Data Science Jupyter Notebooks
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Explore the world of Data Science through Jupyter Notebooksโ€”insights, tutorials, and tools to boost your data journey. Code, analyze, and visualize smarter with every post.
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๐Ÿ”ฅ Trending Repository: superset

๐Ÿ“ Description: Apache Superset is a Data Visualization and Data Exploration Platform

๐Ÿ”— Repository URL: https://github.com/apache/superset

๐ŸŒ Website: https://superset.apache.org/

๐Ÿ“– Readme: https://github.com/apache/superset#readme

๐Ÿ“Š Statistics:
๐ŸŒŸ Stars: 69.7K stars
๐Ÿ‘€ Watchers: 1.5k
๐Ÿด Forks: 16.4K forks

๐Ÿ’ป Programming Languages: TypeScript - Python - Jupyter Notebook - JavaScript - HTML - Shell

๐Ÿท๏ธ Related Topics:
#react #python #flask #data_science #bi #analytics #superset #apache #data_visualization #data_engineering #business_intelligence #data_viz #data_analytics #data_analysis #sql_editor #asf #business_analytics #apache_superset


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๐Ÿง  By: https://t.iss.one/DataScienceM
๐Ÿ”ฅ Trending Repository: memU

๐Ÿ“ Description: Memory infrastructure for LLMs and AI agents

๐Ÿ”— Repository URL: https://github.com/NevaMind-AI/memU

๐ŸŒ Website: https://memu.pro

๐Ÿ“– Readme: https://github.com/NevaMind-AI/memU#readme

๐Ÿ“Š Statistics:
๐ŸŒŸ Stars: 3.6K stars
๐Ÿ‘€ Watchers: 28
๐Ÿด Forks: 241 forks

๐Ÿ’ป Programming Languages: Python

๐Ÿท๏ธ Related Topics:
#python #agent #open_source #benchmark #framework #ai #memory #mcp #sandbox #ai_agents #llm #agentic_framework #agentic_workflow #ai_companion #agentic_ai #ai_roleplay


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๐Ÿง  By: https://t.iss.one/DataScienceM
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๐Ÿ“น Video downloader from YouTube and other platforms

tuitube is a text-based interface for downloading videos from YouTube, ๐•, Twitch, Instagram, and Bilibili using yt-dlp. A convenient tool for those who prefer the command line.

๐Ÿš€ Key features:
- Support for multiple video platforms
- Uses yt-dlp for downloading
- Simple text interface
- Easily configurable and command-line driven

๐Ÿ“Œ GitHub: https://github.com/remorses/tuitube

#python #YouTube

https://t.iss.one/DataScienceN โœ…
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โšก๏ธ Running DeepSeek on our computer using Python

Do you want an LLM on your computer: to work offline, not leak data, and seamlessly integrate into a bot? Then let's take DeepSeek Coder and get started!

โš™๏ธ Installing dependencies:
pip install -U transformers accelerate torch


โ–ถ๏ธ Example code:
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_name = "deepseek-ai/deepseek-coder-6.7b-base"

tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    trust_remote_code=True,
    torch_dtype=torch.float16,   # if the GPU supports fp16
    device_map="auto"            # if there's a GPU โ€” it will use it
)
model.eval()

prompt = "Write a Telegram feedback bot on aiogram"

inputs = tokenizer(prompt, return_tensors="pt")
device = next(model.parameters()).device
inputs = {k: v.to(device) for k, v in inputs.items()}

with torch.inference_mode():
    outputs = model.generate(
        **inputs,
        max_new_tokens=180,
        do_sample=True,      # IMPORTANT: otherwise the temperature doesn't affect
        temperature=0.7,
        top_p=0.9
    )

print(tokenizer.decode(outputs[0], skip_special_tokens=True))


โž• Advantages:
โ€” works locally (after downloading the weights);
โ€” easily integrates into Telegram/Discord/CLI;
โ€” can be accelerated on the GPU via device_map="auto".

If memory is limited โ€” there are quantized versions (4bit/8bit) and GGUF.

๐Ÿ‘ Saving

#python #soft #code
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