Dasturchilar uchun Google tomonidan Code Jam onlayn musobaqasi. Tanlov g'oliblariga pul mukofotlari topshiriladi
Talablar
— Tanlovda 18 yoshdan katta bo'lgan dasturchilik sohasiga qiziquvchi yoshlar qatnashishlari mumkin;
— Dasturchilarning Google accountlarida o'z ism-shariflari, telefon nomerlari va qaysi davlatda yashashlari aniq va batafsil keltirib o'tishlari so'raladi;
— Dastur ishchi tili ingliz tili ekanligi uchun shu tildan xabardor bo'lishi kerak (sertifikat shartmas).
Foydali tomonlari
— 1-raunddan 2-raundga o'tgan eng yaxshi 1000 ta dasturchi ichiga kirgan nomzodlarga Code Jam futbolkalari beriladi;
— Code Jam musobaqasida oxirgi 5-bosqichiga yetib kelgan ishtirokchilar quyidagi miqdordagi pul mukofotlari bilan taqdirlanadilar:
— 1-o'rin - $15 000;
— 2-oʻrin — $2000;
— 3-oʻrin — $1000;
— 4-25-oʻrin — $100.
Oxirgi muddat
03.04.2022 23:59
Batafsil
https://grantgo.uz/go/56580
#tanlovlar#mukofot#AQSh
Совсем лайтовая статья для новичков "10 главных конструкций языка R".
Содержание:
- Комментарии
- Переменные и векторы
- Внешние модули
- Ввод и вывод
- Присваивание и сравнение
- Условный оператор if
- Цикл for
- Функции
- Классы, методы и объекты
#статьи
#easy
#Easy#Credit#T#i#ch#nh#s
Join the Easy Credit - Tài chính số beta on ✈️#TestFlight
🔗 Link: https://testflight.apple.com/join/B8XYxOWV
Shared by Dimitri
#python#deepseek#demo#easy#embedding#flask#gpt#huggingface_transformers#llm#mcp#multimodal#openai#qwen#rag#sentence_transformers#ui#vllm#vlm
UltraRAG is a lightweight framework that makes building retrieval-augmented generation (RAG) systems simple and fast. It uses a low-code approach where you write just dozens of lines of YAML configuration instead of complex code to create sophisticated AI workflows with conditional logic and loops. The framework includes a visual development environment where you can drag-and-drop to build pipelines, adjust parameters in real-time, and instantly convert your logic into interactive chat applications. This means you can deploy powerful AI systems that ground answers in your own data—reducing hallucinations and improving accuracy—without needing extensive coding expertise or lengthy development cycles.
https://github.com/OpenBMB/UltraRAG