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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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🚀 Demystifying Activation Functions! 🧠✨
Ever wondered why activation functions are so critical in neural networks? 🤔🤖
They’re the secret sauce that allows models to capture complex, nonlinear relationships! 🔥📈
Do you want to learn how to implement an artificial neural network from scratch in Python using NumPy? 🐍📊
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Most people don’t fail because they lack ambition 🎯
They fail because their time and energy leak into distractions before they ever reach the goal 🕰💔
This visual explains it perfectly: 📊
Time + Energy are only useful when filtered through discipline 🧠🛡
Without discipline, distractions absorb everything: 🌊
• endless notifications 📱
• reactive meetings 🤝
• poor sleep 😴
• stress-driven habits 🤯
• multitasking disguised as productivity 🔄
And in high-performance environments, this becomes a leadership issue, not just a personal one 🏢📉
I see this often in corporate wellness workshops and executive coaching sessions. 🗣
Leaders want better focus, resilience, and performance ⚡️
But the real challenge is not motivation 🚫🔥
It’s protecting their cognitive energy daily 🧠🔒
Discipline is not punishment ⛔️
It’s a system that helps you direct your energy intentionally 🎯
Sometimes that means: ✔️
✔️ starting the day without your phone 📵
✔️ eating to stabilize energy and focus 🥗
✔️ creating recovery moments between meetings ⏸️
✔️ reducing unnecessary inputs 📉
✔️ building routines that lower decision fatigue 🧘
Because peak performance is rarely about doing more 🏃
It’s about allowing less distraction to consume what matters most 🎯✨
Your goals are not only built by effort 💪
They are built by what you consistently refuse to give your energy to 🚫🕸
And if you’re ready to start, I created something simple for you: 🚀
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14 minutes with an Anthropic engineer will teach you more about building agents 🤖 than most devs figure out in months of trial and error 🛠.
Same guy who wrote “Building Effective Agents”, the post every AI builder has bookmarked 📑.
No fluff. No 47-tool frameworks. Just the patterns that actually work in production 🚀:
→ When to use workflows vs. agents (most people get this wrong) ❌
→ Why simple > clever, every single time ✅
→ The orchestrator-worker pattern that scales 📈
→ When NOT to build an agent at all 🛑
If you’re shipping AI products in 2026 and haven’t watched this, you’re doing it on hard mode 🎮.
14 minutes. Bookmark it 📌. Watch it twice 👀.
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Google Gemma 4's pre-training is completely free
All you need is a browser and access to more than 500 models to choose from.
The process is simple:
1. Open the notebook of Unsloth in Colab
2. Select a model and a dataset
3. Start the trainin
Link: https://colab.research.google.com/github/unslothai/unsloth/blob/main/studio/Unsloth_Studio_Colab.ipynb
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👉 /channel/MachineLearning9
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🎓 Thesis • Dissertation • Research • Programming • Simulation
From a single research idea…
to a complete academic masterpiece.
🔹 Professional assistance for:
✔️ Master’s & PhD Theses
✔️ ISI / Scopus Articles
✔️ Research Proposals & Methodology
✔️ Data Analysis & Statistical Modeling
✔️ AI & Machine Learning Projects
✔️ MATLAB • Python • Simulink • Abaqus • COMSOL • Ansys • ETAP • PSCAD • HOMER • Proteus • LabVIEW
✔️ Electrical, Civil, Mechanical, Medical, Management, Computer Science & All Engineering Fields
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✔️ Simulation Projects & Optimization Algorithms
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💻 Clean & Structured Coding
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Your research deserves more than copy-paste work.
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Master Python the Right Way – Without Procrastination. 🐍✨
When I first started learning Python, I quickly realized:
You can't master a programming language just by reading syntax or watching tutorials. 📚🚫
Real growth happens when you practice, build, and solve problems on your own. 🛠💻
That's exactly why I've compiled a collection of Python programs – designed to take you from basics to advanced logic-building. 📈🧠
What is this collection about? 🤔
✔️ Beginner to advanced programs with clear explanations
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This is perfect for: 🎯
• Preparing for technical interviews 🤝
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/channel/pythonRe
🧐 Python Cheatsheet — a convenient cheat sheet for Python that really saves time at work!
The repository contains a summary of key topics: from basic syntax and data structures to working with files, environments, and OOP with classes and magic methods. Everything is presented compactly, without unnecessary theory, with examples that can be immediately applied in code.
Repo: https://github.com/onyxwizard/python-cheatsheet
/channel/CodeProgrammer 👩💻
🧐 Confusion Matrix: Less confusing 🤯
Many data science beginners struggle to understand true negative (TN), false negative (FN), false positive (FP), and true positive (TP). 🤔
You can easily understand the values using the confusion matrix. 📊
💡 It is a 2x2 matrix for a binary classifier:
- True Negative (TN): True Negative prediction ✅
- False Negative (FN): False Negative prediction ❌
- False Positive (FP): False Positive prediction 🚨
- True Positive (TP): True Positive prediction 🎯
❓ For each prediction, ask two questions:
1. Did the model do it right? Yes (True) or No (False)
2. What was the predicted class? Positive or Negative
/channel/CodeProgrammer
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Overfitting and Generalization in Machine Learning
My ML model had 100% accuracy.
And was completely useless.
That's not a paradox; that's overfitting.
The model didn't learn. It memorized.
Here's the mathematical core most tutorials skip:
E[loss] = Bias² + Variance + σ²
→ Bias² = too simple → Underfitting
→ Variance = too complex → Overfitting
→ σ² = irreducible → always there
What this actually means in practice:
→ A degree-9 polynomial on 6 data points hits R² = 1.0 and oscillates wildly between them
→ A linear model on sine-wave data has near-zero variance — but massive bias
→ The optimal model isn't the simplest. Not the most complex. It's the one minimizing Bias² + Variance
And the generalization gap?
Formally defined as:
gen_gap(f) = R(f) − R_emp(f)
When this value is ≫ 0, your model is learning noise, not signal.
The fix isn't "collect more data and hope."
The fix is regularization, which I derive fully in my paper: L1, L2, Dropout, and Early Stopping, all from first principles.
Which regularization strategy do you use most and why?
/channel/CodeProgrammer
🔖 A huge repository of resources on Data Science 📈
Awesome DataScience — a structured list of open-source data, datasets, libraries, and tutorials for solving real-world problems. 🛠️
It's useful for both beginners and those already familiar with the field — you'll find something new here. 🌱
⛓️ Link to GitHub: https://github.com/academic/awesome-datascience 🔗
tags: #DataScientist 🤖 #AI 🧠 #TechCommunity 🌐 #GrowthMindset 📈 #OpenSource 🏆
▶️ /channel/CodeProgrammer 👨💻
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reader3 📚✨
When you want to connect an AI like Gemini to help you analyze books or content, copying text from a reader usually becomes a hassle. 😩💻
Especially if you want to discuss a book by chapters. Highlighting text manually and copying it disrupts the flow and feels like a waste of time. ⏳🚫
Yesterday, Andrzej Karpati, a well-known AI expert, released a new project to the public: reader3, which solves this problem very neatly. 🎉🛠️ It's a lightweight EPUB reader that allows you to read a book together with AI. 🤖📖
Its interface is as minimalist as possible: only the necessary reading and navigation functions. 📉🧭 You can also manage your library through folders. 📁✨
The key feature is that it breaks an EPUB into chapters and displays the content one chapter at a time. 🔓📄
This makes it easy to copy the needed part of the book and pass it to a large model for analysis or discussion. 📋🔄 It significantly improves the reading experience when paired with AI. 🚀🧠
And it's very easy to get started - just run two commands via uv. ⚡🛠️ As a result, it's an excellent tool for those who love reading and want to use AI as a companion for text analysis. 📚🤝🤖
📁 Language: #Python 61.0%
⭐️ Stars: 1.5k
➡️ Link to GitHub https://github.com/karpathy/reader3
#AI #Python #Reader3 #Tech #BookLovers #Github
/channel/CodeProgrammer ✅
🙏💸 500$ FOR THE FIRST 500 WHO JOIN THE CHANNEL! 🙏💸
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🔖 Interactive textbook on probability theory and statistics 📊✨
A super-intuitive site where you can visually study distributions, sampling, and statistical concepts. 📈🎲
No tons of formulas and boring theory — everything is demonstrated through interactive examples and simulations. 💻🔬
⛓️ Download here 👇
https://seeing-theory.brown.edu/
#Probability #Statistics #DataScience #Learning #Interactive #Math
/channel/CodeProgrammer
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Automate research with NotebookLM + Python 🤖🐍
Notebooklm-py — is a unofficial library for working with Google NotebookLM,📚🧠 which allows automating research tasks, generating content, and connecting AI agents. Suitable for prototypes, pet projects, and personal tools — works both via Python and CLI ⌨️
What it can do:
• integration with AI agents and Claude Code 🤖
• automatic import and processing of sources 📥
• generation of podcasts, videos, and educational materials 🎙️🎥📖
• working via Python API and command line 💻
• using unofficial Google APIs 🔧
https://github.com/teng-lin/notebooklm-py
/channel/CodeProgrammer 📱
📚 AI / ML / DL Learning Resources Hub 🤖🧠
A structured, end-to-end roadmap to master AI — from fundamentals to cutting-edge research. 🚀
A carefully curated, all-in-one repository designed to help Computer Science students, AI enthusiasts, and professionals 👩💻👨💻 who want to build strong foundations and progress confidently from beginner to advanced levels 📈. This hub brings together the high-quality books 📖, courses 🎓, playlists 🎵, research papers 📝, tools 🛠, and learning roadmaps 🗺 covering: Artificial Intelligence, Machine Learning, Deep Learning, Data Science 📊, Transformers, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and MLOps 🔄, all organized in a clear, practical, and industry-relevant manner.
The resources are selected to balance theory 🧠, intuition 💡, and real-world application 🌍, allowing learners to follow modules sequentially or in parallel ⏳ based on their goals.
⭐️ Recommended resources highlight high-impact content widely used in academia 🏛, research 🔬, and industry 🏭, ensuring you focus on what truly matters in modern AI.
🆘 Repo: https://github.com/bishwaghimire/ai-learning-roadmaps
By: /channel/CodeProgrammer
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. 🧑🎓
📂 Repo: https://github.com/TruongDat05/machine-learning-notes-and-code
/channel/CodeProgrammer 📁
🔥 Convolutional Neural Networks: Clearly explained!
🖼 Convolutional Neural Networks (CNNs): CNNs belong to the deep learning methods with layers like convolutional, pooling, and fully-connected layers that transform input images for recognition.
➡️ Feedforward Process: Data flows from input to output layers. Images undergo convolution operations, ReLu activation, and Max-Pooling to reduce size and enhance translation and scaling invariance. Finally, data is classified through a fully connected network.
🔄 Training Process: The training involves batches, backpropagation, and gradient descent to minimize errors. The weights start with random values and are updated through backpropagation. This cycle repeats until accuracy is achieved.
📊 Use Cases: CNNs excel in processing images, videos, and audio for tasks like classification, segmentation, and object detection.
⚠️ Limitations: While CNNs handle translation and scaling well, they struggle with rotation invariance.
Want to learn more about CNNs?
Then, check out super-detailed article about it. 👇
https://lnkd.in/eyA_DnYj
/channel/CodeProgrammer 🧠
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
/channel/CodeProgrammer 🐍
Stop asking "CNN or VLM?" — the answer is both. 🤔
Everyone's talking about Vision Language Models replacing traditional computer vision. 📢
Here's the reality: they're not replacing anything. They're expanding what's possible. 🚀
CNNs are excellent at precise perception — detecting, localizing, classifying fixed objects at high speed and low cost. 🎯
Vision Language Models are better at interpretation — answering open-ended questions about a scene that you can't define as fixed labels in advance. 🧠
The smartest production systems combine both:
→ A lightweight CNN runs first (fast, cheap) ⚡️
→ A VLM handles the complex reasoning (flexible, expensive) 💎
This is the difference between giving machines eyes 👁 vs giving them the ability to talk about what they see. 🗣
Dr. Satya Mallick breaks it down in under 2 minutes. 👇
#ComputerVision #AI #MachineLearning #VisionLanguageModel #DeepLearning #OpenCV #AIEngineering
/channel/CodeProgrammer ✅
Algorithms by Jeff Erickson - one of the best algorithm books out there 📚.
The illustrations make complex concepts surprisingly easy to follow 🎨. Highly recommend this 👍.
Link: https://jeffe.cs.illinois.edu/teaching/algorithms/ 🔗
/channel/MachineLearning9
Hugging Face has literally gathered all the key "secrets". 🤔
It's important to understand the evaluation of large language models. 📊
While you're working with language models:
> training or retraining your models, 🔄
> selecting a model for a task, 🎯
> or trying to understand the current state of the field, 🌍
the question almost inevitably arises:
how to understand that a model is good? ❓
The answer is quality evaluation. It's everywhere:
> leaderboards with model ratings, 🏆
> benchmarks that supposedly measure reasoning, 🧠
> knowledge, coding or mathematics, 👨💻
> articles with claimed new best results. 📈
But what is evaluation actually? 🤷♂️
And what does it really show? 🔍
This guide helps to understand everything. 📚
https://huggingface.co/spaces/OpenEvals/evaluation-guidebook#what-is-model-evaluation-about
What is model evaluation all about 🤖
Basic concepts of large language models for understanding evaluation 🏗️
Evaluation through ready-made benchmarks 📏
Creating your own evaluation system 🔧
The main problem of evaluation ⚠️
Evaluation of free text 📝
Statistical correctness of evaluation 📉
Cost and efficiency of evaluation 💰
/channel/CodeProgrammer 🟢
Most AI engineers never fully understood the maths behind what they build! 🤯🧮
This is an open, unconventional textbook covering maths, CS, and AI from the ground up, written for curious practitioners who want to deeply understand the field, not just survive an interview. 📘✨
Over 7 years of AI/ML experience distilled into intuition-first, no hand-waving explanations that connect the concepts in a way that actually sticks. 🧠🔗
What it covers:
- Vectors, linear algebra, calculus, and optimization 📐📉
- Classical machine learning and deep learning 🤖
- Transformer architectures and LLMs 🦄
- Efficient architectures, quantization, and distillation ⚡️
- CUDA, GPU programming, and SIMD 🚀
- AI inference and deployment 🌐
Ships with an MCP server so Claude Code, Cursor, and any MCP-compatible agent can use the compendium as a live knowledge base during development. You only need elementary maths and basic Python to start. 🐍🏗
Repo: https://github.com/HenryNdubuaku/maths-cs-ai-compendium 🔗
/channel/CodeProgrammer
🔖 3 websites with tasks for improving ML skills
A good selection for those who want to improve their skills in practice, rather than just reading theory:
▶️ Deep-ML — a complete stack from matrices to neural networks;
▶️ Tensorgym — practical exercises in ML;
▶️ NeetCode ML — the ML section from the authors of a well-known platform for preparing for interviews.
tags: #ML #DataScience #DataAnalysis
➡ /channel/CodeProgrammer
Here are the 25 ML feature engineering techniques
/channel/CodeProgrammer