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π¬Python 3.14: What's New? π
β° In 2025, a wave of developer-focused improvements arrived with Python 3.14. Let's dive into some exciting new features:
β’ Lazy annotations resolve long-standing type hinting quirks
β’ Clever t-strings give more control over string interpolation
These enhancements solidify Python's position as the language of choice for AI-powered development.
π Stay up-to-date with the latest Python news and advancements in 2026! #Python #MachineLearning
β° In 2025, a wave of developer-focused improvements arrived with Python 3.14. Let's dive into some exciting new features:
β’ Lazy annotations resolve long-standing type hinting quirks
β’ Clever t-strings give more control over string interpolation
These enhancements solidify Python's position as the language of choice for AI-powered development.
π Stay up-to-date with the latest Python news and advancements in 2026! #Python #MachineLearning
β€1
Forwarded from Code With Python
Python Cheat sheet
#python #oop #interview #coding #programming #datastructures
https://t.me/DataScience4
#python #oop #interview #coding #programming #datastructures
https://t.me/DataScience4
β€3
If you're testing forms, mockups, or just want to play with data, there's Mimesis β a generator of fake data. Names, emails, addresses, and phone numbers. There's a location setting that allows you to select a country, and the data will be generated accordingly.
from typing import Dict
from mimesis.enums import Gender
from mimesis import Person
def generate_fake_user(locale: str = "es", gender: Gender = Gender.MALE) -> Dict[str, str]:
"""
Generates fake user data based on the locale and gender.
:param locale: The locale (for example, 'ru', 'en', 'es')
:param gender: The gender (Gender.MALE or Gender.FEMALE)
:return: A dictionary with the fake user data
"""
person = Person(locale)
user_data = {
"name": person.full_name(gender=gender),
"height": person.height(),
"phone": person.telephone(),
"occupation": person.occupation(),
}
return user_data
if __name__ == "__main__":
fake_user = generate_fake_user(locale="es", gender=Gender.MALE)
print(fake_user)
{
'name': 'Carlos Herrera',
'height': '1.84',
'phone': '912 475 289',
'occupation': 'Arquitecto'
)ru, πΊπΈ en, πͺπΈ es, etc.) Save it, it'll come in handy
#python #github #interview
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SPOTO gives you free, instant access to high-quality, updated resources that help you study smarter and pass exams faster.
β Latest Exam Materials:
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All materials are instantly downloadable
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Forwarded from Machine Learning with Python
A full-fledged educational course has been published on the university's website: 24 lectures, practical tasks, homework assignments, and a collection of materials for self-study.
The program includes modern neural network architectures, generative models, transformers, inference, and other key topics.
A great opportunity to study deep learning based on the structure of a top university, free of charge and without simplifications β let's learn here.
https://ocw.mit.edu/courses/6-7960-deep-learning-fall-2024/resources/lecture-videos/
tags: #python #deeplearning
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Forwarded from Machine Learning with Python
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We're sharing a cool resource for learning about neural networks, offering clear, step-by-step instruction with dynamic visualizations and easy-to-understand explanations.
In addition, you'll find many other useful materials on machine learning on the site.
Find and use it β https://mlu-explain.github.io/neural-networks/
tags: #AI #ML #PYTHON
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Forwarded from Machine Learning with Python
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Forwarded from Machine Learning with Python
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