Official repo for the "Reducing Bias in Modeling Real-world Password Strength via Deep Learning and Dynamic Dictionaries" by Dario Pasquini, Marco Cianfriglia, Giuseppe Ateniese and Massimo Bernaschi presented at USENIX Security 2021
The main features of theadamproject/adams are: Machine Learning Models.
Open-source alternatives to theadamproject/adams include: avik-jain/100-days-of-ml-code — This project is a structured educational curriculum designed to guide developers through the fundamentals of machine… borisbanushev/stockpredictionai — This project is a collection of predictive models and quantitative tools for stock price forecasting. It implements a… cbailes/awesome-deep-trading — List of awesome resources for machine learning-based algorithmic trading. ceruleanacg/personae — 📈 Personae is a repo of implements and environment of Deep Reinforcement Learning & Supervised Learning for… chrisconlan/algorithmic-trading-with-python — Source code for Algorithmic Trading with Python (2020) by Chris Conlan. achillesrasquinha/bulbea — :boar: :bear: Deep Learning based Python Library for Stock Market Prediction and Modelling.
This project is a structured educational curriculum designed to guide developers through the fundamentals of machine learning. It functions as a technical skill builder, offering a curated roadmap of progressive coding challenges that cover core algorithms, statistical concepts, and essential data science libraries. The repository distinguishes itself through an iterative sequencing of content, organizing complex technical topics into a daily progression that facilitates incremental mastery. It integrates third-party academic lectures and educational resources to provide necessary theoretical
This project is a collection of predictive models and quantitative tools for stock price forecasting. It implements a variety of machine learning architectures, including generative adversarial networks, long short-term memory networks, and language models for financial analysis. The system distinguishes itself by combining time-series forecasting with natural language processing to convert financial news into numerical sentiment scores. It also incorporates synthetic market data generation and automated hyperparameter optimization using Bayesian and reinforcement learning methods to reduce p
List of awesome resources for machine learning-based algorithmic trading
:boar: :bear: Deep Learning based Python Library for Stock Market Prediction and Modelling