Convert complex regular expressions into readable Python code with Pregex

Templates like [a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,} look intimidating and are hard to read. It's challenging for a team without experience in regular expressions to understand and modify such validations.

Pregex converts regular expressions into clear Python code from denoscriptive components.

What you get:
• The code itself explains the intent, even without comments
• You can modify it without knowledge of regular expressions
• You can compose patterns for complex validation
• If necessary, you can export it back to a regular regex

The tool is open source. Installation: pip install pregex

Full article: https://bit.ly/3IWAE5O
Run this code: https://bit.ly/4hdQjKM

👉 @codeprogrammer

Create a perfect resume without messing with templates.

You write content in YAML and generate a PDF.

The project is called RenderCV, and it's open-source
https://github.com/rendercv/rendercv


👉 https://news.1rj.ru/str/DataScienceN

Checking the reliability of a password with Python!

Sometimes you need to quickly check how secure a password is. Let's look at a simple example using regular expressions - a good opportunity to practice with re and conditional logic.

Import the module:

import re

Create a password check function:

def check_password_strength(password):
    length = len(password) >= 8
    upper = re.search(r"[A-Z]", password)
    lower = re.search(r"[a-z]", password)
    digit = re.search(r"\d", password)
    special = re.search(r"[@$!%*?&]", password)

    if all([length, upper, lower, digit, special]):
        return "✅ Reliable password"
    else:
        return "⚠️ Weak password"

Check a few examples:

print(check_password_strength("Qwerty123"))
print(check_password_strength("Qw!8zYt@1"))

Output example:

⚠️ Weak password  
✅ Reliable password

🔥 Example of how to check a string for compliance with several conditions using code - and practice with regular expressions.

🚪 @DataScience4

Harvard has made its textbook on ML systems publicly available. It's extremely practical: not just about how to train models, but how to build production systems around them - what really matters.

The topics there are really top-notch:

> Building autograd, optimizers, attention, and mini-PyTorch from scratch to understand how the framework is structured internally. (This is really awesome)
> Basic things about DL: batches, computational accuracy, model architectures, and training
> Optimizing ML performance, hardware acceleration, benchmarking, and efficiency

So this isn't just an introductory course on ML, but a complete cycle from start to practical application. You can already read the book and view the code for free. For 2025, this is one of the strongest textbooks to have been released, so it's best not to miss out.

The repository is here, with a link to the book inside 👏

👉 @codeprogrammer

Awesome open-source project to learn more about Generative Adversarial Networks.

We found this interactive website that shows you visually how #GANs work.

GAN Lab Website: https://lnkd.in/eYV8QvrJ

https://news.1rj.ru/str/CodeProgrammer 🩷

📱 A collection of videos on PyTorch and neural networks

This is not a full-fledged course with a unified program, but a collection of nine separate videos on PyTorch and neural networks gathered in one playlist.

Inside, there are materials of different levels and formats that are suitable for selective study of topics, practice, and a general understanding of the direction.

What's here:

🏮 Introductory videos on PyTorch and the basics of neural networks;

🏮 Practical analyses with code writing and project examples;

🏮 Materials on computer vision and working with medical images;

🏮 Examples of creating chat bots and models on PyTorch;

🏮 Analyses of large language models and generative neural networks;

🏮 Examples of training agents and reinforcement tasks;

🏮 Videos from different authors without a general learning logic.

The collection is suitable for those who are already familiar with Python and want to selectively study PyTorch without a strict study plan — get it here.

https://www.youtube.com/playlist?list=PLp0BA-8NZ4bhBNWvUBPDztbzLar9Jcgd-


tags: #pytorch #DeepLearning #python

➡ @CodeProgrammer

🔖 40 NumPy methods that cover 95% of tasks

A convenient cheat sheet for those who work with data analysis and ML.

Here are collected the main functions for:

▶️ Creating and modifying arrays;
▶️ Mathematical operations;
▶️ Working with matrices and vectors;
▶️ Sorting and searching for values.

Save it for yourself — it will come in handy when working with NumPy.

tags: #NumPy #Python

➡ @DataScienceM

OnSpace Mobile App builder: Build AI Apps in minutes

Visit website: https://www.onspace.ai/?via=tg_datas
Or Download app:https://onspace.onelink.me/za8S/h1jb6sb9?c=datas

With OnSpace, you can build website or AI Mobile Apps by chatting with AI, and publish to PlayStore or AppStore.

What will you get:
✔️ Create app or website by chatting with AI;
✔️ Integrate with Any top AI power just by giving order (like Sora2, Nanobanan Pro & Gemini 3 Pro);
✔️ Download APK,AAB file, publish to AppStore.
✔️ Add payments and monetize like in-app-purchase and Stripe.
✔️ Functional login & signup.
✔️ Database + dashboard in minutes.
✔️ Full tutorial on YouTube and within 1 day customer service

ML engineers, this is for you: an interactive math tutorial for machine learning

Recently, they posted several more blogs on the basics of mathematical analysis for machine learning, with interactive simulations.

Among the topics:

- backprop and gradient descent
- local minima and saddle points
- vector fields
- Taylor series
- Jacobian and Hessian
- partial derivatives

The material is specifically focused on the ML context, with an emphasis on clarity and practical understanding. ✌️

Let's practice here

👉 @codeprogrammer

For beginners: a free online course on Python programming

On the site, you can run code directly in the browser, solve problems, and learn the basics of the language step by step

Start your improvement 👍

👉 @codeprogrammer