github.com/rspadim/Adv_Fin_ML
rspadim/Adv_Fin_ML
Advances in Financial Machine Learning by Marcos Lopez De Prado (⭐ 54)
github.com/philipperemy/fractional-differentiation-time-series
philipperemy/fractional-differentiation-time-series
As described in Advances of Machine Learning by Marcos Prado. (⭐ 122)
github.com/fernandodelacalle/adv-financial-ml-marcos-exercises
fernandodelacalle/adv-financial-ml-marcos-exercises
Exercises of the book: Advances in Financial Machine Learning by Marcos Lopez de Prado (⭐ 224)
github.com/BlackArbsCEO/Adv_Fin_ML_Exercises
BlackArbsCEO/Adv_Fin_ML_Exercises
Experimental solutions to selected exercises from the book [Advances in Financial Machine Learning by Marcos Lopez De Prado] (⭐ 1888)
github.com/JackKuo666/NLP_basis
JackKuo666/NLP_basis
This is the notes and code I took while studying an NLP tutorial [2019 Latest AI Natural Language Processing Deep Machine Learning Top Project Practical Course] (⭐ 453)
arxiv.org/abs/2007.08223v1
An Efficient Mixture of Deep and Machine Learning Models for COVID-19 and Tuberc…
Clinicians in the frontline need to assess quickly whether a patient with symptoms indeed has COVID-19 or not. The difficulty of this task is exacerbated in low resource settings that may not have access to biotechnology tests. Furthermore, Tuberculo...
arxiv.org/abs/1903.12262v1
Towards Standardization of Data Licenses: The Montreal Data License
This paper provides a taxonomy for the licensing of data in the fields of artificial intelligence and machine learning. The paper's goal is to build towards a common framework for data licensing akin to the licensing of open source software. Increase...
arxiv.org/abs/2208.00766v1
Tutorial on the development of AI models for medical image analysis
The idea of using computers to read medical scans was introduced as early as 1966. However, limits to machine learning technology meant progress was slow initially. The Alexnet breakthrough in 2012 sparked new interest in the topic, which resulted in...
www.bing.com/ck/a?!&&p=7939ebd09a623e1c612e9de8eeb3ecc3df2b0366b955142d4a16e7b77def66bcJmltdHM9MTc3MjQ5NjAwMA&ptn=3&ver=2&hsh=4&fclid=0f9e35d1-d29c-63ef-18b9-22c0d3b262e6&u=a1aHR0cHM6Ly93d3cueW91dHViZS5jb20vd2F0Y2g_dj11dXpNNzEtWk90Yw&ntb=1
Build a Bigger Chest in 4 Weeks – Top 5 Exercises
Build a Bigger Chest in 4 Weeks – Top 5 Exercises 00:00 Intro 00:08 Hammer Strength Bench Press 00:41 Incline Barbell Bench Press 01:13 Incline Dumbbell Bench Press 01:45 Seated Machine Fly...
github.com/konstantint/PassportEye
konstantint/PassportEye
Extraction of machine-readable zone information from passports, visas and id-cards via OCR (⭐ 441)
arxiv.org/abs/1011.2946v1
A Survey of System Security in Contactless Electronic Passports
A traditional paper-based passport contains a Machine- Readable Zone (MRZ) and a Visual Inspection Zone (VIZ). The MRZ has two lines of the holder's personal data, some document data, and verification characters encoded using the Optical Character...
arxiv.org/abs/cmp-lg/9601006v1
Possessive Pronouns as Determiners in Japanese-to-English Machine Translation
Possessive pronouns are used as determiners in English when no equivalent would be used in a Japanese sentence with the same meaning. This paper proposes a heuristic method of generating such possessive pronouns even when there is no equivalent in...
arxiv.org/abs/2210.01970v2
Evaluate & Evaluation on the Hub: Better Best Practices for Data and Model Measu…
Evaluation is a key part of machine learning (ML), yet there is a lack of support and tooling to enable its informed and systematic practice. We introduce Evaluate and Evaluation on the Hub --a set of tools to facilitate the evaluation of models and...
arxiv.org/abs/2403.07008v2
AutoEval Done Right: Using Synthetic Data for Model Evaluation
The evaluation of machine learning models using human-labeled validation data can be expensive and time-consuming. AI-labeled synthetic data can be used to decrease the number of human annotations required for this purpose in a process called autoeva...
github.com/aliasgharheidaricom/Harris-Hawks-Optimization-Algorithm-and-Applications
aliasgharheidaricom/Harris-Hawks-Optimization-Algorithm-and-Applications
Harris Hawks Optimization (HHO) is a nature-inspired metaheuristic algorithm that simulates the cooperative hunting behavior of Harris' hawks. Widely used in engineering, machine learning, and resource allocation, HHO is renowned for its simplicity, versatilit…
arxiv.org/abs/2308.11111v1
CAME: Contrastive Automated Model Evaluation
The Automated Model Evaluation (AutoEval) framework entertains the possibility of evaluating a trained machine learning model without resorting to a labeled testing set. Despite the promise and some decent results, the existing AutoEval methods heavi...
arxiv.org/abs/2212.02155v1
Unexpectedly Useful: Convergence Bounds And Real-World Distributed Learning
Convergence bounds are one of the main tools to obtain information on the performance of a distributed machine learning task, before running the task itself. In this work, we perform a set of experiments to assess to which extent, and in which way, s...
arxiv.org/abs/1904.11882v1
Smart Laptop Bag with Machine Learning for Activity Recognition
In todays world of smart living, the smart laptop bag, presented in this paper, provides a better solution to keep track of our precious possessions and monitoring them in real time. As the world moves towards a much tech-savvy direction, the novel l...
en.wikipedia.org/wiki/Workflow_pattern
Workflow pattern - Wikipedia
Computing Patterns for Grid Workflows" Archived 2017-12-30 at the Wayback Machine, In Proc. of the HPDC2006 Workshop on Workflows in Support of Large-Scale
arxiv.org/abs/2308.11531v2
Empowering Refugee Claimants and their Lawyers: Using Machine Learning to Examin…
Our project aims at helping and supporting stakeholders in refugee status adjudications, such as lawyers, judges, governing bodies, and claimants, in order to make better decisions through data-driven intelligence and increase the understanding and t...