Here is the list of resources to learn Data Structures and Algorithms from beginner to advance:

📑 Prerequisite - MIT's Mathematics for Computer Science:
🔸 https://lnkd.in/ejdPkSs

📌 Khan Academy - Intro to algorithms:
🔸 https://lnkd.in/e8ZUWwz

📌 Introduction to Algorithms Book by Charles E. Leiserson, Clifford Stein, Ronald Rivest, and Thomas H. Cormen:
🔸 https://lnkd.in/e8iqvwn

📌 GeeksforGeeks - Data Structures Tutorials:
🔸https://lnkd.in/eiFACVV

📌 MIT - Introduction to Algorithms:
🔸 https://lnkd.in/eKavb3T

📌 Coursera - Data Structures and Algorithms Specialization:
🔸 https://lnkd.in/eDk8ZuY

📌 Coursera - Algorithms Specialization:
🔸 https://lnkd.in/ejJw5TV

📌 MIT - Advanced Data Structures:
🔸 https://lnkd.in/eKA7FD2

📌 GeeksforGeeks - Advanced Data Structures Tutorials:
🔸 https://lnkd.in/eu2J-Bm

💡 I also found this interesting website which explains Data Structures and Algorithms through animations -

🔸 https://visualgo.net/en


#منابع
#datastructures #algorithms #mathematics #machinelearning #computerscience

✴️ @AI_Python_EN
❇️ @AI_Python

The Math of Machine Learning - Berkeley University Textbook

The mathematical skills you need for starting your journey into the field of Machine Learning
Note: It should be noted that It doesn't cover all the mathematical skills you need for doing ML during your life, It's just a brief textbook which could help you to start learning more complicated mathematical concepts in ML

https://www.datasciencecentral.com/profiles/blogs/tutorial-the-math-of-machine-learning-berkeley-university
#mahine_learning #mathematics

MIT Technology Review:
A #NeuralNet solves the three-body problem 100 million times faster

#MachineLearning #mathematics

🔰 NeuralNet

🔰 Paper

❇️ @AI_Python_EN

"The International Mathematical Olympiad Grand Challenge"

The challenge: build an AI that can win a gold medal in the competition -- https://imo-grand-challenge.github.io

#ArtificialIntelligence #DeepLearning #Mathematics

I gladly announce my first online course on #Statistics and #Mathematics for #MachineLearning and #DeepLearning.

The course will be in English, QA sessions with instructor will be in Turkish, Azerbaijani , or English. TA sessions will be in English.

This is the first course of tribology courses to help attendees to capture foundations and mathematics behind ML,DL models.

The courses are listed as follow:
1. Statistics Foundation for ML
2. Introduction to Statistical Learning for ML
3. Advanced Statistical Learning for DL

The course starts on 15 Jan 2022, at 13:00 to 15:00 (Istanbul time):

Course Fee:
Free for unemployed attendees. :)
200 USD for employed candidates :).

Course contents:
https://lnkd.in/dcXKxUjE

Course Registration:
https://lnkd.in/dMpzMfMG

Please kindly share with the ones who are interested.

Here is the list of resources to learn Data Structures and Algorithms from beginner to advance:

📑 Prerequisite - MIT's Mathematics for Computer Science:
🔸 https://lnkd.in/ejdPkSs

📌 Khan Academy - Intro to algorithms:
🔸 https://lnkd.in/e8ZUWwz

📌 Introduction to Algorithms Book by Charles E. Leiserson, Clifford Stein, Ronald Rivest, and Thomas H. Cormen:
🔸 https://lnkd.in/e8iqvwn

📌 GeeksforGeeks - Data Structures Tutorials:
🔸https://lnkd.in/eiFACVV

📌 MIT - Introduction to Algorithms:
🔸 https://lnkd.in/eKavb3T

📌 Coursera - Data Structures and Algorithms Specialization:
🔸 https://lnkd.in/eDk8ZuY

📌 Coursera - Algorithms Specialization:
🔸 https://lnkd.in/ejJw5TV

📌 MIT - Advanced Data Structures:
🔸 https://lnkd.in/eKA7FD2

📌 GeeksforGeeks - Advanced Data Structures Tutorials:
🔸 https://lnkd.in/eu2J-Bm

💡 I also found this interesting website which explains Data Structures and Algorithms through animations -

🔸 https://visualgo.net/en


#منابع
#datastructures #algorithms #mathematics #machinelearning #computerscience

✴️ @AI_Python_EN
❇️ @AI_Python

📚 Mathematics for Machine Learning | University of California, Berkeley
کتاب آموزش ریاضیات برای یادگیری ماشین. خلاصه بودن کتاب می‌تونه توی روند آموزش مفید باشه.

👤 Garrett Thomas

🔗 http://gwthomas.github.io/docs/math4ml.pdf

#Machine_Learning #Mathematics
#Books

@ai_python

📌 Short Notes On Ai: Chris Albon
کریس‌البون؛ مدیر بخش یادگیری‌ماشین ویکی‌مدیا یک سایت داره که آموزش‌ها و مقالات مختلفی در زمینه پایتون، یادگیری‌ماشین و عملیات ماشین‌لرنینگ منتشر کرده. سر زدن بهش می‌تونه براتون مفید باشه.

🔗 https://chrisalbon.com/Home

#MachineLearning #NeuralNetworks
#LLMs #Python #OOP #MLOps
#Mathematics #DataScience

@ai_python

📚 Mathematics and probability for machine learning
تقریبا تمام تکنیک‌های علم داده و روش‌های یادگیری ماشین دارای برخی جنبه‌های عمیق ریاضیاتی هستن. در نتیجه برای درک بیشتر اونها بهتره یه تسلط نسبی به مباحث ریاضی داشته باشید. اینجا ۳ تا کتاب خوب برای ریاضیات و احتمال قرار دادم که می‌تونید استفاده کنید.

🔗 Mathematics for Machine Learning
🔗 Intro to Probability for Data Science
🔗 Mathematics for Machine Learning

#Mathematics #MachineLearning
#Probability #DataScience

@ai_python

📽 Mathematics for Ai | Persian Course
آموزش ریاضیات برای هوش‌مصنوعی و یادگیری ماشین به زبان فارسی

🔗 https://www.youtube.com/playlist?list=PLe_DUrcBPdbIRye6Dw_UdhR8a7Zqvb_BA

#Mathematics #Math
#Ai #MachineLearning

@ai_python

📽 Gilbert Strang lectures on Linear Algebra (MIT)
جبرخطی یکی از مباحث مهم در علم‌داده هست؛ اینجا لینک تدریس گیلبرت استرنگ که به تازگی بازنشست شده به همراه کتاب ایشون هست که می‌تونید استفاده کنید.

🔗 https://www.youtube.com/playlist?list=PL49CF3715CB9EF31D

#Algebra #Mathematics

@ai_python