jorvredeveld
✔ Master's student Data Science ✔ Bachelor's degree in Information Science ✔ Certified Microsoft Excel Specialist
jorvredeveld's Stars
coinbase-samples/exchange-scripts-py
Coinbase Exchange FIX API sample scripts
oobabooga/text-generation-webui
A Gradio web UI for Large Language Models.
sigma67/spotify_to_ytmusic
Clone a Spotify playlist to YouTube Music
plumpmonkey/CICIoT2023
digininja/DVWA
Damn Vulnerable Web Application (DVWA)
apache/iceberg
Apache Iceberg
harshilpatel1799/IoT-Network-Intrusion-Detection-and-Classification-using-Explainable-XAI-Machine-Learning
The continuing increase of Internet of Things (IoT) based networks have increased the need for Computer networks intrusion detection systems (IDSs). Over the last few years, IDSs for IoT networks have been increasing reliant on machine learning (ML) techniques, algorithms, and models as traditional cybersecurity approaches become less viable for IoT. IDSs that have developed and implemented using machine learning approaches are effective, and accurate in detecting networks attacks with high-performance capabilities. However, the acceptability and trust of these systems may have been hindered due to many of the ML implementations being ‘black boxes’ where human interpretability, transparency, explainability, and logic in prediction outputs is significantly unavailable. The UNSW-NB15 is an IoT-based network traffic data set with classifying normal activities and malicious attack behaviors. Using this dataset, three ML classifiers: Decision Trees, Multi-Layer Perceptrons, and XGBoost, were trained. The ML classifiers and corresponding algorithm for developing a network forensic system based on network flow identifiers and features that can track suspicious activities of botnets proved to be very high-performing based on model performance accuracies. Thereafter, established Explainable AI (XAI) techniques using Scikit-Learn, LIME, ELI5, and SHAP libraries allowed for visualizations of the decision-making frameworks for the three classifiers to increase explainability in classification prediction. The results determined XAI is both feasible and viable as cybersecurity experts and professionals have much to gain with the implementation of traditional ML systems paired with Explainable AI (XAI) techniques.
HLTCHKUST/chatgpt-evaluation
This respository contains the code for extracting the test samples we used in our paper: "A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity"
tenable/awesome-llm-cybersecurity-tools
A curated list of large language model tools for cybersecurity research.
shramos/Awesome-Cybersecurity-Datasets
A curated list of amazingly awesome Cybersecurity datasets
inverse-scaling/prize
A prize for finding tasks that cause large language models to show inverse scaling
EleutherAI/lm-evaluation-harness
A framework for few-shot evaluation of language models.
prodramp/DeepWorks
A collection of Deep Learning projects and resources
geeks-of-data/knowledge-gpt
Extract knowledge from all information sources using gpt and other language models. Index and make Q&A session with information sources.
langchain-ai/langchain
🦜🔗 Build context-aware reasoning applications
replicate/cog_stanford_alpaca
Code and documentation to train Stanford's Alpaca models, and generate the data.
gururise/AlpacaDataCleaned
Alpaca dataset from Stanford, cleaned and curated
nitebyte/TripTeller
nomic-ai/gpt4all
GPT4All: Chat with Local LLMs on Any Device
facebookresearch/segment-anything
The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.
microsoft/SynapseML
Simple and Distributed Machine Learning
microsoft/JARVIS
JARVIS, a system to connect LLMs with ML community. Paper: https://arxiv.org/pdf/2303.17580.pdf
Significant-Gravitas/AutoGPT
AutoGPT is the vision of accessible AI for everyone, to use and to build on. Our mission is to provide the tools, so that you can focus on what matters.
visenger/awesome-mlops
A curated list of references for MLOps
lllyasviel/ControlNet
Let us control diffusion models!
davila7/talk-to-gpt-3
Whisper + OpenAI + Speech Recognition
radi-cho/datasetGPT
A command-line interface to generate textual and conversational datasets with LLMs.
rupeshs/alpaca.cpp
Locally run an Instruction-Tuned Chat-Style LLM (Android/Linux/Windows/Mac)
GeWanying/raw-pc-darts-anti-spoofing
This repository includes the code to reproduce our paper "Raw Differentiable Architecture Search for Speech Deepfake and Spoofing Detection" (https://arxiv.org/abs/2107.12212) published in the ASVspoof 2021 workshop.
GeWanying/shap-anti-spoofing
This repository includes the code to reproduce our paper [Explainable deepfake and spoofing detection: an attack analysis using SHapley Additive exPlanations] accepted in The Speaker and Language Recognition Workshop (Speaker Odyssey 2022).