Code & Data for Enhancing Photorealism Enhancement

A complete guide to start and improve in machine learning (ML), artificial intelligence (AI) in 2021 without ANY background in the field and stay up-to-date with the latest news and state-of-the-art techniques!

Dense Prediction Transformers

The fuzzer afl++ is afl with community patches, qemu 5.1 upgrade, collision-free coverage, enhanced laf-intel & redqueen, AFLfast++ power schedules, MOpt mutators, unicorn_mode, and a lot more!

Artificial Intelligence with Python Cookbook, published by Packt

Python API for Intelligent Visual Data Discovery

Intel Owl: analyze files, domains, IPs in multiple ways from a single API at scale

Roadmap to becoming an Artificial Intelligence Expert in 2020

Threat Pursuit Virtual Machine (VM): A fully customizable, open-sourced Windows-based distribution focused on threat intelligence analysis and hunting designed for intel and malware analysts as well as threat hunters to get up and running quickly.

Code repository of the paper Neural circuit policies enabling auditable autonomy published in Nature Machine Intelligence

python powered Intelligent System

A curated list of Awesome Threat Intelligence resources

Resources for the study group in IIT Guwahati focused on learning Artificial Intelligence.

Hands-On Artificial Intelligence for Cybersecurity, publised by Packt

Slides, paper notes, class notes, blog posts, and research on machine learning, statistics, and artificial intelligence.

Scripts and source code for SDN NFV Hands-on Labs

Generalized Aviation: Open source autonomous aviation software platform, designed for fully autonomous drones and flying cars.

Open Cyber Threat Intelligence Platform

VIP cheatsheets for Stanford’s CS 221 Artificial Intelligence

Neural Architecture Search Powered by Swarm Intelligence

Jupyter Notebook and Python business intelligence tools and techniques. [Raw upload]

ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.