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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Red:📈 Análisis del canal de Telegram Machine Learning with Python
El canal Machine Learning with Python (@codeprogrammer) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 68 521 suscriptores, ocupando la posición 2 402 en la categoría Educación y el puesto 4 600 en la región India.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 68 521 suscriptores.
Según los últimos datos del 29 octubre, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 74, y en las últimas 24 horas de -5, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 3.84%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.47% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 2 629 visualizaciones. En el primer día suele acumular 1 007 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 9.
- Intereses temáticos: El contenido se centra en temas clave como insidead, learning, degree, evaluation, algorithm.
📝 Descripción y política de contenido
El autor describe el recurso como un espacio para expresar opiniones subjetivas:
“Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers.
Admin: @HusseinSheikho || @Hussein_Sheikho”
Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 30 octubre, 2026), el canal mantiene la vigencia y un amplio alcance. La analítica demuestra que la audiencia interactúa activamente con el contenido, lo que lo convierte en un punto de referencia dentro de la categoría Educación.
68 521
Suscriptores
-524 horas
-847 días
+7430 días
Archivo de publicaciones
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✨ By: @DataScienceV
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://t.me/CodeProgrammer
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
🤖🧠 Context Engineering 2.0: Redefining Human–Machine Understanding
🗓️ 16 Nov 2025
📚 AI News & Trends
As artificial intelligence advances, machines are becoming increasingly capable of understanding and responding to human language. Yet, one crucial challenge remains how can machines truly understand the context behind human intentions? This question forms the foundation of context engineering, a discipline that focuses on designing, organizing and managing contextual information so that AI systems can ...
#ContextEngineering #AIEducation #HumanMachineUnderstanding #AIContext #NaturalLanguageProcessing #AIModels
🤖🧠 OpenAI Evals: The Framework Transforming LLM Evaluation and Benchmarking
🗓️ 16 Nov 2025
📚 AI News & Trends
As large language models (LLMs) continue to reshape industries from education and healthcare to marketing and software development – the need for reliable evaluation methods has never been greater. With new models constantly emerging, developers and researchers require a standardized system to test, compare and understand model performance across real-world scenarios. This is where OpenAI ...
#OpenAIEvals #LLMEvaluation #Benchmarking #LargeLanguageModels #AIResearch #ModelEvaluation
🤖🧠 Skyvern: The Future of Browser Automation Powered by AI and Computer Vision
🗓️ 16 Nov 2025
📚 AI News & Trends
In today’s fast-evolving digital landscape, automation plays a crucial role in enhancing productivity, efficiency and innovation. Yet, traditional browser automation tools often struggle with complexity, maintenance and reliability. They rely heavily on DOM parsing, XPaths and rigid scripts that easily break when websites change their layout. Enter Skyvern, an open-source, AI-driven browser automation platform developed ...
#Skyvern #BrowserAutomation #AIDriven #ComputerVision #OpenSource #WebAutomation
🤖🧠 Steel Browser: The Open-Source Browser API Powering AI Agents and Automation
🗓️ 16 Nov 2025
📚 AI News & Trends
The evolution of artificial intelligence has ushered in a new era of automation where AI agents can perform complex digital tasks with minimal human intervention. However, one of the biggest challenges for developers building these systems is browser automation managing sessions, proxies, cookies and debugging environments. This is where Steel Browser comes into play. Steel ...
#SteelBrowser #OpenSource #BrowserAutomation #AIAgents #WebScraping #DigitalAutomation
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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
👉 https://t.me/CodeProgrammer
Eurasia 2025 Window Fair
15-18 November 2025 Istanbul Turkey Tüyap Fair Center Hall: 3 Stand: 308 A
At this importent event you will have the opportunity to feel the pulse of the industry
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Eurasia 2025 Window Fair
15-18 November 2025 Istanbul Turkey Tüyap Fair Center Hall: 3 Stand: 308 A
At this importent event you will have the opportunity to feel the pulse of the industry
Sponsored By WaybienAds
Eurasia 2025 Window Fair
15-18 November 2025 Istanbul Turkey Tüyap Fair Center Hall: 3 Stand: 308 A
At this importent event you will have the opportunity to feel the pulse of the industry
Sponsored By WaybienAds
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