File Browser

flashlight

The missing Spotlight plugin system

fruitstrap

Install and debug iPhone apps from the command line, without using Xcode

ghidra

google-research

Google AI Research

gym

A toolkit for developing and comparing reinforcement learning algorithms.