Machine Learning

前往频道在 Telegram

Real Machine Learning — simple, practical, and built on experience. Learn step by step with clear explanations and working code. Admin: @HusseinSheikho || @Hussein_Sheikho

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网络:Machine Learning with Python 叙利亚227 技术与应用3 364...

📈 Telegram 频道 Machine Learning 的分析概览

频道 Machine Learning (@machinelearning9) 英语语言赛道中的是活跃参与者。目前社区聚集了 40 145 名订阅者，在 技术与应用 类别中位列第 3 364，并在 叙利亚 地区排名第 227 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 40 145 名订阅者。

根据 27 六月, 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 412，过去 24 小时变化为 5，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 1.96%。内容发布后 24 小时内通常能获得 1.89% 的反应，占订阅者总量。
帖子覆盖： 每篇帖子平均可获得 785 次浏览，首日通常累积 760 次浏览。
互动与反馈： 受众积极参与，单帖平均反应数为 2。
主题关注点： 内容集中在 distance, insidead, gpu, learning, degree 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：
“Real Machine Learning — simple, practical, and built on experience. Learn step by step with clear explanations and working code. Admin: @HusseinSheikho || @Hussein_Sheikho”

凭借高频更新（最新数据采集于 28 六月, 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 技术与应用 类别中的关键影响点。

40 145

订阅者

+524 小时

+1067 天

+41230 天

785

帖子浏览量

~ 76024 小时

~ 1 06948 小时

1.96%

参与率

~ 3

每日帖子数

Ads index

beta

帖子存档

40 145

Repost from ️Crypto Rates, Prices and news

Check the Risk Before You Send Crypto Run a real-time risk check on any wallet and get an AML-grade security report in minutes. Spot suspicious activity before you send. Supports major chains (BTC, ETH, SOL, BNB and more). Sponsored By WaybienAds

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Sepp Hochreiter, who invented LSTM 30+ year ago, gave a keynote talk at Neurips 2024 and introduced xLSTM (Extended Long Short-Term Memory). I designed this Excel exercise to help you understand how xLSTM works. More: https://www.byhand.ai/p/xlstm

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📌 How to Keep AI Costs Under Control 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-10-23 | ⏱️ Read time: 4 min read Lessons from Scaling LLMs

40 145

📌 When Transformers Sing: Adapting SpectralKD for Text-Based Knowledge Distillation 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-10-23 | ⏱️ Read time: 8 min read Exploring the frequency fingerprints of Transformers to guide smarter knowledge distillation

40 145

📌 Deploy an OpenAI Agent Builder Chatbot to a Website 🗂 Category: AGENTIC AI 🕒 Date: 2025-10-24 | ⏱️ Read time: 12 min read Using OpenAI’s Agent Builder ChatKit

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📌 Choosing the Best Model Size and Dataset Size under a Fixed Budget for LLMs 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-10-24 | ⏱️ Read time: 5 min read A small-scale exploration using Tiny Transformers

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📌 How to Consistently Extract Metadata from Complex Documents 🗂 Category: LLM APPLICATIONS 🕒 Date: 2025-10-24 | ⏱️ Read time: 8 min read Learn how to extract important pieces of information from your documents

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📌 Agentic AI from First Principles: Reflection 🗂 Category: AGENTIC AI 🕒 Date: 2025-10-24 | ⏱️ Read time: 21 min read From theory to code: building feedback loops that improve LLM accuracy

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📌 Building a Geospatial Lakehouse with Open Source and Databricks 🗂 Category: DATA ENGINEERING 🕒 Date: 2025-10-25 | ⏱️ Read time: 10 min read An example workflow for vector geospatial data science

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📌 Data Visualization Explained (Part 4): A Review of Python Essentials 🗂 Category: DATA SCIENCE 🕒 Date: 2025-10-25 | ⏱️ Read time: 8 min read Learn the foundations of Python to take your data visualization game to the next level.

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In Python, handling CSV files is straightforward using the built-in csv module for reading and writing tabular data, or pandas for advanced analysis—essential for data processing tasks like importing/exporting datasets in interviews.

# Reading CSV with csv module (basic)
import csv
with open('data.csv', 'r') as file:
    reader = csv.reader(file)
    data = list(reader)  # data = [['Name', 'Age'], ['Alice', '30'], ['Bob', '25']]

# Writing CSV with csv module
import csv
with open('output.csv', 'w', newline='') as file:
    writer = csv.writer(file)
    writer.writerow(['Name', 'Age'])  # Header
    writer.writerows([['Alice', 30], ['Bob', 25]])  # Data rows

# Advanced: Reading with pandas (handles headers, missing values)
import pandas as pd
df = pd.read_csv('data.csv')  # df = DataFrame with columns 'Name', 'Age'
print(df.head())  # Output: First 5 rows preview

# Writing with pandas
df.to_csv('output.csv', index=False)  # Saves without row indices

#python #csv #pandas #datahandling #fileio #interviewtips 👉 @DataScience4

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📌 Google’s Data Science Agent: Can It Really Do Your Job? 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-03-21 | ⏱️ Read time: 11 min read I tested Google’s Data Science Agent in Colab—here’s what it got right (and where it…

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📌 What Germany Currently Is Up To, Debt-Wise 🗂 Category: DATA SCIENCE 🕒 Date: 2025-03-21 | ⏱️ Read time: 6 min read Billions, visualized to scale using python and HTML

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📌 No More Tableau Downtime: Metadata API for Proactive Data Health 🗂 Category: DATA SCIENCE 🕒 Date: 2025-03-21 | ⏱️ Read time: 14 min read Leverage the power of the Metadata API to act on any potential data disruptions

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📌 Evolving Product Operating Models in the Age of AI 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-03-21 | ⏱️ Read time: 14 min read This article explores how the product operating model, and the core competencies of empowered product…

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📌 Build Your Own AI Coding Assistant in JupyterLab with Ollama and Hugging Face 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-03-24 | ⏱️ Read time: 8 min read A step-by-step guide to creating a local coding assistant without sending your data to the…

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📌 From Fuzzy to Precise: How a Morphological Feature Extractor Enhances AI’s Recognition Capabilities 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-03-25 | ⏱️ Read time: 22 min read Mimicking human visual perception to truly understand objects

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📌 What Do Machine Learning Engineers Do? 🗂 Category: MACHINE LEARNING 🕒 Date: 2025-03-25 | ⏱️ Read time: 8 min read Breaking down my role as a machine learning engineer

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📌 Least Squares: Where Convenience Meets Optimality 🗂 Category: DATA SCIENCE 🕒 Date: 2025-03-25 | ⏱️ Read time: 11 min read Beyond being computationally easy, Least Squares is statically optimal and has a deep connection with…

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📌 Evaluating LLMs for Inference, or Lessons from Teaching for Machine Learning 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-06-02 | ⏱️ Read time: 12 min read It’s like grading papers, but your student is an LLM