Simone G.

Tables Views #ibm #db2 #table #sql #view #cobol

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Hello, i'm Matheus, and I am studying Analysis and Systems Development. This is my academic portfolio, where I showcase my knowledge in Python programming. The purpose of this portfolio is to demonstrate my learning progress with the Python programming language. I included a few basic exercises to show my skills, but the main highlight is a professional project that I plan to improve further. For now, it's only a draft, but the results have been very positive. The name of the project is "Student System." I developed everything in Python and used SQLite to create the database. This system allows for student registration, displays a list with all students, and shows the total number of students, along with information like course, age, and the ability to delete entries. It's a simple, yet effective, and easy-to-understand database system.

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On my way to getting the DataCamp SQL Associate and Associate Data Analyst Certification! - #Datacamp #SQL #SoftwareEngineering #DataAnalysis

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Successfully completed the fifth module of the Full-Stack Development training at Trybe. During this period, in a 170-hour workload, deepened and applied practical content such as: Unit Testing with Pytest, Debugging in VS Code, Object-Oriented Programming with structural and nominal subtyping, Web Scraping with BeautifulSoup and Selenium, REST APIs, Flask, Django, Django REST Framework, MongoDB, SSR, Docker, and Deployment. Additionally, completed 100 hours of Career review content, including Front-end and Back-end revisions and a full-stack challenge. Ready for the next challenge! #FullStackDeveloper #Python #Django #Flask #Trybe

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🚀 Esta é uma pergunta que frequentemente surge ao implementar APIs com bancos de dados, usar ORMs de terceiros, criar nossos próprios ORMs, criar consultas SQL... Neste repositório, iniciei um estudo sobre o desempenho de acesso a bancos de dados em Golang! Comparamos três abordagens comuns: consulta SQL única, CRUD DAO e GORM ORM. Todas testados com PostgreSQL. Os resultados destacam diferenças significativas em eficiência e uso de recursos. Vale ressaltar que, em todos os casos, devemos nos preocupar com a segurança. 🐹💻 🔍 Destaques da comparação: - Consulta SQL Única: Mais eficiente em tempo de execução e uso de memória. - CRUD DAO: Uso moderado de memória, mas mais lento. - GORM: Mais fácil para desenvolvimento, mas maior tempo de execução e consumo de recursos. A documentação está disponível em português e inglês. 📊 Confira os resultados e o projeto no GitHub: https://lnkd.in/dhti4bXW Há muito mais testes a serem feitos. Qualquer feedback ou sugestão são bem vindos! #Golang #DatabasePerformance #PostgreSQL #SQL #GORM #SoftwareDevelopment #Benchmarks #DataScience #Coding #TechResearch #Docker #Python #ORM #CRUD

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Currently, I did a project using Java and React. This project is simply, only to realize a math operation or generate a random string. The ideia is show how I built the backend and front-end, with JWT authentication, logs and project architecture, in addition, create a docker image to boths. The repository from backend: https://lnkd.in/dmjNvPK4 The repository from front-end: https://lnkd.in/dBtPSpqH To facilitate, I did the deploy in AWS and I'll let there for some days. You can use the project access this link: http://54.236.117.98:3000/ There are 10 users with username: [email protected] [email protected] [email protected] ... [email protected] All with the password = "password"

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32 comentários

Excellent course about MultiAgents with crewAI, given by @joaomdmoura and DeepLearning.AI

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Algumas referências que podem ser úteis... ;-) 1. A comprehensive survey of deep learning‑based lightweight object detection models for edge devices 2. Few-shot learning based on deep learning: A survey 3. Deep Lifelong Learning with Factorized Knowledge Transfer 4. The Role of Reinforcement Learning in Dynamic Cyber Defense Strategies 5. Privacy preservation in deep reinforcement learning: A training perspective 6. Interpretability of deep neural networks: A review of methods, classification and hardware 7. A Survey on Deep Active Learning: Recent Advances and New Frontiers 8. Overview of Data Quality: Examining the Dimensions, Antecedents, and Impacts of Data Quality

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