Big news: the first-ever ✨ FastAPI Conf ✨ is coming!

🇳🇱 Netherlands, Amsterdam (жаль не в Казахстане 😅)
📅 October 28

Meet the FastAPI team, connect with developers ☕️

🎟️ Join the waitlist for early ticket access: fastapiconf.com

👀 DeepML Computer Vision practice problems: https://www.deep-ml.com/problems?category=Computer+Vision

+ The essentials of deep learning with PyTorch: https://www.deep-ml.com/collections/PyTorch%20Basics

DeepML #242: Non-Maximum Suppression for Object Detection 👀

Non-Maximum Suppression (NMS) is a crucial post-processing technique in computer vision, notably in object detection (e.g., YOLO, SSD), designed to eliminate redundant, overlapping bounding boxes for a single object. It works by selecting the highest-confidence prediction and suppressing others that overlap significantly

Problem for practice NMS (DeepML #242): https://www.deep-ml.com/problems/242

NMS implementation without Numpy (clean python): https://gist.github.com/silvermete0r/b4396efdeb48e5df639a5e69a0870b95

NMS vectorized implementation with Numpy: https://gist.github.com/silvermete0r/0ae1ed33557ee44e6aad3a2fbf2ec237

Image credits: learnopencv

3910. Count Connected Subgraphs with Even Node Sum | LC Hard

Topics: graphs, bitmasks, dfs, bfs

Problem link: https://leetcode.com/problems/count-connected-subgraphs-with-even-node-sum/

#LeetCode #Daily

🐱‍💻 Kindly reminder!

KBTU Open 2026 Spring Registration Deadline is May 8, 2026!

The contest will take place on May 10, 2026.

P.S. https://kbtuopen.com/team

👀 Прикольно, оказывается на платформе NeetCode есть секция ML: https://neetcode.io/practice/machine-learning (тут всякие ML задачки)
+ есть блок для написания архитектуры GPT (там attention и т.д. дела) -> https://neetcode.io/practice/machine-learning/project

Pursuing passion in your career leads to fulfillment and success ✅

Bayan Sulu Hackathon 💛

Bayan Sulu Hackathon 2026 - двухэтапный хакатон, направленный на разработку и презентацию инновационных решений.

📍 Локация: Астана (офлайн-финал)
📆 Онлайн этап: 28 апреля - 6 мая
📆 Финал: 9-10 мая
💰 Общий призовой фонд: 1 000 000 KZT

Призовые места:
• 1 место - 500 000 KZT
• 2 место - 300 000 KZT
• 3 место - 200 000 KZT

Формат проведения:
• Онлайн этап- регистрация, подача заявок и отбор участников
• Офлайн финал - 24-часовой хакатон и защита проектов в Астане

Условия участия:
• Команды от 2 до 5 человек
• Обязательное участие в офлайн-финале для прошедших отбор
• Открыто для участников, заинтересованных в разработке и реализации проектных решений

Хакатон проводится при поддержке Bayan Sulu, Adai Media и Syndicate Association.

🔗 Регистрация: https://docs.google.com/forms/d/e/1FAIpQLSeNgLzpH0xq91sD5yEb9euJrU03gbmp0cnI_kVRXJXdGV7yqg/viewform
📩 Дополнительная информация на нашем инстаграм аккаунте: @syndicate.association

5-Day AI Agents: Intensive Vibe Coding Course With Google (June 15 - 19, 2026): https://www.kaggle.com/competitions/5-day-ai-agents-intensive-vibecoding-course-with-google

~ Hosted by Google 🤗

Recommend Key Skills and Knowledge:
* Python Programming: The course includes hands-on coding exercises, so prior experience with Python is highly recommended.
* Basic AI Concepts: A foundational understanding of AI concepts will help you follow along more easily. If you’re new to these topics, we recommend starting with the 5-Day AI Agent Intensive Course with Google.
* Google AI Studio: Familiarity with Google AI Studio will help you experiment with and deploy AI models effectively. The API & Google AI Studio is available in these locations: https://ai.google.dev/gemini-api/docs/available-regions.

Зарегистрировался, повайбкодим летом 😁

+1 beautiful representation of the Euler's number.

Bayes' Theorem is a mathematical formula for determining conditional probability, allowing you to update the probability of a hypothesis (an event) based on new evidence.

Core Applications:
* Naive Bayes Classifier: A popular, fast, and scalable algorithm assuming feature independence, often used for text classification, such as spam detection.
* Bayesian Inference: Systematically updating beliefs and quantifying uncertainty in model parameters.
* Bayesian Networks: Probabilistic graphical models that represent conditional dependencies.

DeepML Problem (#336): https://www.deep-ml.com/problems/336

Bayes' Theorem, Clearly Explained by StatQuest (youtube): https://youtu.be/9wCnvr7Xw4E?si=WUCiRH6x6ccVGQHJ