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Source channel @olddriverGDstudy · Post #10 · Mar 17

#语录 请大家做个素质狼友: 1 人和人需要的是相互尊重的,希望我们群的狼友能尊重老师。在相互尊重的情况下我相信大家会得到更好的体验。 2 请大家预约老师后如有变化应该尽快,提前的告知老师,因为老师每天的课时都是有限的。如果不提前告知也很可能再也约不到这位老师或者进入妹子们的黑名单。 3 请大家遵守行规(按照行规S了但是可以待够时间,享受下老师的服务和老师聊聊天。就算时间到了没S也算是课时结束了,如果第一次结束了又做第二次那么不管S没有都应该按PP付费。),一般情况下P是60分钟 PP是90分钟 时间没到老师赶你走是老师的问题,但是超时就是狼友的问题,关于超时最好和老师协商一下,因为老师如果后面有学生,那么超时就会影响到后面的学生,很可能会给老师带来不必要的麻烦。如果想约PP的学生最好在预约的时候就给老师讲清楚。 4 关于等候的时间,有些时候有很多不可控因素比如学生迟到,学生学习时间长等因素,希望大家在等候的时候能稍微耐心点,个人感觉等候时间在20-30分钟还是可接受的。 5 希望我们群的兄弟都能做个素质狼友,当然我们也会对群里的各位老师有所要求,大家对老师有什么不满意的都可以在群里直接投诉,或者找管理员投诉。

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404 KIDS SEE GHOSTS (生产力之王版

@Isaiahsystem · Post #1520 · 05/11/2026, 09:59 AM

生产力全自动化技巧·贰 学会场景模型下沉。这要求给自己所有任务和模型能力对应分层,自动化场景模型要学会下沉,探索部分上升,最优化模型帕累托边界。它意味着要摸清模型能力边界,了解自己任务程度,对应配置。 现在我发现我沉淀下来的日常/工作全自动化场景,Codex 5.5 Medium/ Claude Sonnet 4.6 完全足够(国产模型也能兜底),速度快、成效好。过去我刻意耗费了很多 Tokens,Codex 5.5 xhigh Fast/ Opus 4.7 乱跑,现在已经逐步将场景模型转向收敛。 模型能力调用配置算个人认知、执行等综合行为能力投射。耗费是必要的,这是边界探索、能力投射、纯粹享受的过程。但某些任务思考过度是种数字毒素,对任务质量并不好,并非最优。 相关链接: 1. 生产力全自动化小技巧·壹 2. 没有护城河丨LLMs 帕累托前沿实时更新 #models

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Tesla News 🅥

@teslacn · Post #1044 · 04/22/2022, 03:05 PM

🗞️Tesla Model S Plaid racecar bares its fangs by overtaking hypercars in Laguna Seca 特斯拉Model S格子赛车在拉古纳赛卡赛道上超越超跑 📢频道💭群组🏷#ModelS

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Car News

@car_news · Post #570 · 09/26/2024, 01:01 PM

Amalgam company has released a miniature copy of 🚘12Cilindri for the price of Nissan Versa 🏎 The copy of Ferrari 12Cilindri is made in 1:8 scale (length about 60 centimeters). Amalgam designers achieve a high similarity with the original by using the original documentation and the help of the manufacturer's engineers. 📸 The attention to detail is astounding. The miniature leather interior is decorated with red contrast stitching, just like the full-size car. The level of accuracy is so high that the model can be mistaken for the real thing in the photos. ⏳ Each car takes about 300 hours to assemble by hand. In total, Amalgam will produce 119 coupe models and the same number of roadsters. 👍 As for the real Ferrari 12Cilindri, this car replaces the 812 Superfast, continuing the tradition of V12-powered grand tourers. 💸 The price of the model starts at $18,220. @CarsNews #Ferrari#sportscar#models

🌿 Xalqaro amaliy seminar: “Yashil” kelajak sari modellar va nazarlar “Yangi O‘zbekiston” universiteti HTWD Dresden (Germaniya), Myunxen texnika universiteti (Germaniya) va Nemis-Iordan (Iordaniya)universitetlaribilan hamkorlikda ikki kunlik xalqaro seminar tashkil etmoqda. Seminar-treyningda O‘zsanoatqurilishbank, Osiyo taraqqiyot banki va IT Park dan mutaxassislar va ekspertlar ishtirok etishadi. 📅 Sana: 2025-yil 5–6-noyabr 📍 Manzil: “Yangi O‘zbekiston” universiteti Seminar dasturi 5-noyabr kuni asosiy ma’ruzalar va panel muhokamasidan boshlanadi: ❕“Yashil biznes modellari va mas’uliyatli innovatsiyalar: global va mahalliy istiqbollar”. 6-noyabr kuni TUM International amaliy seminar o‘tkazadi: 💡“Ta’limdan harakatga: barqarorlik va tadbirkorlik ta’siri uchun ta’lim berish”. ‼️Ro‘yxatdan o‘tish 4-noyabrgacha davom etadi. 👉Seminarga ro‘yxatdan o‘tish 🔁English ➖➖➖➖➖➖➖➖➖➖ #workshop#greenfuture#models ⬇️Biz ijtimoiy tarmoqlarda: Veb-sayt | Telegram | Instagram | Facebook

djangoproject

@djangoproject · Post #193 · 10/31/2016, 12:33 PM

http://deeplearning.net/software/theano/tutorial/index.html#basics In terms of #algorithm availability, you can get plenty of algorithms out of the box with scikit-learn. And if you want to customize every detail of your #models, #Python has #Theano. In addition, Theano is easily configured to run on the #GPU, which gives you a cheap and easy way to get much higher speeds without having to change a single line of code or delve into performance details.

Data Science Jobs

@datasciencejobs · Post #2093 · 05/16/2024, 05:03 PM

#вакансия#middle#senior#python#dev#postgresql#ml#math#models#sql Всем привет! Мы Axenix (ex. Accenture) находимся в поисках Python Developer на проект разработки продукта In.Plan (платформа интегрированного планирования) 🔎 Мы ищем Middle/Senior Python Developer Формат работы: гибрид, удаленка (РФ/Армения/Казахстан) 💰 обсуждается после тех. интервью Чем предстоит заниматься: 🔸 Разработка решений для платформы интегрированного планирования производства 🔸 Участие в написании модулей прогнозирования спроса, календарного планирования и производственного планирования, модуля оптимизации складских запасов 🔸 Работа с PostgreSQL как источником данных. Задачи включают написание компонентов по чтению/записи данных из БД, обработке (очистка, проверка качества, сглаживание и т.д.) данных, формулировке задач мат. оптимизации для солверов, написание ML модулей для прогнозирования. Здорово, если ты: 🔹 Знаешь алгоритмы и структуры данных 🔹 Имеешь опыт коммерческой разработки на Python 🔹 Понимаешь и применял нотации Big O для оценки сложности алгоритмов 🔹 Знаешь паттерны ООП и принципы проектирования вычислительных решений 🔹 Имеешь опыт написания тестов, понимание отличий и целей разных типов тестов 🔹 Имеешь опыт работы с SQL базами данных 🔹 Знаешь Git Мы предлагаем: ▪️ Работу в Аккредитованной ИТ-компании ▪️ Конкурентоспособный уровень дохода, годовые бонусы и регулярное повышение по результатам Performance Review ▪️ Культуру непрерывного обучения: сертификация, online и offline обучение в России, менторство в профессиональном развитии; ▪️ ДМС с первого месяца работы, включая стоматологию, в лучших клиниках Москвы и МО для cотрудника и его семьи (жена/муж, дети до 18 лет); ▪️ Возможность оформления по ИП Контакт для связи: @masha_axenix

Data Analytics

@sqlspecialist · Post #1644 · 05/23/2025, 06:46 PM

✨The STAR method is a powerful technique used to answer behavioral interview questions effectively. It helps structure responses by focusing on Situation, Task, Action, and Result. For analytics professionals, using the STAR method ensures that you demonstrate your problem-solving abilities, technical skills, and business acumen in a clear and concise way. Here’s how the STAR method works, tailored for an analytics interview: 📍 1. Situation Describe the context or challenge you faced. For analysts, this might be related to data challenges, business processes, or system inefficiencies. Be specific about the setting, whether it was a project, a recurring task, or a special initiative. Example: “At my previous role as a data analyst at XYZ Company, we were experiencing a high churn rate among our subscription customers. This was a critical issue because it directly impacted revenue.”* 📍 2. Task Explain the responsibilities you had or the goals you needed to achieve in that situation. In analytics, this usually revolves around diagnosing the problem, designing experiments, or conducting data analysis. Example: “I was tasked with identifying the factors contributing to customer churn and providing actionable insights to the marketing team to help them improve retention.”* 📍 3. Action Detail the specific actions you took to address the problem. Be sure to mention any tools, software, or methodologies you used (e.g., SQL, Python, data #visualization tools, #statistical#models). This is your opportunity to showcase your technical expertise and approach to problem-solving. Example: “I collected and analyzed customer data using #SQL to extract key trends. I then used #Python for data cleaning and statistical analysis, focusing on engagement metrics, product usage patterns, and customer feedback. I also collaborated with the marketing and product teams to understand business priorities.”* 📍 4. Result Highlight the outcome of your actions, especially any measurable impact. Quantify your results if possible, as this demonstrates your effectiveness as an analyst. Show how your analysis directly influenced business decisions or outcomes. Example: “As a result of my analysis, we discovered that customers were disengaging due to a lack of certain product features. My insights led to a targeted marketing campaign and product improvements, reducing churn by 15% over the next quarter.”* Example STAR Answer for an Analytics Interview Question: Question: *"Tell me about a time you used data to solve a business problem."* Answer (STAR format): 🔻*S*: “At my previous company, our sales team was struggling with inconsistent performance, and management wasn’t sure which factors were driving the variance.” 🔻*T*: “I was assigned the task of conducting a detailed analysis to identify key drivers of sales performance and propose data-driven recommendations.” 🔻*A*: “I began by collecting sales data over the past year and segmented it by region, product line, and sales representative. I then used Python for #statistical#analysis and developed a regression model to determine the key factors influencing sales outcomes. I also visualized the data using #Tableau to present the findings to non-technical stakeholders.” 🔻*R*: “The analysis revealed that product mix and regional seasonality were significant contributors to the variability. Based on my findings, the company adjusted their sales strategy, leading to a 20% increase in sales efficiency in the next quarter.” Hope this helps you 😊