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 68 117 suscriptores, ocupando la posición 2 370 en la categoría Educación y el puesto 4 740 en la región India.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 68 117 suscriptores.
Según los últimos datos del 29 agosto, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de 67, y en las últimas 24 horas de 13, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 3.98%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.55% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 2 714 visualizaciones. En el primer día suele acumular 1 053 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 6.
- 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 agosto, 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.
68 117
Suscriptores
+1324 horas
-567 días
+6730 días
Archivo de publicaciones
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Repost 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.
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Stochastic and deterministic sampling methods in diffusion models produce noticeably different trajectories, but ultimately both reach the same goal.
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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