Machine Learning with Python – Telegram
Machine Learning with Python
68.8K subscribers
1.34K photos
109 videos
175 files
1.01K links
Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers.

Admin: @HusseinSheikho || @Hussein_Sheikho
Download Telegram
🏆 Unlock Data Analysis: 150 Tips, Practical Code

📢 Unlock data analysis mastery! Explore 150 essential tips, each with clear explanations and practical code examples to boost your skills.

⚡️ Tap to unlock the complete answer and gain instant insight.

━━━━━━━━━━━━━━━
By: @CodeProgrammer 💛
Please open Telegram to view this post
VIEW IN TELEGRAM
4👍2👏2
This media is not supported in your browser
VIEW IN TELEGRAM
This combination is perhaps as low as we can get to explain how the Transformer works

#Transformers #LLM #AI

https://news.1rj.ru/str/CodeProgrammer 👍
2🔥1
python-interview-questions.pdf
1.2 MB
100 Python Interview Questions and Answers

This book is a practical guide to mastering Python interview preparation. It contains 100 carefully curated questions with clear, concise answers designed in a quick-reference style.

#Python #PythonTips #PythonProgramming

https://news.1rj.ru/str/CodeProgrammer
7🔥2
Python Interview Codes Cheatsheet
7
🏆 Python NumPy Tips

📢 Unlock the power of NumPy! Get essential Python tips for creating and manipulating arrays effectively for data analysis and scientific computing.

Tap to unlock the complete answer and gain instant insight.

━━━━━━━━━━━━━━━
By: @CodeProgrammer
8👏2🎉2👍1
🤖🧠 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
5
🏆 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.

━━━━━━━━━━━━━━━
By: @CodeProgrammer
8🎉1
Media is too big
VIEW IN TELEGRAM
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 🩵
Please open Telegram to view this post
VIEW IN TELEGRAM
9
This media is not supported in your browser
VIEW IN TELEGRAM
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
Please open Telegram to view this post
VIEW IN TELEGRAM
13👍2🎉1
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

━━━━━━━━━━━━━━━
By: @CodeProgrammer
7🎉1
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
Please open Telegram to view this post
VIEW IN TELEGRAM
Please open Telegram to view this post
VIEW IN TELEGRAM
11👍3🔥2🏆1
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
5
The difference between import os and from os import *
2🐳119👍5👾3
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
18
🚀 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. 👇

https://news.1rj.ru/str/+AOPQVJRWlJc5ZGRi
https://news.1rj.ru/str/+AOPQVJRWlJc5ZGRi
https://news.1rj.ru/str/+AOPQVJRWlJc5ZGRi
16👎3👍1💯1
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 🔰

More Likes Please 🖕
Please open Telegram to view this post
VIEW IN TELEGRAM
10👍5
🚀 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. 👇

https://news.1rj.ru/str/+AOPQVJRWlJc5ZGRi
https://news.1rj.ru/str/+AOPQVJRWlJc5ZGRi
https://news.1rj.ru/str/+AOPQVJRWlJc5ZGRi
🎉32