اکنون در دسترس! پژوهش تلگرام ۲۰۲۵ — مهمترین بینشهای سال 
Machine Learning
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Real Machine Learning — simple, practical, and built on experience. Learn step by step with clear explanations and working code. Admin: @HusseinSheikho || @Hussein_Sheikho
نمایش بیشتر📈 تحلیل کانال تلگرام Machine Learning
کانال Machine Learning (@machinelearning9) در بخش زبانی انگلیسی بازیگری فعال است. در حال حاضر جامعه شامل 40 039 مشترک است و جایگاه 3 406 را در دسته فناوری و برنامهها و رتبه 232 را در منطقه سوريا دارد.
📊 شاخصهای مخاطب و پویایی
از زمان ایجاد در невідомо، پروژه رشد سریعی داشته و 40 039 مشترک جذب کرده است.
بر اساس آخرین دادهها در تاریخ 21 ژوئن, 2026، کانال فعالیت پایداری دارد. در ۳۰ روز گذشته تغییر اعضا برابر 398 و در ۲۴ ساعت گذشته برابر -1 بوده و همچنان دسترسی گستردهای حفظ شده است.
- وضعیت تأیید: تأیید نشده
- نرخ تعامل (ER): میانگین تعامل مخاطب 2.30% است و در ۲۴ ساعت نخست پس از انتشار، محتوا معمولاً 1.15% واکنش نسبت به کل مشترکان کسب میکند.
- دسترسی پستها: هر پست به طور میانگین 920 بازدید دریافت میکند. در اولین روز معمولاً 461 بازدید جمعآوری میشود.
- واکنشها و تعامل: مخاطبان بهطور فعال حمایت میکنند؛ میانگین واکنش به هر پست 3 است.
- علایق موضوعی: محتوا بر موضوعات کلیدی مانند distance, insidead, gpu, learning, degree تمرکز دارد.
📝 توضیح و سیاست محتوایی
نویسنده این فضا را محل بیان دیدگاههای شخصی توصیف میکند:
“Real Machine Learning — simple, practical, and built on experience.
Learn step by step with clear explanations and working code.
Admin: @HusseinSheikho || @Hussein_Sheikho”
به لطف بهروزرسانیهای پرتکرار (آخرین داده در تاریخ 22 ژوئن, 2026)، کانال همواره بهروز و دارای دسترسی بالاست. تحلیلها نشان میدهد مخاطبان بهطور فعال با محتوا تعامل دارند و آن را به نقطه اثرگذاری مهم در دسته فناوری و برنامهها تبدیل کردهاند.
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You can outsource your thinking but you cannot outsource your understanding. 🧠✨
That's the entire problem with ML education right now. 📉
PyTorch will let you train a model without knowing what a gradient is. ⚡️ Keras will let you stack layers without knowing what any of them compute. The code runs. The model trains. You have output. You have zero understanding. 🤷♂️
Simon J.D. Prince built a notebook collection that won't let you skip the hard part. 🛠
Shallow networks first. What does one layer actually compute? What do the decision regions look like? You see it geometrically before you write a single line. 📐👀
Optimization compared, not prescribed. Line Search vs SGD vs Adam on the same problem. You watch them diverge. You understand why Adam isn't always the answer. 📉📈
Backpropagation to Self-Attention to Graph Neural Networks as one continuous thread. Not isolated tutorials. A progression. 🔗🚀
Three lines of code can train a model. These notebooks make sure you understand the model you trained. 🧐
Here's the resource: udlbook.github.io/udlbook/ 🔗
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✔️ Electrical, Civil, Mechanical, Medical, Management, Computer Science & All Engineering Fields
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Reference sheet I can look up anytime. 📄 Good for anyone who wants to understand DL mathematically. 🧮
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- CNNs, RNNs, GRUs, LSTMs 🧠
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- ML Strategy and Shape Reference Tables 📊
52 pages, free to download. ⬇️
GitHub: https://github.com/Jerry-0821/deep-learning-formula-cheatsheet
Hope it helps other students or anyone trying to understand the math behind deep learning! 🎓✨
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Foundations of Machine Learning 📘🤖
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Link: cs.nyu.edu/~mohri/mlbook 🤩
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Machine Learning Specialization — Study Notes & Labs 📚🔬
Personal notes and lab notebooks from the Machine Learning Specialization by DeepLearning.AI & Stanford Online (Coursera), instructed by Prof. Andrew Ng. 🎓
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🌐 Global, Local, Sparse: Attention Patterns in Long-Context Transformers
The O(n²) complexity of dense (global) attention is impractical for long sequences. Here's what ML engineers need to know about the three dominant patterns: 🧠⚙️
1️⃣ Global (Full Dense) 🌍
➜ Every token attends to every token.
➜ A = softmax(QKᵀ / √d) V
➜ Complexity: O(n²d)
➜ Use: Short contexts (<4k) or precise recall tasks. 🎯
➜ Downside: KV cache memory explodes. 💥
2️⃣ Local (Sliding Window) – e.g., Mistral 🪟
➜ Tokens attend to a fixed neighborhood (±512).
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3️⃣ Sparse – e.g., BigBird, Longformer 🕸
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This Machine Learning Cheat Sheet Saved Me Hours of Revision ⏳
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