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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
💻 Collection of cheat sheets on SQL
I've gathered for you short and understandable cheat sheets on the main topics:
▶️ Basics of the SQL language;
▶️ JOINs with clear examples;
▶️ Window functions;
▶️ SQL for data analysis.
An excellent set to refresh your knowledge before a job interview or quickly recall the syntax.
tags: #sql #useful
/channel/DataAnalyticsX
🗂 20 free MIT courses — the entire Computer Science base in one place
#MIT has made courses in key CS areas publicly available. #Python, #algorithms, #ML, neural networks, #OS, #databases, #mathematics — all can be completed for free directly on #YouTube.
▶️ Introduction to Python Programming
▶️ Data Structures and Algorithms
▶️ Mathematics for Computer Science
▶️ Machine Learning
▶️ Deep Learning
▶️ Artificial Intelligence
▶️ Machine Learning in Healthcare
▶️ Database Management Systems
▶️ Operating Systems
▶️ One-Variable Calculus
▶️ Many-Variable Calculus
▶️ Introduction to Probability Theory
▶️ Statistics
▶️ Probability Theory and Statistics
▶️ Linear Algebra
▶️ Matrix Calculus for Machine Learning
▶️ Java Programming
▶️ Design and Analysis of Algorithms
▶️ Advanced Data Structures
▶️ Introduction to Computational Thinking
tags: #courses
➡/channel/python53
RAG won't work in 2026 if you're still using old approaches.
Yes, many companies are still failing with RAG — not because they're doing it wrong, but because they're stuck on outdated techniques.
Here's what usually happens: most companies start with a chatbot / chat app when talking about AI implementation. And here RAG becomes key — to connect their data via a database and enable the chat app to retrieve relevant documents.
But today, RAG is no longer limited to just chats. The applications of RAG are practically limitless, and that's a good thing.
RAG still remains the foundation for everything you build on LLMs and AI agents. The only thing that's changed is the RAG techniques themselves. The old approach no longer works — more advanced techniques are needed, what's now called advanced RAG.
The essence of RAG is to enrich the system with your data via a database so it can find relevant documents or their parts. The results are simple and often "okay", especially if the documents are well-structured and there aren't many of them.
But when the documents are unstructured and it's important to get not just accurate documents but also the right context, advanced techniques come into play:
- query decomposition
- metadata enrichment
- hybrid indexing
- reranking
- context fusion
These approaches allow the RAG system to find and generate more accurate and contextually relevant answers.
Therefore, advanced RAG is important. RAG isn't dead and can't die. Just use smarter techniques.
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Classical filters & convolution: The heart of computer vision
Before Deep Learning exploded onto the scene, traditional computer vision centered on filters. Filters were small, hand-engineered matrices that you convolved with an image to detect specific features like edges, corners, or textures. In this article, we will dive into the details of classical filters and convolution operation - how they work, why they matter, and how to implement them.
More: https://www.vizuaranewsletter.com/p/classical-filters-and-convolution
📱 Python enthusiasts, this is for you — 15 BEST REPOSITORIES on GitHub for learning Python
▶️ Awesome Python — https://github.com/vinta/awesome-python
— the largest and most authoritative collection of frameworks, libraries, and resources for Python — a must-save
▶️ TheAlgorithms/Python — https://github.com/TheAlgorithms/Python
— a huge collection of algorithms and data structures written in Python
▶️ Project-Based-Learning — https://github.com/practical-tutorials/project-based-learning
— learning Python (and not only) through real projects
▶️ Real Python Guide — https://github.com/realpython/python-guide
— a high-quality guide to the Python ecosystem, tools, and best practices
▶️ Materials from Real Python — https://github.com/realpython/materials
— a collection of code and projects for Real Python articles and courses
▶️ Learn Python — https://github.com/trekhleb/learn-python
— a reference with explanations, examples, and exercises
▶️ Learn Python 3 — https://github.com/jerry-git/learn-python3
— a convenient guide to modern Python 3 with tasks
▶️ Python Reference — https://github.com/rasbt/python_reference
— cheat sheets, scripts, and useful tips from one of the most respected Python authors
▶️ 30-Days-Of-Python — https://github.com/Asabeneh/30-Days-Of-Python
— a 30-day challenge: from syntax to more complex topics
▶️ Python Programming Exercises — https://github.com/zhiwehu/Python-programming-exercises
— 100+ Python tasks with answers
▶️ Coding Problems — https://github.com/MTrajK/coding-problems
— tasks on algorithms and data structures, including for preparation for interviews
▶️ Projects — https://github.com/karan/Projects
— a list of ideas for pet projects (not just Python). Great for practice
▶️ 100-Days-Of-ML-Code — https://github.com/Avik-Jain/100-Days-Of-ML-Code
— machine learning in Python in the format of a challenge
▶️ 30-Seconds-of-Python — https://github.com/30-seconds/30-seconds-of-python
— useful snippets and tricks for everyday tasks
▶️ Geekcomputers/Python — https://github.com/geekcomputers/Python
— various scripts: from working with the network to automation tasks
React ♥️ for more posts like this 💛
📱 A huge cheat sheet for Python, Django, Plotly, Matplotlib, Pygame
Many topics are covered inside:
🔸 All basic constructs: variables, conditions, loops, lists, dictionaries, functions, and classes — with clear examples;
🔸 Working with files, exceptions, and data input — understandable even for beginners;
🔸 #Django, #Pygame, #Matplotlib, and #Plotly — brief instructions on how to get started with each of the frameworks;
🔸 Tips on #Git, project structure, and unit testing.
/channel/CodeProgrammer ❤️
⚡️ Colorizing old black-and-white videos and "bringing faces to life" for FREE
SVFR — a full-fledged framework for restoring faces in videos.
It can:
💬 BFR — improve blurry faces.
💬 Colorization — colorize black-and-white videos.
💬 Inpainting — redraw damaged areas.
💬 and combine all of this in one pass.
Essentially, the model takes old or damaged videos and makes them "as if they were shot yesterday". And it's free and open-source.
⚙️ Installation locally:
1. Create an environment
conda create -n svfr python=3.9 -y
conda activate svfr
pip install torch==2.2.2 torchvision==0.17.2 torchaudio==2.2.2
pip install -r requirements.txt
conda install git-lfs
git lfs install
git clone https://huggingface.co/stabilityai/stable-video-diffusion-img2vid-xt models/stable-video-diffusion-img2vid-xt
python infer.py \
--config config/infer.yaml \
--task_ids 0 \
--input_path input.mp4 \
--output_dir results/ \
--crop_face_region
0 — face enhancement1 — colorization2 — redrawing damage
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Do you want to understand the methods used to train LLMs?
The training of large language models (LLMs) is based on various approaches that help models understand and generate text.
Each method shapes the learning process in its own way - from predicting the next word to classifying entire sentences or labeling entities.
Here are 4 common methods of training LLMs in simple language 👇
1. Causal Language Modeling
Predicts the next word in a sequence based on the previous ones. Helps the model master the natural flow of speech and the structure of sentences.
Analogy: how to finish a sentence for another person by guessing the next word.
2. Masked Language Modeling
Learns by guessing the missing words in a sentence based on the surrounding context. Improves the overall understanding of language.
Analogy: how to solve tasks with missing words.
3. Text Classification Modeling
Determines the general class of a sentence (for example, tone or topic) by comparing predictions with actual labels.
Analogy: how to sort letters into folders "Work", "Personal", or "Promotions".
4. Token Classification Modeling
Assigns labels to each word or subword - for example, highlights names, places, or dates in the text.
Analogy: how to highlight words with different colors - names in blue, places in green, dates in yellow.
These methods form the basis of modern LLMs, and each of them plays a role in making AI smarter and more useful.
/channel/CodeProgrammer
The Python + Generative AI series by Azure AI Foundry has ended, but all materials are open
Now you can calmly rewatch the recordings, download the slides, and try the code from each session — from LLM and RAG to AI agents and MCP.
All resources are here: aka.ms/pythonai/resources
👉 @codeprogrammer
🤖 Python libraries for AI agents — what to study
If you want to develop AI agents in Python, it's important to understand the order of studying libraries.
Start with LangChain, CrewAI or SmolAgents — they allow you to quickly assemble simple agents, connect tools, and test ideas.
The next level is LangGraph, LlamaIndex and Semantic Kernel. These tools are already used for production systems: RAG, orchestration, and complex workflows.
The most complex level is AutoGen, DSPy and A2A. They are needed for autonomous multi-agent systems and optimizing LLM pipelines.
LangChain — simple agents, tools, and memory
github.com/langchain-ai/langchain
CrewAI — multi-agent systems with roles
github.com/joaomdmoura/crewAI
SmolAgents — lightweight agents for quick experiments
github.com/huggingface/smolagents
LangGraph — orchestration and stateful workflow
github.com/langchain-ai/langgraph
LlamaIndex — RAG and knowledge-agents
github.com/run-llama/llama_index
Semantic Kernel — AI workflow and plugins
github.com/microsoft/semantic-kernel
AutoGen — autonomous multi-agent systems
github.com/microsoft/autogen
DSPy — optimizing LLM pipelines
github.com/stanfordnlp/dspy
A2A — protocol for interaction between agents
github.com/a2aproject/A2A
/channel/CodeProgrammer 🌟
CNN vs Vision Transformer — The Battle for Computer Vision 👁⚡️
Two architectures. One goal: identify the cat. But they see things differently:
🧠 CNN (Convolutional Neural Network)
· Scans the image with filters
· Detects local patterns first (edges → textures → shapes)
· Builds understanding layer by layer
🔄 Vision Transformer (ViT)
· Splits image into patches (like words in a sentence)
· Detects global patterns from the start
· Sees the whole picture using attention mechanisms
Same input. Same output. Different journey.
CNNs think locally and build up.
Transformers think globally from the get-go.
Which one wins? Depends on the task — but both are shaping the future of how machines see.
/channel/CodeProgrammer
🚀 𝐓𝐎𝐏 𝐑𝐀𝐆 𝐈𝐍𝐓𝐄𝐑𝐕𝐈𝐄𝐖 𝐐𝐔𝐄𝐒𝐓𝐈𝐎𝐍𝐒 𝐀𝐍𝐃 𝐀𝐍𝐒𝐖𝐄𝐑𝐒
🔹 Advanced #RAG engineering concepts
• Multi-stage retrieval pipelines
• Agentic RAG vs classical RAG
• Latency optimization
• Security risks in enterprise RAG systems
• Monitoring and debugging production RAG systems
📄 𝐓𝐡𝐞 𝐏𝐃𝐅 𝐜𝐨𝐧𝐭𝐚𝐢𝐧𝐬 𝟒𝟎 𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞𝐝 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬 𝐰𝐢𝐭𝐡 𝐜𝐥𝐞𝐚𝐫 𝐞𝐱𝐩𝐥𝐚𝐧𝐚𝐭𝐢𝐨𝐧𝐬 𝐭𝐨 𝐡𝐞𝐥𝐩 𝐲𝐨𝐮 𝐮𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝 𝐛𝐨𝐭𝐡 𝐜𝐨𝐧𝐜𝐞𝐩𝐭𝐬 𝐚𝐧𝐝 𝐬𝐲𝐬𝐭𝐞𝐦 𝐝𝐞𝐬𝐢𝐠𝐧 𝐭𝐡𝐢𝐧𝐤𝐢𝐧𝐠.
/channel/CodeProgrammer
Python Tip: Operator Overloading
This is a very important concept in Python.
Have you ever wondered how #Python understands what the + operator means? For numbers, it's addition; for strings, it's concatenation; for lists, it's union. This is operator overloading in action.
Operator overloading means defining special behavior for operators (+, -, *, ==, etc.) in your user-defined classes. You determine how these operators should work with your objects.
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🐼 Cheat Sheet on Data Wrangling — for everyone who works with Pandas
Everything you need is collected in one file: creating and merging DataFrames, filtering, grouping, handling missing values, and visualization.
It's convenient when you need to quickly refresh your syntax and don't want to dig into the documentation.
The cheat sheet in good quality
https://pandas.pydata.org/Pandas_Cheat_Sheet.pdf
tags: #useful
For more please ❤️
➡ /channel/CodeProgrammer
🚀 New Edge for Polymarket Traders: Oracle Lag Sniper
A high-performance, open-source strategy repo is making waves right now among serious Polymarket users: the Oracle Lag Sniper.
📈 Why it’s worth your attention:
• Exploits oracle timing inefficiencies
• Built for fast execution & precise entries
• Fully open-source, inspect, modify, and run it yourself
🔗 Check out the repo here:
Oracle Lag Sniper GitHub
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📊 Don’t just follow the market, get ahead of it.
💾 TensorTonic — a platform with 200+ algorithms and tasks for ML developers
What's inside:
▶️ Analysis of research and step-by-step reproduction of model architectures;
▶️ Explanation of topics and concepts with interactive visualizations;
▶️ A progress and achievement system — what would we do without gamification.
A great option to hone your ML skills in the evening ⚡
https://www.tensortonic.com/
🗂 Harvard has released a textbook on ML systems — from autograd to production
Not just another "what is a neural network" course — this is about how to build combat-ready ML systems around models.
What's inside:
▶️ Building autograd, optimizers, attention, and mini-PyTorch from scratch;
▶️ Batches, computational accuracy, architectures, and training;
▶️ Performance optimization, hardware acceleration, and benchmarking.
You can read the book and the code for free right now.
✈️ Link to GitHub
https://github.com/harvard-edge/cs249r_book
Visualizing the complexity of algorithms
/channel/CodeProgrammer ✅
📱 TorchCode — a PyTorch training tool for preparing for ML interviews
40 tasks for implementing operators and architectures that are actually asked in interviews. Automatic checking, hints, and reference solutions — all in the browser without installation.
If you're preparing for an ML interview, it's useful to go through at least half of them.
Link: https://github.com/duoan/TorchCode
tags: #useful #pytorch
/channel/CodeProgrammer ✅
𝐕𝐢𝐬𝐮𝐚𝐥 𝐛𝐥𝐨𝐠 on Vision Transformers is live.
https://vizuaranewsletter.com/p/vision-transformers?r=5b5pyd&utm_campaign=post&utm_medium=web
Learn how ViT works from the ground up, and fine-tune one on a real classification dataset.
CNNs process images through small sliding filters. Each filter only sees a tiny local region, and the model has to stack many layers before distant parts of an image can even talk to each other.
Vision Transformers threw that whole approach out.
ViT chops an image into patches, treats each patch like a token, and runs self-attention across the full sequence.
Every patch can attend to every other patch from the very first layer. No stacking required.
That global view from layer one is what made ViT surpass CNNs on large-scale benchmarks.
𝐖𝐡𝐚𝐭 𝐭𝐡𝐞 𝐛𝐥𝐨𝐠 𝐜𝐨𝐯𝐞𝐫𝐬:
- Introduction to Vision Transformers and comparison with CNNs
- Adapting transformers to images: patch embeddings and flattening
- Positional encodings in Vision Transformers
- Encoder-only structure for classification
- Benefits and drawbacks of ViT
- Real-world applications of Vision Transformers
- Hands-on: fine-tuning ViT for image classification
The Image below shows
Self-attention connects every pixel to every other pixel at once. Convolution only sees a small local window. That's why ViT captures things CNNs miss, like the optical illusion painting where distant patches form a hidden face.
The architecture is simple. Split image into patches, flatten them into embeddings (like words in a sentence), run them through a Transformer encoder, and the class token collects info from all patches for the final prediction. Patch in, class out.
Inside attention: each patch (query) compares itself to all other patches (keys), softmax gives attention weights, and the weighted sum of values produces a new representation aware of the full image, visualizes what the CLS token actually attends to through attention heatmaps.
The second half of the blog is hands-on code. I fine-tuned ViT-Base from google (86M params) on the Oxford-IIIT Pet dataset, 37 breeds, ~7,400 images.
𝐁𝐥𝐨𝐠 𝐋𝐢𝐧𝐤
https://vizuaranewsletter.com/p/vision-transformers?r=5b5pyd&utm_campaign=post&utm_medium=web
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PhD Students - Do you need datasets for your research?
Here are 30 datasets for research from NexData.
Use discount code for 20% off: G5W924C3ZI
1. Korean Exam Question Dataset for AI Training
https://lnkd.in/d_paSwt7
2. Multilingual Grammar Correction Dataset
https://lnkd.in/dV43iqTp
3. High quality video caption dataset
https://lnkd.in/dY9kxkhx
4. 3D models and scenes datasets for AI and simulation
https://lnkd.in/dT-zscH4
5. Image editing datasets – object removal, addition & modification
https://lnkd.in/dd8iCGMS
6. QA dataset – visual & text reasoning
https://lnkd.in/dc3TNWFD
7. English instruction tuning dataset
https://lnkd.in/dTeTgd2M
8. Large scale vision language dataset for AI training
https://lnkd.in/dBJuxazN
9. News dataset
https://lnkd.in/dYBJe5gd
10. Global building photos dataset
https://lnkd.in/dVJsDXnC
11. Facial landmarks dataset
https://lnkd.in/dz_KGCS4
12. 3D Human Pose & Landmarks dataset
https://lnkd.in/dXE9ir8Z
13. 3D Hand Pose & Gesture Recognition dataset
https://lnkd.in/d_QdGGb9
14. 14. Driver monitoring dataset – dangerous, fatigue
https://lnkd.in/d6kF-9PW
15. Japanese handwriting OCR dataset
https://lnkd.in/dHnriqrH
16. American English Male voice TTS dataset
https://lnkd.in/dqyvg862
17. Riddles and brain teasers dataset
https://lnkd.in/dKBHY3DE
18. Chinese test questions text
https://lnkd.in/dQpUd8xC
19. Chinese medical question answering data
https://lnkd.in/dsbWUCpz
20. Multi-round interpersonal dialogues text data
https://lnkd.in/dQiUq_Jg
21. Human activity recognition dataset
https://lnkd.in/dHM52MfV
22. Facial expression recognition dataset
https://lnkd.in/dqQAfMau
23. Urban surveillance dataset
https://lnkd.in/dc2RCnTk
24. Human body segmentation dataset
https://lnkd.in/d6sSrDxS
25. Fashion segmentation – clothing & accessories
https://lnkd.in/dptNUTz8
26. Fight video dataset – action recognition
https://lnkd.in/dnY_m5hZ
27. Gesture recognition dataset
https://lnkd.in/dFVPivYg
28. Facial skin defects dataset
https://lnkd.in/dKCbUvU6
29. Smoke detection and behaviour recognition dataset
https://lnkd.in/ddGg56R4
30. Weight loss transformation video dataset
https://lnkd.in/dqqT4ed9
/channel/CodeProgrammer 👾
How a CNN sees images simplified 🧠
1. Input → Image breaks into pixels (RGB numbers)
2. Feature Extraction
· Convolution → Detects edges/patterns
· ReLU → Kills negatives, adds non-linearity
· Pooling → Shrinks data, keeps what matters
3. Fully Connected → Flattens features into meaning
4. Output → Probability scores: Cat? Dog? Car?
Why powerful: Learns hierarchically — edges → shapes → objects
Pixels to predictions. That's it. 👇
#DeepLearning #CNN #ComputerVision #AI
/channel/CodeProgrammer
Horizon Lab 🔭 Джеймс Вебб знаходить галактики, яких не мало б існувати за нашими моделями. Hubble бачить зірки, що вибухнули мільярди років тому. Пишемо про це щодня — українською, на основі наукових публікацій.
👉 http://t.me/horizonlab_space
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