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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 GitHubπ― : https://github.com/gorvgoyl/clone-wars
π https://t.iss.one/CodeProgrammer
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
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Tip for clean code in Python:
Use Dataclasses for classes that primarily store data. The
#Python #CleanCode #ProgrammingTips #SoftwareDevelopment #Dataclasses #CodeQuality
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By: @CodeProgrammer β¨
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
βββββββββββββββ
By: @CodeProgrammer β¨
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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
π https://t.iss.one/CodeProgrammer
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
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Forwarded from Machine Learning
π PyTorch Tutorial for Beginners: Build a Multiple Regression Model from Scratch
π Category: DEEP LEARNING
π Date: 2025-11-19 | β±οΈ 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
π Category: DEEP LEARNING
π Date: 2025-11-19 | β±οΈ 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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Explore AI, ML, Data Science, and Computer Vision with us. π
π‘ Stay Updated: Latest trends & tutorials.
π Grow Your Network: Engage with experts.
π Boost Your Career: Unlock tech mastery.
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Step into the futureβtoday! β¨
Explore AI, ML, Data Science, and Computer Vision with us. π
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π Grow Your Network: Engage with experts.
π Boost Your Career: Unlock tech mastery.
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Step into the futureβtoday! β¨
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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 β neatly organized for quick reference and deep understanding.
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
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π THE 7-DAY PROFIT CHALLENGE! π
Can you turn $100 into $5,000 in just 7 days?
Lisa can. And sheβs challenging YOU to do the same. π
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Can you turn $100 into $5,000 in just 7 days?
Lisa can. And sheβs challenging YOU to do the same. π
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Data Science Formulas Cheat Sheet.pdf
175.4 KB
π¨π»βπ» This cheat sheet presents important data science concepts along with their formulas.
https://t.iss.one/CodeProgrammer
More Likes Please
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Forwarded from Machine Learning with Python
π THE 7-DAY PROFIT CHALLENGE! π
Can you turn $100 into $5,000 in just 7 days?
Lisa can. And sheβs challenging YOU to do the same. π
https://t.iss.one/+AOPQVJRWlJc5ZGRi
https://t.iss.one/+AOPQVJRWlJc5ZGRi
https://t.iss.one/+AOPQVJRWlJc5ZGRi
Can you turn $100 into $5,000 in just 7 days?
Lisa can. And sheβs challenging YOU to do the same. π
https://t.iss.one/+AOPQVJRWlJc5ZGRi
https://t.iss.one/+AOPQVJRWlJc5ZGRi
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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:
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
(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
1β€8π5π―1
Create a Wi-Fi QR code in Python in a couple of seconds
π @codeprogrammer
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")
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πΈ 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 youβre using, PacketSDK can bring you additional revenue!
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β Additional Revenue: Fully compatible with your existing ad SDKs
β CCPA & GDPR: Based on user consent, no collection of any personal data π
β Easy Integration: Only a few simple steps, taking approximately 30 minutes
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Contact us & Estimated income:
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Regardless of whether your app is on desktop, mobile, TV, or Unity platforms, no matter which app monetization tools youβre using, PacketSDK can bring you additional revenue!
β Working Principle: Convert your app's active users into profits π₯βπ΅
β Product Features: Ad-free monetization π«, no user interference
β Additional Revenue: Fully compatible with your existing ad SDKs
β CCPA & GDPR: Based on user consent, no collection of any personal data π
β Easy Integration: Only a few simple steps, taking approximately 30 minutes
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Contact us & Estimated income:
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Whatsapp:https://wa.me/85256440384
Teams:https://teams.live.com/l/invite/FBA_1zP2ehmA6Jn4AI
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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
https://t.iss.one/CodeProgrammer
#NumPy #codingexams
https://t.iss.one/CodeProgrammer
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Machine Learning with Python
πΈ 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 youβre using, PacketSDK can bring you additional revenue! β Working Principle: Convertβ¦
I want to share a tool that I genuinely believe can make a real difference for anyone building apps: PacketSDK. Many developers have strong active-user bases but still struggle to increase revenue. Thatβs exactly why this solution stands outβit adds extra income without disrupting users or interfering with your existing monetization methods.
Why I strongly recommend it:
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As a channel owner, I recommend trying this service; you have nothing to lose.
I used it and found its earnings amazing.
Why I strongly recommend it:
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* Integration is fast and straightforwardβaround 30 minutes.
* It works on all platforms: mobile, desktop, TV, Unity, and more.
As a channel owner, I recommend trying this service; you have nothing to lose.
I used it and found its earnings amazing.
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