Data Science Machine Learning Data Analysis
@DataScienceM
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This channel is for Programmers, Coders, Software Engineers.

1- Data Science
2- Machine Learning
3- Data Visualization
4- Artificial Intelligence
5- Data Analysis
6- Statistics
7- Deep Learning

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Data Science Machine Learning Data Analysis
37.9K subscribers
Data Science Machine Learning Data Analysis
📚 The Ultimate Challenge: The 3x+1 Problem (2011)

🔗 Download Link: https://file.lu/d/1Zct

💬 Tags: #math

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Data Science Machine Learning Data Analysis
📚 Essential Math for AI (2023)

🔗 Download Link: https://file.lu/d/1ZeZ

💬 Tags: #math #AI

⛔️ ➕ interaction = ➕ books

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Data Science Machine Learning Data Analysis
📚 Practical Mathematics for AI and Deep Learning (2022)

🔗 Download Link: https://filelu.com/r6eu96o4uzfs

💬 Tags: #math

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Data Science Machine Learning Data Analysis
📚 Mathematics for machine learning (2021)

🔗 Download Link: https://file.lu/d/1Vjt

💬 Tags: #math

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Data Science Machine Learning Data Analysis
📚 Practical Mathematics for AI and Deep Learning (2022)

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💬 Tags: #math

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Data Science Machine Learning Data Analysis
📚 Mathematical Analysis of Machine Learning Algorithms (2023)

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💬 Tags: #ML #MATH

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Data Science Machine Learning Data Analysis
📚 Math and Architectures of Deep Learning (2023 - V10)

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💬 Tags: #deeplearning #math

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Data Science Machine Learning Data Analysis
📚 Exploring University Mathematics with Python (2023)

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💬 Tags: #math

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Data Science Machine Learning Data Analysis
📚 Applied Math for Security (2023)

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💬 Tags: #Math

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Data Science Machine Learning Data Analysis
📚 Mathematical Methods using Python (2024)

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💬 Tags: #math

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Data Science Machine Learning Data Analysis
📚 Mathematical Logic through Python (2024)

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💬 Tags: #math

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Data Science Machine Learning Data Analysis
📚 Math Adventures with Python (2020)

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Data Science Machine Learning Data Analysis
📚 15 Math Concepts Every Data Scientist Should Know (2024)

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Data Science Machine Learning Data Analysis
Mathematical Theory of Deep Learning.pdf
7.8 MB
Unlock the Secrets of #DeepLearning with Math!
Excited to share a free resource for all data science enthusiasts! "Mathematical Theory of Deep Learning" by Philipp Petersen and Jakob Zech is now available on #arXiv.

This book breaks down the core pillars of deep learning with rigorous yet accessible #math. Perfect for grad students, researchers, or anyone curious about why neural networks work so well!

Key Takeaways:
Mastering feedforward neural networks and ReLU's expressive power
Exploring gradient descent, backpropagation, and the loss landscape
Unraveling generalization, double descent, and adversarial robustness.

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