Ready-to-run Docker images containing Jupyter applications
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Updated
Jun 11, 2024 - Python
The Jupyter Notebook, previously known as the IPython Notebook, is a language-agnostic HTML notebook application for Project Jupyter. Jupyter notebooks are documents that allow for creating and sharing live code, equations, visualizations, and narrative text together. People use them for data cleaning and transformation, numerical simulation, statistical modeling, data visualization, machine learning, and much more.
Ready-to-run Docker images containing Jupyter applications
A comprehensive list of Deep Learning / Artificial Intelligence and Machine Learning tutorials - rapidly expanding into areas of AI/Deep Learning / Machine Vision / NLP and industry specific areas such as Climate / Energy, Automotives, Retail, Pharma, Medicine, Healthcare, Policy, Ethics and more.
This project leverages machine learning to predict the quality of red wine based on various chemical properties. By analyzing key factors that influence wine quality, the model provides insights and predictions to assist in evaluating and selecting high-quality red wines.🍷
Projeto 3 do curso de Analista de Dados do Senac Rio em parceria com o programa "Programadores Cariocas no Mercado Segurador".
Loan Eligibility Prediction Model: A machine learning application to predict loan approval based on applicant data. Includes a web interface for submitting loan applications and receiving predictions. Built with Python and Jupyter Notebook.
This is a public repository of Jupyter notebooks with introductory tutorials on different aspects of Python programming. Please star us if you think it is useful:
Jupyter notebooks in the terminal
Restaurant Arabic Sentiment Analysis
Scipy Cookbook
Python IA: Inteligência Artificial e Previsões
Computational Methods Course at UdeA. Forked and size reduced from:
Proyecto de detección de estado de semáforos para ayudar a personas invidentes utilizando IA. Desarrollo de modelos de aprendizaje automático con una precisión de hasta el 95-97%. Repositorio con código, video y presentación disponible.
Kandinsky 2 — multilingual text2image latent diffusion model
This repository contains code for generating captions for images using a Transformer-based model. The model used is the `VisionEncoderDecoderModel` from the Hugging Face Transformers library, specifically the `nlpconnect/vit-gpt2-image-captioning` model.
Use @oItsMineZ So Vits SVC 4.0 models from Google Colab!
A tutorial for Kaggle's Titanic: Machine Learning from Disaster competition. Demonstrates basic data munging, analysis, and visualization techniques. Shows examples of supervised machine learning techniques.
CatBoost tutorials repository
Speech Emotion Recognition (SER) using Deep neural networks CNN and RNN
Non-Intrusive Load Monitoring Toolkit (nilmtk)
Pytest in IPython notebooks.
Created by Fernando Pérez, Brian Granger, and Min Ragan-Kelley
Released December 2011
Latest release 5 days ago