📌 Project name: Cat/Dog/Fox Lightning 2024

📝 Language: #Python

🔗 Dataset Link: https://www.kaggle.com/datasets/snmahsa/animal-image-dataset-cats-dogs-and-foxes

🖋 Download the dataset offline:
https://yangx.top/datasets1/668

New to Pandas?

Here's a cheat sheet you can download (2025)

#Pandas #Python #DataAnalysis #PandasCheatSheet #PythonForDataScience #LearnPandas #DataScienceTools #PythonLibraries #FreeResources #DataManipulation

✉️ Our Telegram channels: https://yangx.top/addlist/0f6vfFbEMdAwODBk

📱 Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

10 GitHub repos to build a career in AI engineering:

(100% free step-by-step roadmap)

1️⃣ ML for Beginners by Microsoft

A 12-week project-based curriculum that teaches classical ML using Scikit-learn on real-world datasets.

Includes quizzes, lessons, and hands-on projects, with some videos.

GitHub repo → https://lnkd.in/dCxStbYv

2️⃣ AI for Beginners by Microsoft

This repo covers neural networks, NLP, CV, transformers, ethics & more. There are hands-on labs in PyTorch & TensorFlow using Jupyter.

Beginner-friendly, project-based, and full of real-world apps.

GitHub repo → https://lnkd.in/dwS5Jk9E

3️⃣ Neural Networks: Zero to Hero

Now that you’ve grasped the foundations of AI/ML, it’s time to dive deeper.

This repo by Andrej Karpathy builds modern deep learning systems from scratch, including GPTs.

GitHub repo → https://lnkd.in/dXAQWucq

4️⃣ DL Paper Implementations

So far, you have learned the fundamentals of AI, ML, and DL. Now study how the best architectures work.

This repo covers well-documented PyTorch implementations of 60+ research papers on Transformers, GANs, Diffusion models, etc.

GitHub repo → https://lnkd.in/dTrtDrvs

5️⃣ Made With ML

Now it’s time to learn how to go from notebooks to production.

Made With ML teaches you how to design, develop, deploy, and iterate on real-world ML systems using MLOps, CI/CD, and best practices.

GitHub repo → https://lnkd.in/dYyjjBGb

6️⃣ Hands-on LLMs

- You've built neural nets.
- You've explored GPTs and LLMs.

Now apply them. This is a visually rich repo that covers everything about LLMs, like tokenization, fine-tuning, RAG, etc.

GitHub repo → https://lnkd.in/dh2FwYFe

7️⃣ Advanced RAG Techniques

Hands-on LLMs will give you a good grasp of RAG systems. Now learn advanced RAG techniques.

This repo covers 30+ methods to make RAG systems faster, smarter, and accurate, like HyDE, GraphRAG, etc.

GitHub repo → https://lnkd.in/dBKxtX-D

8️⃣ AI Agents for Beginners by Microsoft

After diving into LLMs and mastering RAG, learn how to build AI agents.

This hands-on course covers building AI agents using frameworks like AutoGen.

GitHub repo → https://lnkd.in/dbFeuznE

9️⃣ Agents Towards Production

The above course will teach what AI agents are. Next, learn how to ship them.

This is a practical playbook for building agents covering memory, orchestration, deployment, security & more.

GitHub repo → https://lnkd.in/dcwmamSb

🔟 AI Engg. Hub

To truly master LLMs, RAG, and AI agents, you need projects.

This covers 70+ real-world examples, tutorials, and agent app you can build, adapt, and ship.

GitHub repo → https://lnkd.in/geMYm3b6

#AIEngineering #MachineLearning #DeepLearning #LLMs #RAG #MLOps #Python #GitHubProjects #AIForBeginners #ArtificialIntelligence #NeuralNetworks #OpenSourceAI #DataScienceCareers

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📱 Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A

Let's start at the top...

NumPy contains a broad array of functionality for fast numerical & mathematical operations in Python

The core data-structure within #NumPy is an ndArray (or n-dimensional array)

Behind the scenes - much of the NumPy functionality is written in the programming language C

NumPy functionality is used in other popular #Python packages including #Pandas, #Matplotlib, & #scikitlearn!

✉️ Our Telegram channels: https://yangx.top/addlist/0f6vfFbEMdAwODBk

Topic: Python Script to Convert a Shared ChatGPT Link to PDF – Step-by-Step Guide

---

### Objective

In this lesson, we’ll build a Python script that:

• Takes a ChatGPT share link (e.g., https://chat.openai.com/share/abc123)
• Downloads the HTML content of the chat
• Converts it to a PDF file using pdfkit and wkhtmltopdf

This is useful for archiving, sharing, or printing ChatGPT conversations in a clean format.

---

### 1. Prerequisites

Before starting, you need the following libraries and tools:

#### • Install pdfkit and requests

pip install pdfkit requests

#### • Install wkhtmltopdf

Download from:
[https://wkhtmltopdf.org/downloads.html](https://wkhtmltopdf.org/downloads.html)

Make sure to add the path of the installed binary to your system PATH.

---

### 2. Python Script: Convert Shared ChatGPT URL to PDF

import pdfkit
import requests
import os

# Define output filename
output_file = "chatgpt_conversation.pdf"

# ChatGPT shared URL (user input)
chat_url = input("Enter the ChatGPT share URL: ").strip()

# Verify the URL format
if not chat_url.startswith("https://chat.openai.com/share/"):
    print("Invalid URL. Must start with https://chat.openai.com/share/")
    exit()

try:
    # Download HTML content
    response = requests.get(chat_url)
    if response.status_code != 200:
        raise Exception(f"Failed to load the chat: {response.status_code}")

    html_content = response.text

    # Save HTML to temporary file
    with open("temp_chat.html", "w", encoding="utf-8") as f:
        f.write(html_content)

    # Convert HTML to PDF
    pdfkit.from_file("temp_chat.html", output_file)

    print(f"\n✅ PDF saved as: {output_file}")

    # Optional: remove temp file
    os.remove("temp_chat.html")

except Exception as e:
    print(f"❌ Error: {e}")

---

### 3. Notes

• This approach works only if the shared page is publicly accessible (which ChatGPT share links are).
• The PDF output will contain the web page version, including theme and layout.
• You can customize the PDF output using pdfkit options (like page size, margins, etc.).

---

### 4. Optional Enhancements

• Add GUI with Tkinter
• Accept multiple URLs
• Add PDF metadata (title, author, etc.)
• Add support for offline rendering using BeautifulSoup to clean content

---

### Exercise

• Try converting multiple ChatGPT share links to PDF
• Customize the styling with your own CSS
• Add a timestamp or watermark to the PDF

---

#Python #ChatGPT #PDF #WebScraping #Automation #pdfkit #tkinter

https://yangx.top/CodeProgrammer ✅

🧹 ObjectClear — an AI-powered tool for removing objects from images effortlessly.

⚙️ What It Can Do:

🖼️ Upload any image
🎯 Select the object you want to remove
🌟 The model automatically erases the object and intelligently reconstructs the background

⚡️ Under the Hood:

— Uses Segment Anything (SAM) by Meta for object segmentation
— Leverages Inpaint-Anything for realistic background generation
— Works in your browser with an intuitive Gradio UI

✔️ Fully open-source and can be run locally.

📎 GitHub: https://github.com/zjx0101/ObjectClear

#AI #ImageEditing #ComputerVision #Gradio #OpenSource #Python

python-docx: Create and Modify Word Documents #python

python-docx is a Python library for reading, creating, and updating Microsoft Word 2007+ (.docx) files.

Installation

pip install python-docx

Example

from docx import Document

document = Document()
document.add_paragraph("It was a dark and stormy night.")
<docx.text.paragraph.Paragraph object at 0x10f19e760>
document.save("dark-and-stormy.docx")

document = Document("dark-and-stormy.docx")
document.paragraphs[0].text
'It was a dark and stormy night.'

https://yangx.top/DataScienceN 🚗