A collection of completely free neural nets on hugginface
You can use them to do cool photo upscaling, remove backgrounds, edit images and more.
All the models are free and opensource here: huggingface.
🌐 http://t.me/codeprogrammer ✅️
You can use them to do cool photo upscaling, remove backgrounds, edit images and more.
All the models are free and opensource here: huggingface.
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🤖 Mistral has released a neural network that knows more than 80 programming languages. Codestral surpasses such giants as Llama-3 and CodeLlama, as well as... GPT-4o, and yet weighs three times less!
This model knows how to write and improve code, suggests the best solutions to problems and even knows design patterns. You can connect it to your projects via API or just use it in VS Code. For olds: the neural network even knows Fortran and COBOL.
Use it here or directly in your browser here.
#GPT4 #AI #PYTHON
🌐 http://t.me/codeprogrammer ✅️
This model knows how to write and improve code, suggests the best solutions to problems and even knows design patterns. You can connect it to your projects via API or just use it in VS Code. For olds: the neural network even knows Fortran and COBOL.
Use it here or directly in your browser here.
#GPT4 #AI #PYTHON
🌐 http://t.me/codeprogrammer ✅️
We’ve carefully selected the best YouTube channels for diving deep into AI:
1. Sentdex: Python and machine learning tutorials.
2. Two Minute Papers: Quick AI research summaries.
3. Siraj Raval: Accessible AI education.
4. TensorFlow: Tutorials and demos on this open-source library.
5. Lex Fridman: Interviews with AI experts.
6. Matt Wolfe: AI news, reviews, and tutorials.
7. AI Explained: Simplifying complex AI concepts.
8. DeepLearning.AI: Courses by Andrew Ng.
9. Yannic Kilcher: Practical AI tutorials.
10. Data School: Data science and machine learning tutorials.
Check out these channels for more!
#AI #MACHINELEARNING #PYTHON
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By the way, here are some of them:
sample.txt file to the .tar.gz archive:import tarfile
with tarfile.open('sample.tar.gz', 'w:gz') as tar:
tar.add('sample.txt')
import difflib
diff = difflib.ndiff('one\ntwo\nthree\n'.splitlines(keepends=True),
'ore\ntree\nemu\n'.splitlines(keepends=True))
print(''.join(diff))
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Here are tons of useful links for each section, some of these links have already been posted in the channel, for example, links to incredibly useful tutorials from Lilian Weng .
Here are the main sections that this roadmap covers:
#python #deeplearning #ML
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Aider got the top score on SWE Bench, a challenging benchmark in which Aider solved real-world problems on #GitHub from popular open source projects like #django, #scikitlearn, #matplotlib, etc.
🟡 Docs
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🎁 Lisa has given away over $100,000 in the last 30 days. Every single one of her subscribers is making money.
She is a professional trader and broadcasts her way of making money trading on her channel EVERY subscriber she has helped, and she will help you.
🧠 Do this and she will help you earn :
1. Subscribe to her channel
2. Write “GIFT” to her private messages
3. Follow her channel and trade with her. Repeat transactions after her = earn a lot of money.
Subscribe 👇🏻
https://t.me/+VwDs29eEzRA5MTAx
She is a professional trader and broadcasts her way of making money trading on her channel EVERY subscriber she has helped, and she will help you.
🧠 Do this and she will help you earn :
1. Subscribe to her channel
2. Write “GIFT” to her private messages
3. Follow her channel and trade with her. Repeat transactions after her = earn a lot of money.
Subscribe 👇🏻
https://t.me/+VwDs29eEzRA5MTAx
With these chips the AI will work without limits, gossip about everything in the world without fear of bans, and respond in ways that OpenAI has never even dreamed of. There are many other chips that will make the neural network much smarter and more accurate.
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@codeprogrammer Scientific Python Lectures.pdf
18.7 MB
Gaël Varoquaux, Emmanuelle Gouillart, Olav Vahtras Pierre de Buyl, and many others.
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Loading YouTube playlist using Python.
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https://t.me/addlist/8_rRW2scgfRhOTc0
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WebScraping with Gen AI
During this session, we'll explore the following topics:
1️⃣ Basics of Web Scraping:
Understand the fundamental concepts and techniques of web scraping and its legal and ethical considerations.
2️⃣ Scraping with Gen AI:
Discover how Gen AI revolutionizes the web scraping landscape with real-world examples.
3️⃣ Jina Reader API:
Get acquainted with the Jina Reader API, a powerful tool for obtaining LLM-friendly input from URLs or web searches.
4️⃣ ScrapeGraphAI:
Dive into ScrapeGraphAI, a groundbreaking Python library that combines LLMs and direct graph logic for creating robust scraping pipelines.
Event Details:
🗓 Date: 22 June, Saturday
⏰ Time: 11:00 AM IST
🔗 Register now: https://www.buildfastwithai.com/events/web-scraping-with-gen-ai
Connect with Founder from IIT Delhi;
https://www.linkedin.com/in/satvik-paramkusham/
During this session, we'll explore the following topics:
Understand the fundamental concepts and techniques of web scraping and its legal and ethical considerations.
Discover how Gen AI revolutionizes the web scraping landscape with real-world examples.
Get acquainted with the Jina Reader API, a powerful tool for obtaining LLM-friendly input from URLs or web searches.
Dive into ScrapeGraphAI, a groundbreaking Python library that combines LLMs and direct graph logic for creating robust scraping pipelines.
Event Details:
Connect with Founder from IIT Delhi;
https://www.linkedin.com/in/satvik-paramkusham/
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👨🏻💻 If you want to start learning data science from scratch in 2024, this roadmap can be a great starting point for you.👌🏼
🏷 Statistics and probability
◾┘️ link: Statistics & Probability
🏷 Linear Algebra
┘ ◽️ link: Essence of Linear Algebra
🏷 Python
◾┘️ link: Learn Python 3
🏷 Pandas library
◾┘️ link: pandas documentation
🏷 Data preparation with Pandas
Link : Data Wrangling with Pandas
🏷 NumPy library
◾┘️ link: NumPy documentation
🏷 Matplotlib and Seaborn library
◾┘️ Link: Matplotlib / seaborn
🏷 Tableau Public platform
Link : Tableau Public
🏷 scikit-learn library
◾┘️ link: scikit-learn
🏷 Hands-On Machine Learning book
◾┘️ link: Hands-On ML
🏷 TensorFlow library
◾┘️ link: TensorFlow Tutorial
🏷 PyTorch library
◾┘️ Link: PyTorch Documentation
🏷 Spark framework course
◾┘️ Link: Spark Course
🏷 Natural language processing course in Python
◾┘️ link: NLP in Python
🏷 Kaggle platform
◾┘️ link: Kaggle
🏷 GitHub platform
◾┘️ Link: GitHub
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