Machine Learning with Python – Telegram
Machine Learning with Python
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Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers.

Admin: @HusseinSheikho || @Hussein_Sheikho
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🤖🧠 The Transformer Architecture: How Attention Revolutionized Deep Learning

🗓️ 11 Nov 2025
📚 AI News & Trends

The field of artificial intelligence has witnessed a remarkable evolution and at the heart of this transformation lies the Transformer architecture. Introduced by Vaswani et al. in 2017, the paper “Attention Is All You Need” redefined the foundations of natural language processing (NLP) and sequence modeling. Unlike its predecessors – recurrent and convolutional neural networks, ...

#TransformerArchitecture #AttentionMechanism #DeepLearning #NaturalLanguageProcessing #NLP #AIResearch
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🏆 Streamline Your Email Sending

📢 Effortlessly send emails to large audiences! This guide unlocks the power of email automation for your information and promotional campaigns.

Tap to unlock the complete answer and gain instant insight.

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By: @CodeProgrammer
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The easiest way to write documentation for code

The open Davia project allows you to generate neat internal documentation with visual diagrams for any code.

Just install it on your system, follow the steps in their documentation, run the command in the project folder, and voila, it will generate complete documentation with structured visuals that you can view and edit 💯

👉 https://github.com/davialabs/davia

https://news.1rj.ru/str/CodeProgrammer 🩵
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Brought an awesome repo for those who love learning from real examples. It contains over a hundred open-source clones of popular services: from Airbnb to YouTube

Each project is provided with links to the source code, demos, stack denoscription, and the number of stars on GitHub. Some even have tutorials on how to create them

Grab it on GitHub 🍯: https://github.com/gorvgoyl/clone-wars

👉 https://news.1rj.ru/str/CodeProgrammer
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Tip for clean code in Python:

Use Dataclasses for classes that primarily store data. The @dataclass decorator automatically generates special methods like __init__(), __repr__(), and __eq__(), reducing boilerplate code and making your intent clearer.

from dataclasses import dataclass

# --- BEFORE: Using a standard class ---
# A lot of boilerplate code is needed for basic functionality.

class ProductOld:
def __init__(self, name: str, price: float, sku: str):
self.name = name
self.price = price
self.sku = sku

def __repr__(self):
return f"ProductOld(name='{self.name}', price={self.price}, sku='{self.sku}')"

def __eq__(self, other):
if not isinstance(other, ProductOld):
return NotImplemented
return (self.name, self.price, self.sku) == (other.name, other.price, other.sku)

# Example Usage
product_a = ProductOld("Laptop", 1200.00, "LP-123")
product_b = ProductOld("Laptop", 1200.00, "LP-123")

print(product_a) # Output: ProductOld(name='Laptop', price=1200.0, sku='LP-123')
print(product_a == product_b) # Output: True


# --- AFTER: Using a dataclass ---
# The code is concise, readable, and less error-prone.

@dataclass(frozen=True) # frozen=True makes instances immutable
class Product:
name: str
price: float
sku: str

# Example Usage
product_c = Product("Laptop", 1200.00, "LP-123")
product_d = Product("Laptop", 1200.00, "LP-123")

print(product_c) # Output: Product(name='Laptop', price=1200.0, sku='LP-123')
print(product_c == product_d) # Output: True


#Python #CleanCode #ProgrammingTips #SoftwareDevelopment #Dataclasses #CodeQuality

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By: @CodeProgrammer
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Stochastic and deterministic sampling methods in diffusion models produce noticeably different trajectories, but ultimately both reach the same goal.

Diffusion Explorer allows you to visually compare different sampling methods and training objectives of diffusion models by creating visualizations like the one in the 2 videos.

Additionally, you can, for example, train a model on your own dataset and observe how it gradually converges to a sample from the correct distribution.

Check out this GitHub repository:
https://github.com/helblazer811/Diffusion-Explorer

👉 https://news.1rj.ru/str/CodeProgrammer
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Forwarded from Machine Learning
📌 PyTorch Tutorial for Beginners: Build a Multiple Regression Model from Scratch

🗂 Category: DEEP LEARNING

🕒 Date: 2025-11-19 | ⏱️ Read time: 14 min read

Dive into PyTorch with this hands-on tutorial for beginners. Learn to build a multiple regression model from the ground up using a 3-layer neural network. This guide provides a practical, step-by-step approach to machine learning with PyTorch, ideal for those new to the framework.

#PyTorch #MachineLearning #NeuralNetwork #Regression #Python
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The difference between import os and from os import *
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Comprehensive Python Cheatsheet.pdf
6.3 MB
Comprehensive Python Cheatsheet

This Comprehensive #Python Cheatsheet brings together core syntax, data structures, functions, #OOP, decorators, regular expressions, libraries, and more — neatly organized for quick reference and deep understanding.

https://news.1rj.ru/str/CodeProgrammer
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🚀 THE 7-DAY PROFIT CHALLENGE! 🚀

Can you turn $100 into $5,000 in just 7 days?
Lisa can. And she’s challenging YOU to do the same. 👇

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Data Science Formulas Cheat Sheet.pdf
175.4 KB
🏷 Data Science Formulas Cheat Sheet
Application of Each Formula

👨🏻‍💻 This cheat sheet presents important data science concepts along with their formulas.

From key topics in statistics to machine learning and NLP.

And the main formulas that are always needed + real examples for each formula, showing you when and why to use each method.

🌐 #Data_Science #DataScience

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

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🚀 THE 7-DAY PROFIT CHALLENGE! 🚀

Can you turn $100 into $5,000 in just 7 days?
Lisa can. And she’s challenging YOU to do the same. 👇

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This GitHub repo is a gold mine for EVERY data scientist!

(full of hands-on and interactive tutorials)

DS Interactive Python provides several dashboards to interactively learn about statistics, ML models, and other related theoretical concepts!

Some key topics you can understand interactively:
• PCA
• Bagging and boosting
• Linear regression and OLS
• Bayesian and frequentist statistics
• confidence intervals
• clustering (kmeans, spectral clustering, etc.)
• central limit theorem
• neural networks (the backpropagation animation is really good)
• and many more.

Link: https://github.com/GeostatsGuy/DataScienceInteractivePython

https://news.1rj.ru/str/CodeProgrammer
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Create a Wi-Fi QR code in Python in a couple of seconds

pip install wifi_qrcode_generator


import wifi_qrcode_generator.generator
from PIL import Image

ssid = "CLCoding_WIFI"
password = "supersecret123"
security = "WPA"

from wifi_qrcode_generator.generator import wifi_qrcode
qr = wifi_qrcode(ssid, False, security, password)

qr.make_image().save("wifi_qr.png")
Image.open("wifi_qr.png")


👉  @codeprogrammer
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Amazing NumPy Cheat Sheet.pdf
259.7 KB
Amazing NumPy Cheat Sheet Snippet with 100 exercises for practicing the concept to get hands on to clear the coding round in the interviews

#NumPy #codingexams

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