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TimesFM (Time Series Foundation Model) is a pretrained time-series foundation model developed by Google Research for time-series forecasting. timesfm-1.0-200m is the first open model checkpoint: * It performs univariate time series forecasting for context lengths up to 512 timepoints and any horizon lengths, with an optional frequency indicator. * It focuses on point forecasts, and does not support probabilistic forecasts. We experimentally offer quantile heads but they have not been calibrated after pretraining. * It requires the context to be contiguous (i.e. no "holes"), and the context and the horizon to be of the same frequency. Follow @dataspoof to learn Python programming, data science and big data #datascience #machinelearning #ai #Python #python3 #sql #deeplearning #computervision #computerscience #programming #bigdata #chatgpt #deeplearningalgorithms #llm #timeseries #generativeai #ai4good
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Continuous Integration (CI): Developers commit code changes to a shared repository multiple times a day. Automated tests are triggered after each commit to ensure code quality. If tests fail, developers are notified to fix issues before merging changes. Continuous Deployment (CD): After passing CI, the code is automatically deployed to a staging environment for further testing. Automated tests, such as integration tests and user acceptance tests, are conducted in the staging environment. If all tests pass, the code is automatically deployed to production. Pipeline Automation: CI/CD tools like Jenkins, GitLab CI, or Travis CI are used to automate the pipeline. Configuration files (e.g., Jenkinsfile, .gitlab-ci.yml) define the steps of the pipeline. Version control systems (e.g., Git) track changes to code and pipeline configurations. Feedback Loop: Developers receive immediate feedback on code changes through automated tests and deployment status. Continuous monitoring tools track the performance of applications in production. Feedback from monitoring helps improve future iterations of the application and pipeline. Follow @dataspoof to learn Python programming, data science and big data #datascience #machinelearning #ai #Python #python3 #sql #deeplearning #computervision #computerscience #programming #bigdata #chatgpt #deeplearningalgorithms
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Clasificador de botellas de acuerdo a su condición (vacía o llena) utilizando Vision Artificial y Machine Learning. Se utilizaron algunas técnicas de preprocesado de datos para optimizar el gasto computacional en la Raspberry Pi. Esta aplicación también puede correr en otros procesadores con más recursos computacionales. #computervision #raspberrypi4 #machinelearningalgorithms #IA #pythonlearning #computerscience #inteligenciaartificial #visionartificial #pythonprogramming #pythondeveloper #artificialintelligence #machinelearning #python3 #esp32 #chatgpt #openai #arduino #arduinoprojects #dignal #dignalacademy
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