The Attention Mechanism from Scratch

https://machinelearningmastery.com/the-attention-mechanism-from-scratch/

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pandas for Data Science
Learning Path

Skills: pandas, Data Science, Data Visualization

In this learning path, you’ll get started with pandas and get to know the ins and outs of how you can use it to analyze data with Python.

https://realpython.com/learning-paths/pandas-data-science/

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Public APIs

A collective list of free APIs

📃 Creator: public-apis

📃 Forked by: 27.8k

🌐 GitHub repo: https://github.com/public-apis/public-apis

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How to Add an Editor to Your Django App (Ckeditor)

In this tutorial, we will be looking at how to add an editor to your Django app which will ease your content creation process.

https://bytexplain.com/how-to-add-an-editor-to-your-django-app-ckeditor

✅ More ♥️♥️ = more posts

@CodeProgrammer ♥️

The Transformer Model

https://machinelearningmastery.com/the-transformer-model/

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⭐️ Data Science Books
https://t.me/DataScienceM

⭐️ Python | Algorithms | Data Structures
https://t.me/DataScience4

⭐️ Programming Languages | Web & Mobile Development | SQL | Software Design & Analysis
https://t.me/Ebooks2023

Machine Learning With Python
Learning Path

Skills: Image Processing, Text Classification, Speech Recognition

Machine learning is a field of computer science that uses statistical techniques to give computer programs the ability to learn from past experiences and improve how they perform specific tasks.

With this learning path, you’ll sample a range of common machine learning scenarios using Python.

https://realpython.com/learning-paths/machine-learning-python

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@CodeProgrammer ♥️

What Is Attention?

Attention is becoming increasingly popular in machine learning, but what makes it such an attractive concept? What is the relationship between attention applied in artificial neural networks and its biological counterpart? What components would one expect to form an attention-based system in machine learning?

https://machinelearningmastery.com/what-is-attention/

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@CodeProgrammer ♥️

🖥 Free Courses on Large Language Models

▪ChatGPT Prompt Engineering for Developers

▪LangChain for LLM Application Development

▪Building Systems with the ChatGPT API

▪Google Cloud Generative AI Learning Path

▪Introduction to Large Language Models with Google Cloud

▪LLM University

▪Full Stack LLM Bootcamp

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@CodeProgrammer ♥️

The source code for DragGAN has been released! 🔥🔥🔥

We can finally play with that marvel!

🔗 GitHub repository: https://github.com/XingangPan/DragGAN

https://t.me/DataScienceT

A special and important channel to download the most important books to learn programming and data science

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How to Make a Twitter Bot in Python With Tweepy

https://realpython.com/twitter-bot-python-tweepy/

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@CodeProgrammer ♥️

Understanding Simple Recurrent Neural Networks in Keras

https://machinelearningmastery.com/understanding-simple-recurrent-neural-networks-in-keras/

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Python Docker Tutorials

Docker is a containerization tool used for spinning up isolated, reproducible application environments. It is a popular development tool for Python developers.

https://realpython.com/tutorials/docker/

The Transformer Attention Mechanism

https://machinelearningmastery.com/the-transformer-attention-mechanism/

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@CodeProgrammer ♥️

This channel is for Programmers, Coders, Software Engineers.

1- Data Science
2- Machine Learning
3- Data Visualization
4- Artificial Intelligence
5- Data Analysis
6- Statistics
7- Deep Learning

https://t.me/DataScienceM
https://t.me/DataScienceM

Data Version Control With Python and DVC

https://realpython.com/python-data-version-control

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The Luong Attention Mechanism

https://machinelearningmastery.com/the-luong-attention-mechanism/

@CodeProgrammer

This channels is for Programmers, Coders, Software Engineers.

0- Python
1- Data Science
2- Machine Learning
3- Data Visualization
4- Artificial Intelligence
5- Data Analysis
6- Statistics
7- Deep Learning
8- programming Languages

https://t.me/addlist/8_rRW2scgfRhOTc0

🖥 10 Advanced Python Scripts For Everyday Programming

1. SpeedTest with Python
# pip install pyspeedtest
# pip install speedtest
# pip install speedtest-cli

#method 1
import speedtest

speedTest = speedtest.Speedtest()
print(speedTest.get_best_server())

#Check download speed
print(speedTest.download())

#Check upload speed
print(speedTest.upload())

# Method 2

import pyspeedtest
st = pyspeedtest.SpeedTest()
st.ping()
st.download()
st.upload()

2. Search on Google
# pip install google

from googlesearch import search

query = "Medium.com"

for url in search(query):
print(url)

3. Make Web Bot
# pip install selenium

import time
from selenium import webdriver
from selenium.webdriver.common.keys import Keys

bot = webdriver.Chrome("chromedriver.exe")
bot.get('[http://www.google.com'](http://www.google.com'))

search = bot.find_element_by_name('q')
search.send_keys("@codedev101")
search.send_keys(Keys.RETURN)
time.sleep(5)
bot.quit()

4. Fetch Song Lyrics
# pip install lyricsgenius

import lyricsgenius

api_key = "xxxxxxxxxxxxxxxxxxxxx"

genius = lyricsgenius.Genius(api_key)
artist = genius.search_artist("Pop Smoke", max_songs=5,sort="title")
song = artist.song("100k On a Coupe")

print(song.lyrics)

5. Get Exif Data of Photos
# Get Exif of Photo

# Method 1
# pip install pillow
import PIL.Image
import PIL.ExifTags

img = PIL.Image.open("Img.jpg")
exif_data =
{
PIL.ExifTags.TAGS[i]: j
for i, j in img._getexif().items()
if i in PIL.ExifTags.TAGS
}
print(exif_data)


# Method 2
# pip install ExifRead
import exifread

filename = open(path_name, 'rb')

tags = exifread.process_file(filename)
print(tags)

6. OCR Text from Image
# pip install pytesseract

import pytesseract
from PIL import Image

pytesseract.pytesseract.tesseract_cmd = r'C:\Program Files\Tesseract-OCR\tesseract.exe'

t=Image.open("img.png")
text = pytesseract.image_to_string(t, config='')

print(text)

7. Convert Photo into Cartonize
# pip install opencv-python

import cv2

img = cv2.imread('img.jpg')
grayimg = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
grayimg = cv2.medianBlur(grayimg, 5)

edges = cv2.Laplacian(grayimg , cv2.CV_8U, ksize=5)
r,mask =cv2.threshold(edges,100,255,cv2.THRESH_BINARY_INV)

img2 = cv2.bitwise_and(img, img, mask=mask)
img2 = cv2.medianBlur(img2, 5)

cv2.imwrite("cartooned.jpg", mask)

8. Empty Recycle Bin
# pip install winshell

import winshell
try:
winshell.recycle_bin().empty(confirm=False, /show_progress=False, sound=True)
print("Recycle bin is emptied Now")
except:
print("Recycle bin already empty")

9. Python Image Enhancement
# pip install pillow

from PIL import Image,ImageFilter
from PIL import ImageEnhance

im = Image.open('img.jpg')

# Choose your filter
# add Hastag at start if you don't want to any filter below

en = ImageEnhance.Color(im)
en = ImageEnhance.Contrast(im)
en = ImageEnhance.Brightness(im)
en = ImageEnhance.Sharpness(im)

# result
en.enhance(1.5).show("enhanced")

10. Get Window Version
# Window Version

import wmi
data = wmi.WMI()
for os_name in data.Win32_OperatingSystem():
print(os_name.Caption) # Microsoft Windows 11 Home

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