Machine Learning

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Updated: May 21, 2026 at 6:17 PM

Machine Learning

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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Machine Learning

May 21, 2026, 06:18 PM

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Reference sheet I can look up anytime. 📄 Good for anyone who wants to understand DL mathematically. 🧮

Topics covered:
- Notation, Forward Prop & Backpropagation 🔃
- Activation Functions, Loss, Gradient Descent (Adam, RMSProp...) 📉
- CNNs, RNNs, GRUs, LSTMs 🧠
- Transformers and Self-Attention 🔄
- 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! 🎓✨

😮

1,980

Machine Learning

May 21, 2026, 06:18 PM

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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: https://www.linkedin.com/safety/go/?url=http://udlbook.github.io/udlbook/&urlhash=Ah9k&isSdui=true 🔗

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Machine Learning

May 21, 2026, 06:18 PM

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Machine Learning Specialization: Study Notes and Laboratory Exercises

This repository contains personal notes and laboratory notebooks derived from the Machine Learning Specialization offered by http://DeepLearning.AI/ and Stanford Online (Coursera), under the instruction of Professor Andrew Ng.

Repository: https://github.com/TruongDat05/machine-learning-notes-and-code

Tg Channel:

2,370

Machine Learning

May 21, 2026, 06:18 PM

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Foundations of Machine Learning 📘🤖

A 505-pages book from MIT for beginners is FREE. 🎓✨

Link: http://cs.nyu.edu/~mohri/mlbook 🤩

🤩

2,620

Machine Learning

May 21, 2026, 06:18 PM

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Cheat sheet on Matplotlib tips & tricks 📊✨:

* Transparency — displaying data density 📉
* Rasterization — optimizing large graphs through rasterization ⚙️
* Offline rendering — rendering without a GUI via the backend 🖥️
* Text outline — outlining text for better visibility 🖊️
* Multiline plot — constructing multiple lines in a single graph 📈
* Dotted lines — custom dotted lines ⚪
* Colorbar adjustment — adjusting the color scale 🌈
* Typography — improving the appearance of text and fonts 🅰️
* Remove margins — removing unnecessary indents 📏
* Hatching — fill patterns 🎨
* Colormap — working with color palettes 🖌️
* Combining axes — combining different axes in a single graph 🔄

2,210

Machine Learning

May 21, 2026, 06:18 PM

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Machine Learning

May 21, 2026, 06:18 PM

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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).
➜ Complexity: O(n · w)
➜ Use: Streaming text, audio, DNA. 🎧🧬
➜ Trade-off: Linear scaling but zero long-range mixing between windows. 🔄

3️⃣ Sparse – e.g., BigBird, Longformer 🕸
➜ Pattern: Local + Global (e.g., [CLS] tokens) + Random/strided.
➜ Complexity: O(n · (w + g + r)) ≈ O(n)
➜ Use: Document summarization (5k–16k tokens). 📝
➜ Insight: Sparse graphs preserve universal approximation if graph diameter is bounded. 🔗

Where we're going: Static sparsity is losing to dynamic routing (Mixture of Depths, 2024). 🚀 Also, linear RNN-like attention (Mamba, RWKV) challenges whether we need any static pattern. 🤔

😡

Machine Learning

May 21, 2026, 06:18 PM

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This Machine Learning Cheat Sheet Saved Me Hours of Revision ⏳

It includes:
✅ Supervised & Unsupervised algorithms
✅ Regression, Classification & Clustering techniques
✅ PCA & Dimensionality Reduction
✅ Neural Networks, CNN, RNN & Transformers
✅ Assumptions, Pros/Cons & Real-world use cases

Whether you're:
🔹 Preparing for data science interviews
🔹 Working on ML projects
🔹 Or strengthening your fundamentals
this one-page guide is a must-save.

♻️ Repost and share with your ML circle.

#MachineLearning #DataScience #AI #MLAlgorithms #InterviewPrep #LearnML

2,000

Machine Learning

May 21, 2026, 06:18 PM

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Linear Regression explained in a simple geometric way

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1,620

Machine Learning

May 21, 2026, 06:18 PM

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Convolutional Neural Network

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