Machine Learning with Python
68K subscribers
1.27K photos
94 videos
159 files
924 links
Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers.
Download Telegram
πŸ€–πŸ§  Bytebot: The Future of AI Desktop Automation

πŸ—“οΈ 12 Nov 2025
πŸ“š AI News & Trends

In the era of rapid digital transformation, automation is the driving force behind business efficiency and innovation. While most AI agents are limited to browsers or APIs, a groundbreaking open-source project called Bytebot has redefined what AI can achieve. Bytebot introduces a self-hosted AI desktop agent β€” a virtual computer that performs complex, multi-step tasks ...

#Bytebot #AIDesktopAutomation #SelfHostedAI #OpenSourceAI #AIAgents #TaskAutomation
❀4
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 πŸ’―

πŸ‘‰ https://github.com/davialabs/davia

https://t.iss.one/CodeProgrammer 🩡
Please open Telegram to view this post
VIEW IN TELEGRAM
❀8
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 description, 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

πŸ‘‰ https://t.iss.one/CodeProgrammer
Please open Telegram to view this post
VIEW IN TELEGRAM
❀13πŸ‘2πŸŽ‰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 ✨
❀4πŸŽ‰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

πŸ‘‰ https://t.iss.one/CodeProgrammer
Please open Telegram to view this post
VIEW IN TELEGRAM
Please open Telegram to view this post
VIEW IN TELEGRAM
❀9πŸ‘2πŸ”₯2πŸ†1
Forwarded from Machine Learning
πŸ“Œ PyTorch Tutorial for Beginners: Build a Multiple Regression Model from Scratch

πŸ—‚ Category: DEEP LEARNING

πŸ•’ 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
❀2
The difference between import os and from os import *
🐳11❀7πŸ‘4πŸ‘Ύ2
🌟 Join @DeepLearning_ai & @MachineLearning_Programming! 🌟
Explore AI, ML, Data Science, and Computer Vision with us. πŸš€

πŸ’‘ Stay Updated: Latest trends & tutorials.
🌐 Grow Your Network: Engage with experts.
πŸ“ˆ Boost Your Career: Unlock tech mastery.

Subscribe Now!
➑️ @DeepLearning_ai
➑️ @MachineLearning_Programming

Step into the futureβ€”today! ✨
❀3πŸ‘1πŸ”₯1
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 β€” neatly organized for quick reference and deep understanding.

https://t.iss.one/CodeProgrammer
❀13
πŸš€ THE 7-DAY PROFIT CHALLENGE! πŸš€

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

https://t.iss.one/+AOPQVJRWlJc5ZGRi
https://t.iss.one/+AOPQVJRWlJc5ZGRi
https://t.iss.one/+AOPQVJRWlJc5ZGRi
1❀6πŸ‘Ž2πŸ‘1
Data Science Formulas Cheat Sheet.pdf
175.4 KB
🏷 Data Science Formulas Cheat Sheet
βž• Application of Each Formula

πŸ‘¨πŸ»β€πŸ’» This cheat sheet presents important data science concepts along with their formulas.

βœ… From key topics in statistics to machine learning and NLP.

βœ… 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://t.iss.one/CodeProgrammer πŸ”°

More Likes Please πŸ–•
Please open Telegram to view this post
VIEW IN TELEGRAM
❀8πŸ‘4
πŸš€ THE 7-DAY PROFIT CHALLENGE! πŸš€

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

https://t.iss.one/+AOPQVJRWlJc5ZGRi
https://t.iss.one/+AOPQVJRWlJc5ZGRi
https://t.iss.one/+AOPQVJRWlJc5ZGRi
❀2πŸŽ‰2
This media is not supported in your browser
VIEW IN TELEGRAM
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:
β€’ PCA
β€’ Bagging and boosting
β€’ Linear regression and OLS
β€’ Bayesian and frequentist statistics
β€’ confidence intervals
β€’ clustering (kmeans, spectral clustering, etc.)
β€’ central limit theorem
β€’ neural networks (the backpropagation animation is really good)
β€’ and many more.

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

https://t.iss.one/CodeProgrammer
1❀7πŸ‘5πŸ’―1
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")


πŸ‘‰  @codeprogrammer
Please open Telegram to view this post
VIEW IN TELEGRAM
❀9πŸ‘9πŸŽ‰2πŸ’―1
πŸ’Έ PacketSDK--A New Way To Make Revenue From Your Apps

Regardless of whether your app is on desktop, mobile, TV, or Unity platforms, no matter which app monetization tools you’re using, PacketSDK can bring you additional revenue!

● Working Principle: Convert your app's active users into profits πŸ‘₯β†’πŸ’΅

● Product Features: Ad-free monetization 🚫, no user interference

● Additional Revenue: Fully compatible with your existing ad SDKs

● CCPA & GDPR: Based on user consent, no collection of any personal data πŸ”’

● Easy Integration: Only a few simple steps, taking approximately 30 minutes

Join us:https://www.packetsdk.com/?utm-source=SyWayQNK

Contact us & Estimated income:
Telegram:@Packet_SDK
Whatsapp:https://wa.me/85256440384
Teams:https://teams.live.com/l/invite/FBA_1zP2ehmA6Jn4AI

⏰ Join early ,earn early!
❀9πŸŽ‰1
Channel photo updated
Learn Complete MatPlotLib.pdf
4.3 MB
Learn Complete #MatPlotLib with most asked #interview questions

πŸ‘‰  @codeprogrammer
Please open Telegram to view this post
VIEW IN TELEGRAM
❀7πŸ‘5πŸ’―1