Geometry of Deep Learning: A Signal Processing Perspective

By Jong Chul Ye

Part I Basic Tools for Machine Learning: 1. Mathematical Preliminaries.- 2. Linear and Kernel Classifiers.- 3. Linear, Logistic, and Kernel Regression.- 4. Reproducing Kernel Hilbert Space, Representer Theorem.- Part II Building Blocks of Deep Learning: 5. Biological Neural Networks.- 6. Artificial Neural Networks and Backpropagation.- 7. Convolutional Neural Networks.- 8. Graph Neural Networks.- 9. Normalization and Attention.- Part III Advanced Topics in Deep Learning.- 10. Geometry of Deep Neural Networks.- 11. Deep Learning Optimization.- 12. Generalization Capability of Deep Learning.- 13. Generative Models and Unsupervised Learning.- Summary and Outlook.- Bibliography.- Index.

• Format: Hardcover
• Language: English
• ISBN: 981166045X
• ISBN13: 9789811660450
• Release Date: January 2022
• Publisher: Springer
• Length: 330 Pages
• Weight: 1.42 lbs.
• Dimensions: 0.8" x 6.1" x 9.2"