💠 The Best Tool for Extracting Data from PDF Files!

👩🏻‍💻 Usually, PDF files like financial reports, scientific articles, or data analyses are full of tables, formulas, and complex texts.

⬅️ Most tools only extract texts and destroy the data structure, causing important information to be lost.

✅ But the tool Docling uses artificial intelligence to preserve all those structures (text, tables, formulas) exactly as they are in the file. Then it converts that data into a structured format. Meaning AI models can work on them.

⭕ The interesting point is that with just three lines of Python code, you can convert any PDF into searchable data!

┌ 🥵 Docling
├ 🔎 Article
├ 📄 Documentation
└ 🐱 GitHub-Repos

🌐 #Data_Science #DataScience
➖➖➖➖➖➖➖➖➖➖➖➖

⚙️ This tool is turning the world of Web Scraping upside down!

👨🏻‍💻 A new tool called Crawl4AI has been introduced that makes Web Scraping and data extraction from websites much easier, faster, and smarter! Especially designed for use in AI models like ChatGPT and similar tools.

1⃣ Its special features:

🔹 Completely free and open-source. That means you can use it however you want without any cost.

🔹 Works much faster than paid tools.

🔹 Its outputs are AI-friendly, such as JSON, HTML, or Markdown.

🔹 Can extract data from multiple websites simultaneously.

🔹 Collects images, videos, and audio from pages as well.

🔹 Extracts all internal and external links for you.
                   ➖ ➖ ➖

🔢 More advanced features:

🔹 Takes screenshots of pages and collects metadata (like noscript, denoscription, tags).

🔹 You can write custom code or special settings like auth and headers.

🔹 You can even change its browser User-Agent to behave like a human.

🔹 Before starting extraction, it can run your custom JavaScript code.

┌ ♦️ Crawl4AI
└ 🐱 GitHub Repos

🌐 #DataScience #DataScience

https://news.1rj.ru/str/CodeProgrammer

Question:
What is type hinting in Python, and how does it enhance code quality?

Answer: 👉 @DataScienceQ

☄️ Top 12 YouTube Channels to Learn Python

💐 Python will include 57% of data scientist job ads in 2024 . It is still the number one programming language for data scientists.

✅ Now, if you are looking for the best resources to improve your Python skills, after searching and reviewing various resources, I have prepared a list of 12 top channels that provide first-class Python training, which can turn beginners into professional Python programmers. convert


🎬 Python Programmer channel
┤ 📈 211 videos / 465K SUB
┘ 🔴 Link: Python Programmer


🎬 Luke Barousse channel
┤ 📈 157 videos / 429K SUB
┘ 🔴 Link: Luke Barousse


🎬 codebasics channel
┤ 📈 837 videos / 990K SUB
┘ 🔴 link: codebasics


🎬 StatQuest channel with Josh Starmer
┤ 📈 271 videos / 1.14M SUB
┘ 🔴 Link: StatQuest with Josh Starmer


🎬 Sundas Khalid channel
┤ 📈 143 videos / 203K SUB
┘ 🔴 Link: Sundas Khalid


🎬 Shashank Kalanithi channel
┤ 📈 152 videos / 148K SUB
┘ 🔴 Link: Shashank Kalanithi


🎬 Programming with Mosh channel
┤ 📈 203 videos / 3.85M SUB
┘ 🔴 Link: Programming with Mosh


🎬 Corey Schafer channel
┤ 📈 233 videos / 129K SUB
┘ 🔴 Link: Corey Schafer


🎬 sentdex channel
┤ 📈 1254 videos / 1.3M SUB
┘ 🔴 link: sentdex


🎬 Patrick Loeber channel
┤ 📈 206 videos / 264K SUB
┘ 🔴 Link: Patrick Loeber


🎬 Socratica channel
┤ 📈 659 videos / 876K SUB
┘ 🔴 Link: Socratica


🎬 Tech With Tim channel
┤ 📈 983 videos / 1.48M SUB
┘ 🔴 Link: Tech With Tim

😠 More likes 😠 ➡️ more posts
✈️ http://t.me/codeprogrammer ✅

Instant geodata visualization from the command line

Now you can interactively view raster and vector layers without launching a desktop GIS or Jupyter.

Just run:

pip install "leafmap[viewer]"

Then visualize data with a single command:

view-raster /path/to/raster.tif
view-vector /path/to/vector.geojson

Need to customize the display:

view-raster /path/to/raster.tif --band 1 --colormap coolwarm
view-vector /path/to/vector.geojson --style liberty

These CLI utilities are based on Leafmap, MapLibre, and LocalTileserver and support all formats compatible with rasterio and geopandas.

See here: https://github.com/opengeos/leafmap

👉 @codeprogrammer

🔗 Keras vs. TensorFlow vs. PyTorch: The ultimate showdown for deep learning supremacy! 🚀

🤔 Keras: The user-friendly champion! Perfect for beginners and rapid prototyping.

⚡️ TensorFlow: The powerhouse! Great for complex projects with extensive capabilities.

🔥 PyTorch: The flexible innovator! With its dynamic computation graph, it’s a favorite among researchers.

👉 @codeprogrammer

Free course on learning deep learning concepts

A conceptual and architectural journey through computer vision models in #deeplearning, tracing the evolution from LeNet and AlexNet to ResNet, EfficientNet, and Vision Transformers.

The #course explains the design principles behind skip connections, bottleneck blocks, identity preservation, depth/width trade-offs, and attention.

Each chapter combines clear illustrations, historical context, and side-by-side comparisons to show why architectures look the way they do and how they process information.

Grab it on YouTube
https://youtu.be/tfpGS_doPvY?si=1L_NvEm3Lwpj_Jgl

👉 @codeprogrammer

PyTorch 2.9 has been released, an update focused on performance, portability, and developer convenience.

The fresh version brings a stable libtorch ABI for C++/CUDA extensions, symmetric memory for multi-GPU kernels, extended wheel package support for ROCm, XPU, and #CUDA 13, as well as improvements for Intel, Arm, and x86 platforms.

The release includes 3216 commits from 452 contributors, and #PyTorch 2.9 continues to develop the open source #AI ecosystem worldwide.

Full analysis: https://hubs.la/Q03NNKqW0

👉 @codeprogrammer

Question: What are Python set comprehensions?

Answer:Set comprehensions are similar to list comprehensions but create a set instead of a list. The syntax is:

{expression for item in iterable if condition}

For example, to create a set of squares of even numbers:

squares_set = {x**2 for x in range(10) if x % 2 == 0}

This will create a set with the values

{0, 4, 16, 36, 64}

https://news.1rj.ru/str/DataScienceQ 🌟

AI Engineering roadmap that beginners can actually follow. Everything is based on 100% free, open-source, and community resources

All resources can be found here: GitHub

👉  @codeprogrammer

🖥 Want your Telegram bot to respond using ChatGPT?

Do it in a couple of minutes: just install the python-telegram-bot library, add your #OpenAI #API key and bot token, and the bot will start replying to all messages using #ChatGPT.

from telegram import Update
from telegram.ext import ApplicationBuilder, MessageHandler, filters, ContextTypes
from openai import OpenAI


Specify your keys
OPENAI_API_KEY = "sk-..."  
TELEGRAM_TOKEN = "123456789:ABC..."

client = OpenAI(api_key=OPENAI_API_KEY)

async def handle_message(update: Update, context: ContextTypes.DEFAULT_TYPE):
    user_text = update.message.text

    response = client.chat.completions.create(
        model="gpt-4o-mini",
        messages=[{"role": "user", "content": user_text}]
    )

    await update.message.reply_text(response.choices[0].message.content)

app = ApplicationBuilder().token(TELEGRAM_TOKEN).build()
app.add_handler(MessageHandler(filters.TEXT & ~filters.COMMAND, handle_message))
app.run_polling()

https://news.1rj.ru/str/CodeProgrammer 👍

🎓 Stanford has released a new course: “Transformers & Large Language Models”

The authors are the Amidi brothers, and three free lectures are already available on YouTube. This is probably one of the most systematic introductory courses on modern LLMs.

Course content:

• Transformers: tokenization, embeddings, attention, architecture
• #LLM basics: Mixture of Experts, decoding types
• Training and fine-tuning: SFT, RL, LoRA
• Model evaluation: LLM/VLM-as-a-judge, best practices
• Tricks: RoPE, attention approximations, quantization
• Reasoning: scaling during training and inference
• Agentic approaches: #RAG, tool calling

If you are already familiar with this topic — it’s a great opportunity to refresh your knowledge and try implementing some techniques from scratch.

https://cme295.stanford.edu/syllabus/

https://news.1rj.ru/str/CodeProgrammer 🌟