This media is not supported in your browser
VIEW IN TELEGRAM
🔖 Interactive textbook on probability theory and statistics 📊✨
A super-intuitive site where you can visually study distributions, sampling, and statistical concepts. 📈🎲
No tons of formulas and boring theory — everything is demonstrated through interactive examples and simulations. 💻🔬
⛓️ Download here 👇
https://seeing-theory.brown.edu/
#Probability #Statistics #DataScience #Learning #Interactive #Math
https://t.iss.one/CodeProgrammer
A super-intuitive site where you can visually study distributions, sampling, and statistical concepts. 📈🎲
No tons of formulas and boring theory — everything is demonstrated through interactive examples and simulations. 💻🔬
⛓️ Download here 👇
https://seeing-theory.brown.edu/
#Probability #Statistics #DataScience #Learning #Interactive #Math
https://t.iss.one/CodeProgrammer
❤8
Found an easy way to learn math for ML: Mathematics for Machine Learning 🎓📚
This is a curated collection on GitHub, including books, research papers, video lectures, and basic materials on math for studying and reviewing the mathematical foundations of machine learning. 📖📊
It helps build a stronger knowledge base by bringing together trusted resources around topics that machine learning engineers constantly encounter: linear algebra, mathematical analysis, probability theory, statistics, information theory, matrix calculus, and deep learning mathematics. 🧮🤖
Free public repository on GitHub. 💻✨
https://github.com/dair-ai/Mathematics-for-ML
#MachineLearning #Mathematics #DataScience #Learning #GitHub #AI
✨ Join Best TG Channels
https://t.iss.one/addlist/0f6vfFbEMdAwODBk
⭐️ Join Our WhatsApp Channel
https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
This is a curated collection on GitHub, including books, research papers, video lectures, and basic materials on math for studying and reviewing the mathematical foundations of machine learning. 📖📊
It helps build a stronger knowledge base by bringing together trusted resources around topics that machine learning engineers constantly encounter: linear algebra, mathematical analysis, probability theory, statistics, information theory, matrix calculus, and deep learning mathematics. 🧮🤖
Free public repository on GitHub. 💻✨
https://github.com/dair-ai/Mathematics-for-ML
#MachineLearning #Mathematics #DataScience #Learning #GitHub #AI
✨ Join Best TG Channels
https://t.iss.one/addlist/0f6vfFbEMdAwODBk
⭐️ Join Our WhatsApp Channel
https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
GitHub
GitHub - dair-ai/Mathematics-for-ML: 🧮 A collection of resources to learn mathematics for machine learning
🧮 A collection of resources to learn mathematics for machine learning - dair-ai/Mathematics-for-ML
❤9👎1
This media is not supported in your browser
VIEW IN TELEGRAM
✍️ Pyneng — a large base for Python and network automation!
Detailed documentation and educational materials. The site contains lessons on Python syntax, working with files, functions, OOP, as well as separate sections on network technologies. The materials are presented with a large number of examples and practical tasks.
📌 I'll leave a link: https://pyneng.readthedocs.io/en/latest/
#Python #NetworkAutomation #DevOps #Coding #Learning #Tech
✨ Join Best TG Channels https://t.iss.one/addlist/0f6vfFbEMdAwODBk
⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
🚀 Level up your AI & Data Science skills with HelloEncyclo — a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
✅ 13 courses live + 40+ coming soon
🎯 One access, lifetime updates
🔑 Use code: PRESALE-BOOK-WAVE-2GFG
👉 https://helloencyclo.com/?ref=HUSSEINSHEIKHO
Detailed documentation and educational materials. The site contains lessons on Python syntax, working with files, functions, OOP, as well as separate sections on network technologies. The materials are presented with a large number of examples and practical tasks.
📌 I'll leave a link: https://pyneng.readthedocs.io/en/latest/
#Python #NetworkAutomation #DevOps #Coding #Learning #Tech
✨ Join Best TG Channels https://t.iss.one/addlist/0f6vfFbEMdAwODBk
⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
🚀 Level up your AI & Data Science skills with HelloEncyclo — a growing all-in-one platform featuring hands-on courses in LLMs, Deep Learning, MLOps, Data Engineering, and more.
✅ 13 courses live + 40+ coming soon
🎯 One access, lifetime updates
🔑 Use code: PRESALE-BOOK-WAVE-2GFG
👉 https://helloencyclo.com/?ref=HUSSEINSHEIKHO
❤5🎉1
Understanding algorithms changes the way you write code. 🧠 This playlist teaches you exactly that: understanding.
Pavel Mavrin. 61 lectures. Free. 🎓
Here's the difference:
Before understanding algorithms:
- You use hash tables for everything.
- You write the code first, then optimize it.
- You estimate the time complexity randomly.
After:
- You choose the appropriate data structure for the task.
- You think about complexity before writing the code.
- You learn an approach that is suitable for solving the problem.
What you will learn:
→ Time complexity and sorting.
→ Dynamic programming.
→ Advanced trees: segment tree, Fenwick tree, splay tree, link-cut tree.
→ Graph algorithms: traversals, shortest paths, flows.
→ String algorithms: substring search, suffix structures.
→ Fast Fourier Transform, linear programming, cryptography.
https://www.youtube.com/playlist?list=PLrS21S1jm43igE57Ye_edwds_iL7ZOAG4
https://t.iss.one/CodeProgrammer
#Algorithms #ComputerScience #Programming #DataStructures #Learning #Coding
✨ Join Best TG Channels https://t.iss.one/addlist/0f6vfFbEMdAwODBk
⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
Pavel Mavrin. 61 lectures. Free. 🎓
Here's the difference:
Before understanding algorithms:
- You use hash tables for everything.
- You write the code first, then optimize it.
- You estimate the time complexity randomly.
After:
- You choose the appropriate data structure for the task.
- You think about complexity before writing the code.
- You learn an approach that is suitable for solving the problem.
What you will learn:
→ Time complexity and sorting.
→ Dynamic programming.
→ Advanced trees: segment tree, Fenwick tree, splay tree, link-cut tree.
→ Graph algorithms: traversals, shortest paths, flows.
→ String algorithms: substring search, suffix structures.
→ Fast Fourier Transform, linear programming, cryptography.
https://www.youtube.com/playlist?list=PLrS21S1jm43igE57Ye_edwds_iL7ZOAG4
https://t.iss.one/CodeProgrammer
#Algorithms #ComputerScience #Programming #DataStructures #Learning #Coding
✨ Join Best TG Channels https://t.iss.one/addlist/0f6vfFbEMdAwODBk
⭐️ Join Our WhatsApp Channel https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A
❤7