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Machine Learning with Python

Machine Learning with Python

前往频道在 Telegram

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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📈 Telegram 频道 Machine Learning with Python 的分析概览

频道 Machine Learning with Python (@codeprogrammer) 英语 语言赛道中的 是活跃参与者。目前社区聚集了 68 505 名订阅者,在 教育 类别中位列第 2 398,并在 印度 地区排名第 4 584 位。

📊 受众指标与增长动态

自 невідомо 创建以来,项目保持高速增长,吸引了 68 505 名订阅者。

根据 30 十月, 2026 的最新数据,频道保持稳定运转。过去 30 天订阅人数变化为 44,过去 24 小时变化为 -15,整体触达仍然可观。

  • 认证状态: 未认证
  • 互动率 (ER): 平均受众互动率为 3.96%。内容发布后 24 小时内通常能获得 1.47% 的反应,占订阅者总量。
  • 帖子覆盖: 每篇帖子平均可获得 2 712 次浏览,首日通常累积 1 004 次浏览。
  • 互动与反馈: 受众积极参与,单帖平均反应数为 9。
  • 主题关注点: 内容集中在 insidead, learning, degree, evaluation, algorithm 等核心主题上。

📝 描述与内容策略

作者将该频道定位为表达主观观点的平台:
“Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers. Admin: @HusseinSheikho || @Hussein_Sheikho”

凭借高频更新(最新数据采集于 31 十月, 2026),频道始终保持新鲜度与高覆盖。分析显示受众积极互动,使其成为 教育 类别中的关键影响点。

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68 505
订阅者
-1524 小时
-1027 天
+4430 天
帖子存档
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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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