Python | Machine Learning | Coding | R
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Discover powerful insights with Python, Machine Learning, Coding, and Rβ€”your essential toolkit for data-driven solutions, smart alg

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By: @CodeProgrammer ✨
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πŸ€–πŸ§  Nanobrowser: The Open-Source AI Web Automation Tool Changing How We Browse

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

The rise of artificial intelligence has redefined how we interact with the web, transforming routine browsing into a space for automation and productivity. Among the most exciting innovations in this field is Nanobrowser, an open-source AI-powered web automation tool designed to run directly inside your browser. Developed as a free alternative to OpenAI Operator, Nanobrowser ...

#Nanobrowser #AIWebAutomation #OpenSourceTools #BrowserAI #ProductivityTech #AIAutomation
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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
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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 πŸ’―

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

https://t.iss.one/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 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
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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

πŸ‘‰ https://t.iss.one/CodeProgrammer
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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
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The difference between import os and from os import *
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