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
(100% free step-by-step roadmap)
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
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
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
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
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
- 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
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
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
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
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
✉️ Our Telegram channels: https://t.me/addlist/0f6vfFbEMdAwODBk📱 Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
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Forwarded from Data Science Machine Learning Data Analysis Books
mcp guide.pdf.pdf
16.7 MB
A comprehensive PDF has been compiled that includes all MCP-related posts shared over the past six months.
(75 pages, 10+ projects & visual explainers)
Over the last half year, content has been published about the Modular Computation Protocol (MCP), which has gained significant interest and engagement from the AI community. In response to this enthusiasm, all tutorials have been gathered in one place, featuring:
* The fundamentals of MCP
* Explanations with visuals and code
* 11 hands-on projects for AI engineers
Projects included:
1. Build a 100% local MCP Client
2. MCP-powered Agentic RAG
3. MCP-powered Financial Analyst
4. MCP-powered Voice Agent
5. A Unified MCP Server
6. MCP-powered Shared Memory for Claude Desktop and Cursor
7. MCP-powered RAG over Complex Docs
8. MCP-powered Synthetic Data Generator
9. MCP-powered Deep Researcher
10. MCP-powered RAG over Videos
11. MCP-powered Audio Analysis Toolkit
(75 pages, 10+ projects & visual explainers)
Over the last half year, content has been published about the Modular Computation Protocol (MCP), which has gained significant interest and engagement from the AI community. In response to this enthusiasm, all tutorials have been gathered in one place, featuring:
* The fundamentals of MCP
* Explanations with visuals and code
* 11 hands-on projects for AI engineers
Projects included:
1. Build a 100% local MCP Client
2. MCP-powered Agentic RAG
3. MCP-powered Financial Analyst
4. MCP-powered Voice Agent
5. A Unified MCP Server
6. MCP-powered Shared Memory for Claude Desktop and Cursor
7. MCP-powered RAG over Complex Docs
8. MCP-powered Synthetic Data Generator
9. MCP-powered Deep Researcher
10. MCP-powered RAG over Videos
11. MCP-powered Audio Analysis Toolkit
#MCP #ModularComputationProtocol #AIProjects #DeepLearning #ArtificialIntelligence #RAG #VoiceAI #SyntheticData #AIAgents #AIResearch #TechWriting #OpenSourceAI #AI #python
✉️ Our Telegram channels: https://t.me/addlist/0f6vfFbEMdAwODBk📱 Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
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Auto-Encoder & Backpropagation by hand ✍️ lecture video ~ 📺 https://byhand.ai/cv/10
It took me a few years to invent this method to show both forward and backward passes for a non-trivial case of a multi-layer perceptron over a batch of inputs, plus gradient descents over multiple epochs, while being able to hand calculate each step and code in Excel at the same time.
= Chapters =
• Encoder & Decoder (00:00)
• Equation (10:09)
• 4-2-4 AutoEncoder (16:38)
• 6-4-2-4-6 AutoEncoder (18:39)
• L2 Loss (20:49)
• L2 Loss Gradient (27:31)
• Backpropagation (30:12)
• Implement Backpropagation (39:00)
• Gradient Descent (44:30)
• Summary (51:39)
✉️ Our Telegram channels: https://t.me/addlist/0f6vfFbEMdAwODBk
It took me a few years to invent this method to show both forward and backward passes for a non-trivial case of a multi-layer perceptron over a batch of inputs, plus gradient descents over multiple epochs, while being able to hand calculate each step and code in Excel at the same time.
= Chapters =
• Encoder & Decoder (00:00)
• Equation (10:09)
• 4-2-4 AutoEncoder (16:38)
• 6-4-2-4-6 AutoEncoder (18:39)
• L2 Loss (20:49)
• L2 Loss Gradient (27:31)
• Backpropagation (30:12)
• Implement Backpropagation (39:00)
• Gradient Descent (44:30)
• Summary (51:39)
#AIEngineering #MachineLearning #DeepLearning #LLMs #RAG #MLOps #Python #GitHubProjects #AIForBeginners #ArtificialIntelligence #NeuralNetworks #OpenSourceAI #DataScienceCareers
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GPU by hand ✍️ I drew this to show how a GPU speeds up an array operation of 8 elements in parallel over 4 threads in 2 clock cycles. Read more 👇
CPU
• It has one core.
• Its global memory has 120 locations (0-119).
• To use the GPU, it needs to copy data from the global memory to the GPU.
• After GPU is done, it will copy the results back.
GPU
• It has four cores to run four threads (0-3).
• It has a register file of 28 locations (0-27)
• This register file has four banks (0-3).
• All threads share the same register file.
• But they must read/write using the four banks.
• Each bank allows 2 reads (Read 0, Read 1) and 1 write in a single clock cycle.
✉️ Our Telegram channels: https://t.me/addlist/0f6vfFbEMdAwODBk
CPU
• It has one core.
• Its global memory has 120 locations (0-119).
• To use the GPU, it needs to copy data from the global memory to the GPU.
• After GPU is done, it will copy the results back.
GPU
• It has four cores to run four threads (0-3).
• It has a register file of 28 locations (0-27)
• This register file has four banks (0-3).
• All threads share the same register file.
• But they must read/write using the four banks.
• Each bank allows 2 reads (Read 0, Read 1) and 1 write in a single clock cycle.
#AIEngineering #MachineLearning #DeepLearning #LLMs #RAG #MLOps #Python #GitHubProjects #AIForBeginners #ArtificialIntelligence #NeuralNetworks #OpenSourceAI #DataScienceCareers
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What is torch.nn really?
This article explains it quite well.
📌 Read
✉️ Our Telegram channels: https://t.me/addlist/0f6vfFbEMdAwODBk
When I started working with PyTorch, my biggest question was: "What is torch.nn?".
This article explains it quite well.
📌 Read
#pytorch #AIEngineering #MachineLearning #DeepLearning #LLMs #RAG #MLOps #Python #GitHubProjects #AIForBeginners #ArtificialIntelligence #NeuralNetworks #OpenSourceAI #DataScienceCareers
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#DataScience #SQL #Python #MachineLearning #Statistics #BusinessAnalytics #ProductCaseStudies #DataScienceProjects #InterviewPrep #LearnDataScience #YouTubeLearning #CodingInterview #MLInterview #SQLProjects #PythonForDataScience
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NUMPY FOR DS.pdf
4.5 MB
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://t.me/addlist/0f6vfFbEMdAwODBk
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!
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Topic: Handling Datasets of All Types – Part 1 of 5: Introduction and Basic Concepts
---
1. What is a Dataset?
• A dataset is a structured collection of data, usually organized in rows and columns, used for analysis or training machine learning models.
---
2. Types of Datasets
• Structured Data: Tables, spreadsheets with rows and columns (e.g., CSV, Excel).
• Unstructured Data: Images, text, audio, video.
• Semi-structured Data: JSON, XML files containing hierarchical data.
---
3. Common Dataset Formats
• CSV (Comma-Separated Values)
• Excel (.xls, .xlsx)
• JSON (JavaScript Object Notation)
• XML (eXtensible Markup Language)
• Images (JPEG, PNG, TIFF)
• Audio (WAV, MP3)
---
4. Loading Datasets in Python
• Use libraries like
• Use libraries like
---
5. Basic Dataset Exploration
• Check shape and size:
• Preview data:
• Check for missing values:
---
6. Summary
• Understanding dataset types is crucial before processing.
• Loading and exploring datasets helps identify cleaning and preprocessing needs.
---
Exercise
• Load a CSV and JSON dataset in Python, print their shapes, and identify missing values.
---
#DataScience #Datasets #DataLoading #Python #DataExploration
The rest of the parts👇
https://t.me/DataScienceM🌟
---
1. What is a Dataset?
• A dataset is a structured collection of data, usually organized in rows and columns, used for analysis or training machine learning models.
---
2. Types of Datasets
• Structured Data: Tables, spreadsheets with rows and columns (e.g., CSV, Excel).
• Unstructured Data: Images, text, audio, video.
• Semi-structured Data: JSON, XML files containing hierarchical data.
---
3. Common Dataset Formats
• CSV (Comma-Separated Values)
• Excel (.xls, .xlsx)
• JSON (JavaScript Object Notation)
• XML (eXtensible Markup Language)
• Images (JPEG, PNG, TIFF)
• Audio (WAV, MP3)
---
4. Loading Datasets in Python
• Use libraries like
pandas
for structured data:import pandas as pd
df = pd.read_csv('data.csv')
• Use libraries like
json
for JSON files:import json
with open('data.json') as f:
data = json.load(f)
---
5. Basic Dataset Exploration
• Check shape and size:
print(df.shape)
• Preview data:
print(df.head())
• Check for missing values:
print(df.isnull().sum())
---
6. Summary
• Understanding dataset types is crucial before processing.
• Loading and exploring datasets helps identify cleaning and preprocessing needs.
---
Exercise
• Load a CSV and JSON dataset in Python, print their shapes, and identify missing values.
---
#DataScience #Datasets #DataLoading #Python #DataExploration
The rest of the parts
https://t.me/DataScienceM
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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.,
• Downloads the HTML content of the chat
• Converts it to a PDF file using
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
#### • Install
Download from:
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
---
### 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
---
### 4. Optional Enhancements
• Add GUI with Tkinter
• Accept multiple URLs
• Add PDF metadata (title, author, etc.)
• Add support for offline rendering using
---
### 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://t.me/CodeProgrammer✅
---
### 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
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://t.me/CodeProgrammer
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Python | Machine Learning | Coding | R
Photo
# 📚 Python Tutorial: Convert EPUB to PDF (Preserving Images)
#Python #EPUB #PDF #EbookConversion #Automation
This comprehensive guide will show you how to convert EPUB files (including those with images) to high-quality PDFs using Python.
---
## 🔹 Required Tools & Libraries
We'll use these Python packages:
-
-
-
Also install system dependencies:
---
## 🔹 Step 1: Extract EPUB Contents
First, we'll unpack the EPUB file to access its HTML and images.
---
## 🔹 Step 2: Convert HTML to PDF
Now we'll convert the extracted HTML files to PDF while preserving images.
---
## 🔹 Step 3: Complete Conversion Function
Combine everything into a single workflow.
---
## 🔹 Advanced Options
### 1. Custom Styling
Add CSS to improve PDF appearance:
#Python #EPUB #PDF #EbookConversion #Automation
This comprehensive guide will show you how to convert EPUB files (including those with images) to high-quality PDFs using Python.
---
## 🔹 Required Tools & Libraries
We'll use these Python packages:
-
ebooklib
- For EPUB parsing-
pdfkit
(wrapper for wkhtmltopdf) - For PDF generation-
Pillow
- For image handling (optional)pip install ebooklib pdfkit pillow
Also install system dependencies:
# On Ubuntu/Debian
sudo apt-get install wkhtmltopdf
# On MacOS
brew install wkhtmltopdf
# On Windows (download from wkhtmltopdf.org)
---
## 🔹 Step 1: Extract EPUB Contents
First, we'll unpack the EPUB file to access its HTML and images.
from ebooklib import epub
from bs4 import BeautifulSoup
import os
def extract_epub(epub_path, output_dir):
book = epub.read_epub(epub_path)
# Create output directory
os.makedirs(output_dir, exist_ok=True)
# Extract all items (chapters, images, styles)
for item in book.get_items():
if item.get_type() == epub.ITEM_IMAGE:
# Save images
with open(os.path.join(output_dir, item.get_name()), 'wb') as f:
f.write(item.get_content())
elif item.get_type() == epub.ITEM_DOCUMENT:
# Save HTML chapters
with open(os.path.join(output_dir, item.get_name()), 'wb') as f:
f.write(item.get_content())
return [item.get_name() for item in book.get_items() if item.get_type() == epub.ITEM_DOCUMENT]
---
## 🔹 Step 2: Convert HTML to PDF
Now we'll convert the extracted HTML files to PDF while preserving images.
import pdfkit
from PIL import Image # For image validation (optional)
def html_to_pdf(html_files, output_pdf, base_dir):
options = {
'encoding': "UTF-8",
'quiet': '',
'enable-local-file-access': '', # Critical for local images
'no-outline': None,
'margin-top': '15mm',
'margin-right': '15mm',
'margin-bottom': '15mm',
'margin-left': '15mm',
}
# Validate images (optional)
for html_file in html_files:
soup = BeautifulSoup(open(os.path.join(base_dir, html_file)), 'html.parser')
for img in soup.find_all('img'):
img_path = os.path.join(base_dir, img['src'])
try:
Image.open(img_path) # Validate image
except Exception as e:
print(f"Image error in {html_file}: {e}")
img.decompose() # Remove broken images
# Convert to PDF
pdfkit.from_file(
[os.path.join(base_dir, f) for f in html_files],
output_pdf,
options=options
)
---
## 🔹 Step 3: Complete Conversion Function
Combine everything into a single workflow.
def epub_to_pdf(epub_path, output_pdf, temp_dir="temp_epub"):
try:
print(f"Converting {epub_path} to PDF...")
# Step 1: Extract EPUB
print("Extracting EPUB contents...")
html_files = extract_epub(epub_path, temp_dir)
# Step 2: Convert to PDF
print("Generating PDF...")
html_to_pdf(html_files, output_pdf, temp_dir)
print(f"Success! PDF saved to {output_pdf}")
return True
except Exception as e:
print(f"Conversion failed: {str(e)}")
return False
finally:
# Clean up temporary files
if os.path.exists(temp_dir):
import shutil
shutil.rmtree(temp_dir)
---
## 🔹 Advanced Options
### 1. Custom Styling
Add CSS to improve PDF appearance:
def html_to_pdf(html_files, output_pdf, base_dir):
options = {
# ... previous options ...
'user-style-sheet': 'styles.css', # Custom CSS
}
# Create CSS file if needed
css = """
body { font-family: "Times New Roman", serif; font-size: 12pt; }
img { max-width: 100%; height: auto; }
"""
with open(os.path.join(base_dir, 'styles.css'), 'w') as f:
f.write(css)
pdfkit.from_file(/* ... */)
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📚 JaidedAI/EasyOCR — an open-source Python library for Optical Character Recognition (OCR) that's easy to use and supports over 80 languages out of the box.
### 🔍 Key Features:
🔸 Extracts text from images and scanned documents — including handwritten notes and unusual fonts
🔸 Supports a wide range of languages like English, Russian, Chinese, Arabic, and more
🔸 Built on PyTorch — uses modern deep learning models (not the old-school Tesseract)
🔸 Simple to integrate into your Python projects
### ✅ Example Usage:
### 📌 Ideal For:
✅ Text extraction from photos, scans, and documents
✅ Embedding OCR capabilities in apps (e.g. automated data entry)
🔗 GitHub: https://github.com/JaidedAI/EasyOCR
👉 Follow us for more: @DataScienceN
#Python #OCR #MachineLearning #ComputerVision #EasyOCR
### 🔍 Key Features:
🔸 Extracts text from images and scanned documents — including handwritten notes and unusual fonts
🔸 Supports a wide range of languages like English, Russian, Chinese, Arabic, and more
🔸 Built on PyTorch — uses modern deep learning models (not the old-school Tesseract)
🔸 Simple to integrate into your Python projects
### ✅ Example Usage:
import easyocr
reader = easyocr.Reader(['en', 'ru']) # Choose supported languages
result = reader.readtext('image.png')
### 📌 Ideal For:
✅ Text extraction from photos, scans, and documents
✅ Embedding OCR capabilities in apps (e.g. automated data entry)
🔗 GitHub: https://github.com/JaidedAI/EasyOCR
👉 Follow us for more: @DataScienceN
#Python #OCR #MachineLearning #ComputerVision #EasyOCR
❤3👎1🎉1
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— 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
#AI #ImageEditing #ComputerVision #Gradio #OpenSource #Python
✉️ Our Telegram channels: https://t.me/addlist/0f6vfFbEMdAwODBk📱 Our WhatsApp channel: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
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In this comprehensive, step-by-step tutorial, you will learn how to build a real-time folder monitoring and intruder detection system using Python.
Create a background program that:
- Monitors a specific folder on your computer.
- Instantly captures a photo using the webcam whenever someone opens that folder.
- Saves the photo with a timestamp in a secure folder.
- Runs automatically when Windows starts.
- Keeps running until you manually stop it (e.g., via Task Manager or a hotkey).
Read and get code: https://hackmd.io/@husseinsheikho/Build-a-Folder-Monitoring
#Python #Security #FolderMonitoring #IntruderDetection #OpenCV #FaceCapture #Automation #Windows #TaskScheduler #ComputerVision
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