Data Science | Machine Learning with Python for Researchers

🖥 5 useful Python automation scripts

1. Download Youtube videos
pip install pytube

from pytube import YouTube

# Specify the URL of the YouTube video
video_url = "https://www.youtube.com/watch?v=dQw4w9WgXcQ"

# Create a YouTube object
yt = YouTube(video_url)

# Select the highest resolution stream
stream = yt.streams.get_highest_resolution()

# Define the output path for the downloaded video
output_path = "path/to/output/directory/"

# Download the video
stream.download(output_path)

print("Video downloaded successfully!")

2. Automate WhatsApp messages

pip install pywhatkit

import pywhatkit

# Set the target phone number (with country code) and the message
phone_number = "+1234567890"
message = "Hello, this is an automated WhatsApp message!"

# Schedule the message to be sent at a specific time (24-hour format)
hour = 13
minute = 30

# Send the scheduled message
pywhatkit.sendwhatmsg(phone_number, message, hour, minute)

3. Google search with Python


pip install googlesearch-python

from googlesearch import search

# Define the query you want to search
query = "Python programming"

# Specify the number of search results you want to retrieve
num_results = 5

# Perform the search and retrieve the results
search_results = search(query, num_results=num_results, lang='en')

# Print the search results
for result in search_results:
    print(result)

4. Download Instagram posts

pip install instaloader

import instaloader

# Create an instance of Instaloader
loader = instaloader.Instaloader()

# Define the target Instagram profile
target_profile = "instagram"

# Download posts from the profile
loader.download_profile(target_profile, profile_pic=False, fast_update=True)

print("Posts downloaded successfully!")

5. Extract audio from video files

pip install moviepy


from moviepy.editor import VideoFileClip

# Define the path to the video file
video_path = "path/to/video/file.mp4"

# Create a VideoFileClip object
video_clip = VideoFileClip(video_path)

# Extract the audio from the video
audio_clip = video_clip.audio

# Define the output audio file path
output_audio_path = "path/to/output/audio/file.mp3"

# Write the audio to the output file
audio_clip.write_audiofile(output_audio_path)

# Close the clips
video_clip.close()
audio_clip.close()

print("Audio extracted successfully!")

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Data Science | Machine Learning with Python for Researchers

🚀 NAUTILUS: boosting Bayesian importance nested sampling with deep learning

A novel approach to boost the efficiency of the importance nested sampling (INS) technique for Bayesian posterior and evidence estimation using deep learning.

Install:
pip install nautilus-sampler

import corner
import numpy as np
from nautilus import Prior, Sampler
from scipy.stats import multivariate_normal

prior = Prior()
for key in 'abc':
    prior.add_parameter(key)

def likelihood(param_dict):
    x = [param_dict[key] for key in 'abc']
    return multivariate_normal.logpdf(x, mean=[0.4, 0.5, 0.6], cov=0.01)

sampler = Sampler(prior, likelihood)
sampler.run(verbose=True)
points, log_w, log_l = sampler.posterior()
corner.corner(points, weights=np.exp(log_w), labels='abc')

🖥 Github: https://github.com/johannesulf/nautilus

⭐️ Docs: https://nautilus-sampler.readthedocs.io/

📕 Paper: https://arxiv.org/abs/2306.16923v1

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1.57K viewsedited 14:46

Data Science | Machine Learning with Python for Researchers

🏌️ GlOttal-flow LPC Filter (GOLF)

A DDSP-based neural vocoder.

🖥 Github: https://github.com/yoyololicon/golf

📕 Paper: https://arxiv.org/abs/2306.17252v1

🔗Demo: https://yoyololicon.github.io/golf-demo/

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Data Science | Machine Learning with Python for Researchers

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🔮 SAM-PT: Segment Anything + Tracking 🔮

⭐️ SAM-PT is the first method to utilize sparse point propagation for Video Object Segmentation (VOS).

🌐 Review https://t.ly/QLMG

🌐 Paper arxiv.org/pdf/2307.01197.pdf

🌐 Project www.vis.xyz/pub/sam-pt/

🌐 Code github.com/SysCV/sam-pt

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Data Science | Machine Learning with Python for Researchers

🍸The Drunkard’s Odometry: Estimating Camera Motion in Deforming Scenes

🖥 Github: https://github.com/UZ-SLAMLab/DrunkardsOdometry

⏩ Paper: https://arxiv.org/pdf/2306.16917v1.pdf

💨 Dataset: https://paperswithcode.com/dataset/drunkard-s-dataset

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Data Science | Machine Learning with Python for Researchers

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🪄 Making a web app generator with open ML models

🖥 Github: https://github.com/huggingface/blog/blob/main/text-to-webapp.md

📕 HuggingFace: https://huggingface.co/blog/text-to-webapp

🔗Demo: https://huggingface.co/spaces/jbilcke-hf/webapp-factory-wizardcoder

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1.76K viewsedited 11:51

Data Science | Machine Learning with Python for Researchers

🤳Filtered-Guided Diffusion

🖥 Github: https://github.com/jaclyngu/filteredguideddiffusion

⏩ Paper: https://arxiv.org/pdf/2306.17141v1.pdf

💨 Dataset: https://paperswithcode.com/dataset/afhq

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1.64K viewsedited 12:26

Data Science | Machine Learning with Python for Researchers

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🪩 DISCO: Human Dance Generation

⭐️ NTU (+ #Microsoft) unveils DISCO: a big step towards the Human Dance Generation.

🌐 Review https://t.ly/cNGX

🌐 Paper arxiv.org/pdf/2307.00040.pdf

🌐Project: disco-dance.github.io/

🌐 Code github.com/Wangt-CN/DisCo

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1.68K viewsedited 13:04

Data Science | Machine Learning with Python for Researchers

Building an Image Recognition API using Flask.

Step 1: Set up the project environment

1. Create a new directory for your project and navigate to it.
2. Create a virtual environment (optional but recommended):
(Image 1.)
3. Install the necessary libraries (image 2.)

Step 2: Create a Flask Web Application
Create a new file called app.py in the project directory (image 3.)

Step 3: Launch the Flask Application
Save the changes and run the Flask application (image 4.)

Step 4: Test the API
Your API is now up and running and you can send images to /predict via HTTP POST requests.
You can use tools such as curl or Postman to test the API.
• An example of using curl (image 5.)
• An example using Python queries (image 6.)

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Data Science | Machine Learning with Python for Researchers

Deep Learning
NLP
AI
Python
ML
Data Mining
Tensorflow
Keras

👇👇👇👇👇
@Machine_learn

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Data Science | Machine Learning with Python for Researchers

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🚀 Hierarchical Open-vocabulary Universal Image Segmentation

Decoupled text-image fusion mechanism and representation learning modules for both "things" and "stuff".

🖥 Github: https://github.com/berkeley-hipie/hipie

📕 Paper: https://arxiv.org/abs/2307.00764v1

🔗Project: https://people.eecs.berkeley.edu/~xdwang/projects/HIPIE/

🔗 Dataset: https://paperswithcode.com/dataset/pascal-panoptic-parts

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1.86K viewsedited 12:20

Data Science | Machine Learning with Python for Researchers

🔍Foundation Model for Endoscopy Video Analysis

🖥 Github: https://github.com/med-air/endo-fm

⏩ Paper: https://arxiv.org/pdf/2306.16741v1.pdf

💨 Dataset: https://paperswithcode.com/dataset/kumc

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1.85K viewsedited 14:41

Data Science | Machine Learning with Python for Researchers

We launched a special bot some time ago to download all scientific, software and mathematics books The bot contains more than thirty million books, and new books are downloaded first, In addition to the possibility of downloading all articles and scientific papers for free

To request a subscription: talk to @Hussein_Sheikho

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Data Science | Machine Learning with Python for Researchers