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
إظهار المزيد
الشبكة:📈 نظرة تحليلية على قناة تيليجرام Machine Learning with Python
تُعد قناة Machine Learning with Python (@codeprogrammer) في القطاع اللغوي الإنكليزية لاعباً نشطاً. يضم المجتمع حالياً 68 505 مشتركاً، محتلاً المرتبة 2 398 في فئة التعليم والمرتبة 4 584 في منطقة الهند.
📊 مؤشرات الجمهور والحراك
منذ تأسيسه في невідомо، حقق المشروع نمواً سريعاً وجمع 68 505 مشتركاً.
بحسب آخر البيانات بتاريخ 30 أكتوبر, 2026، تحافظ القناة على نشاط مستقر. خلال آخر 30 يوماً تغيّر عدد الأعضاء بمقدار 44، وفي آخر 24 ساعة بمقدار -15، مع بقاء الوصول العام مرتفعاً.
- حالة التحقق: غير موثّقة
- معدل التفاعل (ER): يبلغ متوسط تفاعل الجمهور 3.96%. وخلال أول 24 ساعة من النشر يحصد المحتوى عادةً 1.47% من ردود الفعل نسبةً إلى إجمالي المشتركين.
- وصول المنشورات: يحصل كل منشور على متوسط 2 712 مشاهدة. وخلال اليوم الأول يجمع عادةً 1 004 مشاهدة.
- التفاعلات والاستجابة: يتفاعل الجمهور بانتظام؛ متوسط التفاعلات لكل منشور يبلغ 9.
- الاهتمامات الموضوعية: يركز المحتوى على مواضيع رئيسية مثل insidead, learning, degree, evaluation, algorithm.
📝 الوصف وسياسة المحتوى
يصف المؤلف القناة بأنها مساحة للتعبير عن الآراء الذاتية:
“Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers.
Admin: @HusseinSheikho || @Hussein_Sheikho”
بفضل وتيرة التحديث المرتفعة (أحدث البيانات بتاريخ 31 أكتوبر, 2026) تحافظ القناة على حداثتها ومستوى وصول مرتفع. وتُظهر التحليلات تفاعلاً نشطاً من الجمهور، ما يجعلها نقطة تأثير مهمة ضمن فئة التعليم.
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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
🤖🧠 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 ...
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🤖🧠 OpenAI Evals: The Framework Transforming LLM Evaluation and Benchmarking
🗓️ 16 Nov 2025
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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 ...
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🗓️ 16 Nov 2025
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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 ...
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🤖🧠 Steel Browser: The Open-Source Browser API Powering AI Agents and Automation
🗓️ 16 Nov 2025
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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 ...
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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
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15-18 November 2025 Istanbul Turkey Tüyap Fair Center Hall: 3 Stand: 308 A
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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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