Forwarded from Machine Learning with Python
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Forwarded from Data Analytics
SQL Basics.pdf
102.8 KB
💻 Collection of cheat sheets on SQL
I've gathered for you short and understandable cheat sheets on the main topics:
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▶️ JOINs with clear examples;
▶️ Window functions;
▶️ SQL for data analysis.
An excellent set to refresh your knowledge before a job interview or quickly recall the syntax.
tags: #sql #useful
https://t.iss.one/DataAnalyticsX
I've gathered for you short and understandable cheat sheets on the main topics:
▶️ Basics of the SQL language;
▶️ JOINs with clear examples;
▶️ Window functions;
▶️ SQL for data analysis.
An excellent set to refresh your knowledge before a job interview or quickly recall the syntax.
tags: #sql #useful
https://t.iss.one/DataAnalyticsX
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When have you ever needed to add a mathematical description for your function in Python, but found that it takes too much time?
Non-programmers can't easily read Python's logic. However, manually converting it to LaTeX is slow and quickly becomes outdated as the code changes.
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In addition, latexify_py is open source. Install it using
An article about 3 tools that convert Python code to LaTeX: https://bit.ly/3Pw89yP
Run this code: https://bit.ly/4bW2ycE
https://t.iss.one/CodeProgrammer
Non-programmers can't easily read Python's logic. However, manually converting it to LaTeX is slow and quickly becomes outdated as the code changes.
latexify_py solves this problem with a single decorator, generating LaTeX directly from your function, so that the mathematics remains readable and always synchronized with the code.
Main features:
• Three decorators for different outputs: expressions, full equations, or pseudocode
• Displays the rendered LaTeX directly in Jupyter cells
• Functions continue to work normally when called
In addition, latexify_py is open source. Install it using
pip install latexify-pyAn article about 3 tools that convert Python code to LaTeX: https://bit.ly/3Pw89yP
Run this code: https://bit.ly/4bW2ycE
https://t.iss.one/CodeProgrammer
👍7❤4
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cs-video-courses — 78K+ stars.
MIT.
Stanford University.
University of California, Berkeley.
Harvard University.
Carnegie Mellon University.
Indian Institutes of Technology.
Princeton University.
California Institute of Technology.
Everything is free. All lectures are in video format. Everything is collected in one repository.
Topics:
→ Data structures and algorithms
→ Operating systems
→ Distributed systems
→ Database systems
→ Computer networks
→ Machine learning
→ Deep learning
→ Natural language processing (NLP)
→ Computer vision
→ Computer graphics
→ Security
→ Quantum computing
→ Robotics
→ Blockchain
From beginner level (CS50) to advanced (6.824 Distributed Systems).
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https://t.iss.one/CodeProgrammer
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Forwarded from Code With Python
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Useful playbook:
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• Exam guide:
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• MCP documentation:
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API documentation:
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Useful playbook:
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Forwarded from Udemy Free Coupons
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Build a Large Language Model from Scratch! 🚀
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Repository: https://github.com/rasbt/LLMs-from-scratch 🔗
This repository provides code examples for developing, pretraining, and fine-tuning a Large Language Model (LLM) from the ground up. It serves as the official codebase for the book "Build a Large Language Model (From Scratch)." 📘
Notebook examples are included for each chapter:
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Chapter 2: Working with Text Data 📝
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Chapter 5: Pretraining on Unlabeled Data 📊
Chapter 6: Fine-tuning for Text Classification 🏷
Chapter 7: Fine-tuning to Follow Instructions 🗣
Repository: https://github.com/rasbt/LLMs-from-scratch 🔗
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