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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
💾 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
🎁 23 Years of SPOTO – Claim Your Free IT Certs Prep Kit!
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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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🗂 Building our own mini-Skynet — a collection of 10 powerful AI repositories from big tech companies
1. Generative AI for Beginners and AI Agents for Beginners
Microsoft provides a detailed explanation of generative AI and agent architecture: from theory to practice.
2. LLMs from Scratch
Step-by-step assembly of your own GPT to understand how LLMs are structured "under the hood".
3. OpenAI Cookbook
An official set of examples for working with APIs, RAG systems, and integrating AI into production from OpenAI.
4. Segment Anything and Stable Diffusion
Classic tools for computer vision and image generation from Meta and the CompVis research team.
5. Python 100 Days and Python Data Science Handbook
A powerful resource for Python and data analysis.
6. LLM App Templates and ML for Beginners
Ready-made app templates with LLMs and a structured course on classic machine learning.
If you want to delve deeply into AI or start building your own projects — this is an excellent starting kit.
tags: #github #LLM #AI #ML
➡️ /channel/CodeProgrammer
🚀 Top 25 Machine Learning Architecture Questions (Every ML Engineer Should Know)
Machine Learning isn’t just about training models it’s about designing systems that scale, perform, and survive production.
If you’re preparing for ML interviews, system design rounds, or real-world MLOps work, these are the most important ML Architecture questions you should be comfortable answering
🧠 Core ML Architecture Concepts
1️⃣ What is Machine Learning architecture and why does it matter?
2️⃣ Batch inference vs Real-time inference
3️⃣ What is model serving and common tools used
4️⃣ Data drift: what it is and how to handle it
5️⃣ Feature stores and their role in ML systems
6️⃣ What is MLOps and why it’s critical
⚙️ Training, Optimization & Pipelines
7️⃣ Training vs fine-tuning
8️⃣ Regularization techniques (L1, L2, Dropout, Early stopping)
9️⃣ Model versioning in production
🔟 ML pipelines and workflow automation
1️⃣1️⃣ CI/CD for ML systems
🗄 Data, Embeddings & Databases
1️⃣2️⃣ Choosing the right database for ML
1️⃣3️⃣ What are embeddings and why they’re powerful
1️⃣4️⃣ Handling sensitive data (GDPR, HIPAA, security)
📊 Monitoring, Explainability & Scaling
1️⃣5️⃣ Monitoring tools for ML models
1️⃣6️⃣ Explainability vs Interpretability
1️⃣7️⃣ Horizontal vs Vertical scaling
1️⃣8️⃣ Ensuring reproducibility in ML
1️⃣9️⃣ Factors affecting ML latency
🚢 Deployment & Production Strategies
2️⃣0️⃣ Why Docker/containerization matters
2️⃣1️⃣ GPU-accelerated deployment — when & why
2️⃣2️⃣ A/B testing in ML systems
2️⃣3️⃣ Multi-model deployment strategies
2️⃣4️⃣ Model rollback strategies
2️⃣5️⃣ Designing ML architectures for scalability
Machine Learning in Python (Course Notes)
I just went through an amazing resource on #MachineLearning in #Python by 365 Data Science, and I had to share the key takeaways with you!
Here’s what you’ll learn:
🔘 Linear Regression - The foundation of predictive modeling
🔘 Logistic Regression - Predicting probabilities and classifications
🔘 Clustering (K-Means, Hierarchical) - Making sense of unstructured data
🔘 Overfitting vs. Underfitting - The balancing act every ML engineer must master
🔘 OLS, R-squared, F-test - Key metrics to evaluate your models
/channel/CodeProgrammer || Share 🌐 and Like 👍
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.
🛫 ML Roadmap 2026 — a comprehensive guide to entering ML, LLM, and MLOps
A rather insightful ML roadmap has gone viral on GitHub: within it, the author has compiled a path from a foundation in mathematics, NumPy, and Pandas to LLM, agentic RAG, fine-tuning, MLOps, and interview preparation. The repository indeed includes sections on Karpathy, MCP, RLHF, LoRA/PEFT, and system design for AI interviews.
Conveniently, this isn't just a list of random links, but rather a structured route through the topics:
▶️ Foundations and tools;
▶️ Classic ML;
▶️ LLM and agents;
▶️ Engineering and MLOps;
▶️ Interview preparation.
➡️ GitHub link:
https://github.com/loganthorneloe/ml-roadmap
tags: #ml #llm
➡ /channel/CodeProgrammer
👨🏻💻 6 Free Python Certifications >>>
Python for Beginners -
https://learn.microsoft.com/en-us/shows/intro-to-python-development/
Programming with Python 3. X
https://www.simplilearn.com/free-python-programming-course-skillup
Advanced Python -
https://www.codecademy.com/learn/learn-advanced-python
AI Python for Beginners -
https://www.deeplearning.ai/short-courses/ai-python-for-beginners/
Python Libraries for Data Science -
https://www.simplilearn.com/learn-python-libraries-free-course-skillup
Data Analysis with Python -
https://www.freecodecamp.org/learn/data-analysis-with-python/#data-analysis-with-python-courseLearn more and practice more 👨🏻💻
/channel/CodeProgrammer
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Знайшов цікавий сервіс для розробників — ApplicationHubs.
Це платформа, яка дозволяє запускати повноцінне Linux-середовище розробки у хмарі. Можна створити свій Dev Hub, підключитися через SSH, VSCode Remote або JetBrains Gateway і працювати як на звичайному комп'ютері — тільки без налаштування локального середовища.
Підтримуються Docker-проєкти, будь-які мови та фреймворки.
По суті це персональна cloud development machine, яку можна запустити за кілька секунд.
Зараз відкрито ранній доступ (early access).
👉 https://applicationhubs.com