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Machine Learning with Python

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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Machine Learning with Python (@codeprogrammer) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 68 505 obunachidan iborat bo'lib, Taʼlim toifasida 2 398-o'rinni va Hindiston mintaqasida 4 584-o'rinni egallagan.

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невідомо sanasidan buyon loyiha tez o‘sib, 68 505 obunachiga ega bo‘ldi.

30 Oktabr, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni 44 ga, so‘nggi 24 soatda esa -15 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.

  • Tasdiqlash holati: Tasdiqlanmagan
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  • Post qamrovi: Har bir post o‘rtacha 2 712 marta ko‘riladi; birinchi sutkada odatda 1 004 ta ko‘rish yig‘iladi.
  • Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 9 ta reaksiya keladi.
  • Tematik yo‘nalishlar: Kontent insidead, learning, degree, evaluation, algorithm kabi asosiy mavzularga jamlangan.

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Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida ta’riflaydi:
“Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers. Admin: @HusseinSheikho || @Hussein_Sheikho”

Yuqori yangilanish chastotasi (oxirgi ma’lumot 31 Oktabr, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli bo‘lib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Taʼlim toifasidagi muhim ta’sir nuqtasiga aylantirishini ko‘rsatadi.

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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 👉 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
#Python #CleanCode #ProgrammingTips #SoftwareDevelopment #Dataclasses #CodeQuality ━━━━━━━━━━━━━━━ By: @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 👉 https://t.me/CodeProgrammer

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