Learn Python Coding

✨ Quiz: Exploring Protocols in Python ✨ 📖 Test your knowledge of Python protocols, structural subtyping, and static type checking, including generic and subprotocol usage. 🏷️ #intermediate #python

✨ curses | Python Standard Library ✨ 📖 Provides tools for building text-based user interfaces in Unix terminals, providing control over cursor position, colors, windows, and keyboard input. 🏷️ #Python

This channels is for Programmers, Coders, Software Engineers. 0️⃣ Python 1️⃣ Data Science 2️⃣ Machine Learning 3️⃣ Data Visualization 4️⃣ Artificial Intelligence 5️⃣ Data Analysis 6️⃣ Statistics 7️⃣ Deep Learning 8️⃣ programming Languages ✅ https://t.me/addlist/8_rRW2scgfRhOTc0 ✅ https://t.me/Codeprogrammer

✨ Quiz: Reading Input and Writing Output in Python ✨ 📖 Test your Python basics: read user input with input(), display output with print(), and handle numeric conversions. 🏷️ #basics #python

✨ ctypes | Python Standard Library ✨ 📖 Allows for calling functions in shared C libraries and DLLs using C-compatible data types. 🏷️ #Python

✨ Dictionaries in Python ✨ 📖 Learn how dictionaries in Python work: create and modify key-value pairs using dict literals, the dict() constructor, built-in methods, and operators. 🏷️ #basics #python

✨ Quiz: Implementing the Factory Method Pattern in Python ✨ 📖 Learn how the Factory Method pattern separates creation from use, promotes decoupling, and organizes Python products efficiently. 🏷️ #intermediate #best-practices

🚀 Sber has released two open-source MoE models: GigaChat-3.1 Ultra and Lightning Both code and weights are available under the MIT license on HuggingFace. 👉 Key details: • Trained from scratch (not a finetune) on proprietary data and infrastructure • Mixture-of-Experts (MoE) architecture Models: 🧠 GigaChat-3.1 Ultra • 702B MoE model for high-performance environments • Outperforms DeepSeek-V3-0324 and Qwen3-235B on math and reasoning benchmarks • Supports FP8 training and MTP ⚡️ GigaChat-3.1 Lightning • 10B model (1.8B active parameters) • Outperforms Qwen3-4B and Gemma-3-4B on Sber benchmarks • Efficient local inference • Up to 256k context Engineering highlights: • Custom metric to detect and reduce generation loops • DPO training moved to native FP8 • Improvements in post-training pipeline • Identified and fixed a critical issue affecting evaluation quality 🌍 Trained on 14 languages (optimized for English and Russian) Use cases: • chatbots • AI assistants • copilots • internal ML systems Sber provides a solid open foundation for developers to build production-ready AI systems with lower infrastructure costs.

✨ Quiz: Using Loguru to Simplify Python Logging ✨ 📖 Test your knowledge of Loguru for Python logging, from zero-config setup and log levels to custom formats and adding context. 🏷️ #intermediate #tools

✨ Quiz: Building a Python GUI Application With Tkinter ✨ 📖 Test your Tkinter skills with this interactive challenge on widgets, layouts, event loops, and text/file handling in Python GUIs. 🏷️ #basics #gui #stdlib

✨ copyreg | Python Standard Library ✨ 📖 Provides a registry for defining custom pickling and copying functions for specific object types. 🏷️ #Python

✨ Quiz: For Loops in Python (Definite Iteration) ✨ 📖 Test your understanding of Python loops, iterables, and control flow with break, continue, and StopIteration. 🏷️ #basics #python

✨ compileall | Python Standard Library ✨ 📖 Provides utilities for byte-compiling Python source files into bytecode across a directory tree, a single file, or all locations on sys.path. 🏷️ #Python

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Follow the Machine Learning with Python channel on WhatsApp: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

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