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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.
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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