Python library RetinaFace for face detection and working with key points (eyes, nose, mouth)

Supports face alignment, easily installed via pip install retina-face, and works based on deep models from the insightface project.

An excellent tool for tasks in computer vision and face recognition.

Usage examples:

from retinaface import RetinaFace

resp = RetinaFace.detect_faces("img1.jpg")
print(resp)

{
    "face_1": {
        "score": 0.9993440508842468,
        "facial_area": [155, 81, 434, 443],
        "landmarks": {
          "right_eye": [257.82974, 209.64787],
          "left_eye": [374.93427, 251.78687],
          "nose": [303.4773, 299.91144],
          "mouth_right": [228.37329, 338.73193],
          "mouth_left": [320.21982, 374.58798]
        }
  }
}

👉 @DataScienceN

Awesome interactive textbook on probability theory and statistics

Inside are clear visualizations, interactive elements, and minimal dry theory. You can tweak distributions, sample datasets, play with confidence intervals, and clearly see how it all works

Get it here, I recommend opening it on a desktop
https://seeing-theory.brown.edu/

👉 @DataScienceM

Great find for developers: free cheat sheets on Deep Learning and PyTorch

A detailed guide to creating and training neural networks - link

Basic principles and practice of working with PyTorch - link

👉 @CODEPROGRAMMER

🖥 Extremely useful collection of 800+ SQL questions frequently asked in interviews.

It also includes tasks for self-study and many examples.

The collection is perfect for those who want to improve their SQL skills, refresh their knowledge, and test themselves.

▪️ GitHub

https://t.iss.one/addlist/8_rRW2scgfRhOTc0 ⚡️

“Learn AI” is everywhere. But where do the builders actually start?
Here’s the real path, the courses, papers and repos that matter.

✅ Videos:

Everything here ⇒ https://lnkd.in/ePfB8_rk

➡️ LLM Introduction → https://lnkd.in/ernZFpvB
➡️ LLMs from Scratch - Stanford CS229 → https://lnkd.in/etUh6_mn
➡️ Agentic AI Overview →https://lnkd.in/ecpmzAyq
➡️ Building and Evaluating Agents → https://lnkd.in/e5KFeZGW
➡️ Building Effective Agents → https://lnkd.in/eqxvBg79
➡️ Building Agents with MCP → https://lnkd.in/eZd2ym2K
➡️ Building an Agent from Scratch → https://lnkd.in/eiZahJGn

✅ Courses:

All Courses here ⇒ https://lnkd.in/eKKs9ves

➡️ HuggingFace's Agent Course → https://lnkd.in/e7dUTYuE
➡️ MCP with Anthropic → https://lnkd.in/eMEnkCPP
➡️ Building Vector DB with Pinecone → https://lnkd.in/eP2tMGVs
➡️ Vector DB from Embeddings to Apps → https://lnkd.in/eP2tMGVs
➡️ Agent Memory → https://lnkd.in/egC8h9_Z
➡️ Building and Evaluating RAG apps → https://lnkd.in/ewy3sApa
➡️ Building Browser Agents → https://lnkd.in/ewy3sApa
➡️ LLMOps → https://lnkd.in/ex4xnE8t
➡️ Evaluating AI Agents → https://lnkd.in/eBkTNTGW
➡️ Computer Use with Anthropic → https://lnkd.in/ebHUc-ZU
➡️ Multi-Agent Use → https://lnkd.in/e4f4HtkR
➡️ Improving LLM Accuracy → https://lnkd.in/eVUXGT4M
➡️ Agent Design Patterns → https://lnkd.in/euhUq3W9
➡️ Multi Agent Systems → https://lnkd.in/evBnavk9

✅ Guides:

Access all ⇒ https://lnkd.in/e-GA-HRh

➡️ Google's Agent → https://lnkd.in/encAzwKf
➡️ Google's Agent Companion → https://lnkd.in/e3-XtYKg
➡️ Building Effective Agents by Anthropic → https://lnkd.in/egifJ_wJ
➡️ Claude Code Best practices → https://lnkd.in/eJnqfQju
➡️ OpenAI's Practical Guide to Building Agents → https://lnkd.in/e-GA-HRh

✅ Repos:
➡️ GenAI Agents → https://lnkd.in/eAscvs_i
➡️ Microsoft's AI Agents for Beginners → https://lnkd.in/d59MVgic
➡️ Prompt Engineering Guide → https://lnkd.in/ewsbFwrP
➡️ AI Agent Papers → https://lnkd.in/esMHrxJX

✅ Papers:
🟡 ReAct → https://lnkd.in/eZ-Z-WFb
🟡 Generative Agents → https://lnkd.in/eDAeSEAq
🟡 Toolformer → https://lnkd.in/e_Vcz5K9
🟡 Chain-of-Thought Prompting → https://lnkd.in/eRCT_Xwq
🟡 Tree of Thoughts → https://lnkd.in/eiadYm8S
🟡 Reflexion → https://lnkd.in/eggND2rZ
🟡 Retrieval-Augmented Generation Survey → https://lnkd.in/eARbqdYE

Access all ⇒ https://lnkd.in/e-GA-HRh

By: https://t.iss.one/CodeProgrammer 🟡

👨🏻‍💻 This Python library helps you extract usable data for language models from complex files like tables, images, charts, or multi-page documents.

📝 The idea of Agentic Document Extraction is that unlike common methods like OCR that only read text, it can also understand the structure and relationships between different parts of the document. For example, it understands which title belongs to which table or image.


✅ Works with PDFs, images, and website links.

☑️ Can chunk and process very large documents (up to 1000 pages) by itself.

✔️ Outputs both JSON and Markdown formats.

☑️ Even specifies the exact location of each section on the page.

✔️ Supports parallel and batch processing.

pip install agentic-doc

┌ 🥵 Agentic Document Extraction
├ 🌎 Website
└ 🐱 GitHub Repos

🌐 #DataScience #DataScience
➖➖➖➖➖➖➖➖➖➖➖➖➖
https://t.iss.one/CodeProgrammer