Data and tools for generating and inspecting OLMo pre-training data.
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Updated
Sep 5, 2024 - Python
Data and tools for generating and inspecting OLMo pre-training data.
SDN networks (Software Defined Networking ) are exposed to new security threats and attacks, especially Distributed Denial of Service (DDoS) attacks. For this aim, we have proposed a model able to detect and mitigate attacks automatically in SDN networks using Machine Learning (ML)
How to improve a 14% your image matching with only one line of code? BEBLID is the key!
A Hand Written Digit Recognition app trained on the MNIST dataset of Keras using the CNN model. skills used are Tensorflow, HTML,CSS,javascript.
Front end for Heart Disease Analysis and Prediction Project
Proyecto de entrenamiento de modelos de IA con aprendizaje por refuerzo (reinforcement learning) en Unity. Corresponde al trabajo práctico grupal de la materia INTELIGENCIA ARTIFICIAL (9525) de la Facultad de Ingeniería de la Universidad de Buenos Aires. Link a la demo (el comportamiento de los agentes puede verse afectado de forma negativa):
MLP Regression model to predict quantitative data
💻 Personal blog/website, built using Jekyll & Github pages.
Stroke-GFCN: segmentation of Ischemic brain lesions
Clone your favorite voice for your script
SMOTE-MR: A distributed Synthetic Minority Oversampling Technique (SMOTE) for Big Data which applies a MapReduce based-approach. SMOTE-MR is categorized as an `approximated/ non exact` solution. Also, there is an `exact` solution called SMOTE-BD written by the author (See: https://github.com/majobasgall/smote-bd)
Master's program ''Machine learning and high-load networks'' at the Higher School of Economics.
Kaggle Competition...
Implementação de algoritmos de aprendizado de máquina para detecção de doença em radiografias.
Self-driving toy car built in C++ with raspberry pi, opencv, and tensorflow.
machine learning for design
A program made using DeepAI and python for smart refrigerator for HackWithInfy Hackathon
To give people an estimate of how much they need based on their individual health situation. After that, customers can work with any health insurance carrier and its plans and perks while keeping the projected cost from our study in mind. I am considering variables as age, sex, BMI, number of children, smoking habits and living region to predict.
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