Introduction to Deep Learning.pdf
10.5 MB
Introduction to Deep Learning
As we continue to push the boundaries of what's possible with artificial intelligence, I wanted to take a moment to share some insights on one of the most exciting fields in AI: Deep Learning.
Deep Learning is a subset of machine learning that uses neural networks to analyze and interpret data. These neural networks are designed to mimic the human brain, with layers of interconnected nodes (neurons) that process and transmit information.
What makes Deep Learning so powerful?
Ability to learn from large datasets: Deep Learning algorithms can learn from vast amounts of data, including images, speech, and text.
Improved accuracy: Deep Learning models can achieve state-of-the-art performance in tasks such as image recognition, natural language processing, and speech recognition.
Ability to generalize: Deep Learning models can generalize well to new, unseen data, making them highly effective in real-world applications.
Real-world applications of Deep Learning
Computer Vision: Self-driving cars, facial recognition, object detection
Natural Language Processing: Language translation, text summarization, sentiment analysis
Speech Recognition: Virtual assistants, voice-controlled devices.
#DeepLearning #AI #MachineLearning #NeuralNetworks #ArtificialIntelligence #DataScience #ComputerVision #NLP #SpeechRecognition #TechInnovation
As we continue to push the boundaries of what's possible with artificial intelligence, I wanted to take a moment to share some insights on one of the most exciting fields in AI: Deep Learning.
Deep Learning is a subset of machine learning that uses neural networks to analyze and interpret data. These neural networks are designed to mimic the human brain, with layers of interconnected nodes (neurons) that process and transmit information.
What makes Deep Learning so powerful?
Ability to learn from large datasets: Deep Learning algorithms can learn from vast amounts of data, including images, speech, and text.
Improved accuracy: Deep Learning models can achieve state-of-the-art performance in tasks such as image recognition, natural language processing, and speech recognition.
Ability to generalize: Deep Learning models can generalize well to new, unseen data, making them highly effective in real-world applications.
Real-world applications of Deep Learning
Computer Vision: Self-driving cars, facial recognition, object detection
Natural Language Processing: Language translation, text summarization, sentiment analysis
Speech Recognition: Virtual assistants, voice-controlled devices.
#DeepLearning #AI #MachineLearning #NeuralNetworks #ArtificialIntelligence #DataScience #ComputerVision #NLP #SpeechRecognition #TechInnovation
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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.
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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.
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What is torch.nn really?
This article explains it quite well.
📌 Read
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When I started working with PyTorch, my biggest question was: "What is torch.nn?".
This article explains it quite well.
📌 Read
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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!
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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.
---
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---
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.
---
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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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Python | Machine Learning | Coding | R
Photo
### 2. Handling Complex EPUBs
For problematic EPUBs, try this pre-processing:
---
## 🔹 Full Usage Example
Run from command line:
---
## 🔹 Troubleshooting Common Issues
| Problem | Solution |
|---------|----------|
| Missing images | Ensure
| Broken CSS paths | Use absolute paths in CSS references |
| Encoding issues | Specify UTF-8 in both HTML and pdfkit options |
| Large file sizes | Optimize images before conversion |
| Layout problems | Add CSS media queries for print |
---
## 🔹 Alternative Libraries
If
1. WeasyPrint (pure Python)
2. PyMuPDF (fitz)
3. Calibre's
---
## 🔹 Best Practices
1. Always clean temporary files after conversion
2. Validate input EPUBs before processing
3. Handle metadata (title, author, etc.)
4. Batch process multiple files with threading
5. Log conversion results for debugging
---
### 📚 Final Notes
This solution preserves:
✔️ All images in original quality
✔️ Chapter structure and formatting
✔️ Text encoding and special characters
For production use, consider adding:
- Progress tracking
- Parallel conversion of chapters
- EPUB metadata preservation
- Custom cover page support
#PythonAutomation #EbookTools #PDFConversion 🚀
Try enhancing this script by:
1. Adding a progress bar
2. Preserving table of contents
3. Supporting custom cover pages
4. Creating a GUI version
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For problematic EPUBs, try this pre-processing:
def clean_html(html_file):
with open(html_file, 'r+', encoding='utf-8') as f:
content = f.read()
soup = BeautifulSoup(content, 'html.parser')
# Remove problematic elements
for element in soup(['script', 'iframe', 'object']):
element.decompose()
# Fix image paths
for img in soup.find_all('img'):
if not os.path.isabs(img['src']):
img['src'] = os.path.abspath(os.path.join(os.path.dirname(html_file), img['src']))
# Write back cleaned HTML
f.seek(0)
f.write(str(soup))
f.truncate()
---
## 🔹 Full Usage Example
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser(description='Convert EPUB to PDF')
parser.add_argument('epub_file', help='Input EPUB file path')
parser.add_argument('pdf_file', help='Output PDF file path')
args = parser.parse_args()
success = epub_to_pdf(args.epub_file, args.pdf_file)
if not success:
exit(1)
Run from command line:
python epub_to_pdf.py input.epub output.pdf
---
## 🔹 Troubleshooting Common Issues
| Problem | Solution |
|---------|----------|
| Missing images | Ensure
enable-local-file-access
is set || Broken CSS paths | Use absolute paths in CSS references |
| Encoding issues | Specify UTF-8 in both HTML and pdfkit options |
| Large file sizes | Optimize images before conversion |
| Layout problems | Add CSS media queries for print |
---
## 🔹 Alternative Libraries
If
pdfkit
doesn't meet your needs:1. WeasyPrint (pure Python)
pip install weasyprint
2. PyMuPDF (fitz)
pip install pymupdf
3. Calibre's
ebook-convert
CLIebook-convert input.epub output.pdf
---
## 🔹 Best Practices
1. Always clean temporary files after conversion
2. Validate input EPUBs before processing
3. Handle metadata (title, author, etc.)
4. Batch process multiple files with threading
5. Log conversion results for debugging
---
### 📚 Final Notes
This solution preserves:
✔️ All images in original quality
✔️ Chapter structure and formatting
✔️ Text encoding and special characters
For production use, consider adding:
- Progress tracking
- Parallel conversion of chapters
- EPUB metadata preservation
- Custom cover page support
#PythonAutomation #EbookTools #PDFConversion 🚀
Try enhancing this script by:
1. Adding a progress bar
2. Preserving table of contents
3. Supporting custom cover pages
4. Creating a GUI version
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30 NumPy MCQs with solutions
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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
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Transformer Lesson - Part 1/7: Introduction and Architecture
Let's start:
https://hackmd.io/@husseinsheikho/transformers
Let's start:
https://hackmd.io/@husseinsheikho/transformers
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