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

显示更多

网络:Machine Learning with Python 叙利亚231 技术与应用3 380...

📈 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 40 134 名订阅者，在 技术与应用 类别中位列第 3 380，并在 叙利亚 地区排名第 231 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 40 134 名订阅者。

根据 25 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 395，过去 24 小时变化为 12，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 1.89%。内容发布后 24 小时内通常能获得 1.31% 的反应，占订阅者总量。
帖子覆盖： 每篇帖子平均可获得 758 次浏览，首日通常累积 525 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 26 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。

40 134

订阅者

+1224 小时

+697 天

+39530 天

758

帖子浏览量

~ 52524 小时

~ 70348 小时

1.89%

参与率

~ 3

每日帖子数

Ads index

beta

帖子存档

40 138

📌 How to Implement Three Use Cases for the New Calendar-Based Time Intelligence 🗂 Category: POWER BI 🕒 Date: 2025-11-25 | ⏱️ Read time: 11 min read Unlock advanced analytics with Power BI's new Calendar-based Time Intelligence, introduced in the September 2025 release. This guide explores three practical use cases, providing step-by-step implementation instructions to help you leverage this powerful new feature for more sophisticated date and time analysis in your reports. #PowerBI #TimeIntelligence #DataAnalytics #BusinessIntelligence

40 138

📌 Why CrewAI’s Manager-Worker Architecture Fails — and How to Fix It 🗂 Category: AGENTIC AI 🕒 Date: 2025-11-25 | ⏱️ Read time: 20 min read CrewAI's manager-worker architecture can misfire in real-world scenarios. This analysis explores the common failures of its hierarchical orchestration model and, more importantly, presents a practical, implementable fix. For developers working with autonomous agents, this guide offers crucial insights for improving the reliability and performance of your AI crews. #CrewAI #AIAgents #AutonomousAgents #AIArchitecture

40 138

💸 PacketSDK--A New Way To Make Revenue From Your Apps Regardless of whether your app is on desktop, mobile, TV, or Unity platforms, no matter which app monetization tools you’re using, PacketSDK can bring you additional revenue! ● Working Principle: Convert your app's active users into profits 👥→💵 ● Product Features: Ad-free monetization 🚫, no user interference ● Additional Revenue: Fully compatible with your existing ad SDKs ● CCPA & GDPR: Based on user consent, no collection of any personal data 🔒 ● Easy Integration: Only a few simple steps, taking approximately 30 minutes Join us：https://www.packetsdk.com/?utm-source=SyWayQNK Contact us & Estimated income: Telegram:@Packet_SDK Whatsapp:https://wa.me/85256440384 Teams:https://teams.live.com/l/invite/FBA_1zP2ehmA6Jn4AI ⏰ Join early ,earn early!

40 138

📌 LLM-as-a-Judge: What It Is, Why It Works, and How to Use It to Evaluate AI Models 🗂 Category: LARGE LANGUAGE MODELS 🕒 Date: 2025-11-24 | ⏱️ Read time: 9 min read Explore the 'LLM-as-a-Judge' framework, a novel approach for evaluating AI systems. This guide explains how to use large language models as automated judges to assess model performance and ensure AI quality control. It provides a step-by-step breakdown of the methodology, explores the reasons behind its effectiveness, and shows you how to implement this powerful evaluation technique. #AIEvaluation #LLM #MLOps #LLMasJudge

40 138

📌 A Hands-On Guide to Anthropic’s New Structured Output Capabilities 🗂 Category: LLM APPLICATIONS 🕒 Date: 2025-11-24 | ⏱️ Read time: 12 min read Unlock reliable, structured outputs from Anthropic's latest AI models. This hands-on developer guide demonstrates how to leverage new capabilities in Claude 3.5 Sonnet and Opus to generate perfect JSON and other typed outputs, streamlining data extraction and tool use for your applications. #Anthropic #ClaudeAI #StructuredOutput #AIdevelopment #JSON

40 138

What if the future of mining was already here? Discover how AI-powered rigs, sky grids, and passive crypto income are reshaping the rules right now. Want effortless returns—even while you sleep? Unlock the next era of mining and start earning with zero stress—exclusive insights await in Mining Pulse. Don’t miss your chance to join the innovators—subscribe now! InsideAds

40 138

📌 Struggling with Data Science? 5 Common Beginner Mistakes 🗂 Category: DATA SCIENCE 🕒 Date: 2025-11-24 | ⏱️ Read time: 6 min read New to data science? Accelerate your career growth by steering clear of common beginner pitfalls. The journey into data science is challenging, but understanding and avoiding five frequent mistakes can significantly streamline your learning curve and set you on a faster path to success. This guide highlights the key errors to watch out for as you build your skills and advance in the field. #DataScience #MachineLearning #CareerAdvice #DataAnalytics

40 138

📌 How to Implement Randomization with the Python Random Module 🗂 Category: PROGRAMMING 🕒 Date: 2025-11-24 | ⏱️ Read time: 6 min read Master Python's built-in random module to introduce unpredictability into your applications. This guide explores how to effectively generate random outputs, a crucial technique for tasks ranging from shuffling data and creating simulations to developing games and selecting random samples. Learn the core functions and practical implementations to leverage randomization in your code. #Python #Programming #CodingTips #Random

40 138

Agentic #Ai Learning #roadmap https://t.me/DataScienceM

40 138

I never thought “money while I sleep” was real—until I saw my balance jump overnight. No charts, no stress—just connect, and the profits come in. Want to know how I did it? 👉 See the secret here InsideAds

40 138

🏆 Demystifying Epochs & Iterations in Deep Learning 📢 Unlock deep learning clarity! Understand Epochs vs. Iterations, the fundamental terms defining neural network training. ⚡ Tap to unlock the complete answer and gain instant insight. ━━━━━━━━━━━━━━━ By: @DataScienceM ✨

40 138

📌 Your Next ‘Large’ Language Model Might Not Be Large After All 🗂 Category: ARTIFICIAL INTELLIGENCE 🕒 Date: 2025-11-23 | ⏱️ Read time: 11 min read A paradigm shift may be underway in AI, as a compact 27M-parameter model has outperformed industry giants like DeepSeek R1, o3-mini, and Claude 3.7 on complex reasoning tasks. This breakthrough challenges the "bigger is better" philosophy for language models, signaling a significant trend towards smaller, more efficient, and highly capable models. This development suggests future advancements may focus on architectural innovation and training efficiency over sheer parameter count. #AI #LLM #SLM #ModelEfficiency

40 138

📌 Learning Triton One Kernel at a Time: Softmax 🗂 Category: MACHINE LEARNING 🕒 Date: 2025-11-23 | ⏱️ Read time: 10 min read Explore a step-by-step guide to implementing a fast, readable, and PyTorch-ready softmax kernel with Triton. This tutorial breaks down how to write efficient GPU code for a crucial machine learning function, offering developers practical insights into high-performance computing and AI model optimization. #Triton #GPUProgramming #PyTorch #MachineLearning

40 138

Curious how mining is becoming carbon-negative and even repairs the planet? Stop reading outdated crypto news — get ahead of the curve with real tech insights, futuristic mining models, and ways to earn passively with zero trading hassle. Don’t miss the evolution — discover the next era of mining right here. Profit grows while you sleep. Level up now! Join Mining Pulse InsideAds

40 138

AI - ML Explained.pdf1.58 MB

40 138

📌 Overfitting vs. Underfitting: Making Sense of the Bias-Variance Trade-Off 🗂 Category: DATA SCIENCE 🕒 Date: 2025-11-22 | ⏱️ Read time: 4 min read Mastering the bias-variance trade-off is key to effective machine learning. Overfitting creates models that memorize training data noise and fail to generalize, while underfitting results in models too simple to find patterns. The optimal model exists in a "sweet spot," balancing complexity to perform well on new, unseen data. This involves learning just the right amount from the training set—not too much, and not too little—to achieve strong predictive power. #MachineLearning #DataScience #Overfitting #BiasVariance

40 138

📌 Empirical Mode Decomposition: The Most Intuitive Way to Decompose Complex Signals and Time Series 🗂 Category: DATA SCIENCE 🕒 Date: 2025-11-22 | ⏱️ Read time: 7 min read Discover Empirical Mode Decomposition (EMD), an intuitive method for breaking down complex signals and time series. This technique provides a step-by-step approach to effectively extract underlying patterns and components from your data, offering a powerful tool for signal processing and time series analysis. #EMD #TimeSeriesAnalysis #SignalProcessing #DataScience

40 138

Repost from Machine Learning with Python

🚀 THE 7-DAY PROFIT CHALLENGE! 🚀 Can you turn $100 into $5,000 in just 7 days? Lisa can. And she’s challenging YOU to do the same. 👇 https://t.me/+AOPQVJRWlJc5ZGRi https://t.me/+AOPQVJRWlJc5ZGRi https://t.me/+AOPQVJRWlJc5ZGRi

40 138

Love our channel? Advertise here — and across 6 000+ Telegram channels ✈️ ⚡️ Launch your Telegram ads in minutes with access to verified channels, groups, mini apps, and bots. Reach real, bot-free audiences — from crypto to lifestyle — with automated placements, live analytics, and measurable results. How it works: 1️⃣ Sign up via this link: Telega.io 2️⃣ Add funds 3️⃣ Choose channels and add your ad post ➡️ We’ll take care of the rest Stay ahead — 6 000+ channels to test, track, and scale!