A Chinese book! Solve almost any machine learning problem!

Today's recommendation

Signal Processing and Machine Learning Theory

Signal Processing and Machine Learning Theory

Suitable for people

Those who want to integrate classical signal processing methods and theories with the latest deep learning and machine learning algorithms (do paper innovation), and want to deeply understand the ideas of machine learning algorithms.

Why learn signal processing?

Many algorithms in machine learning share similarities with methods in signal processing, such as linear regression and Fourier transforms. Learning signal processing will give you a better understanding of the principles and application scenarios of these algorithms.

The author of the book

It is jointly produced by nearly 60 bigwigs in the field of signal processing and machine learning

Edited by Paul Diniz, Life Fellow of the Institute of Electrical and Electronics Engineers who has studied electrical engineering and artificial intelligence for nearly five decades

Features of this book:

This book introduces the basic theories and the latest technologies, methods, and principles of signal processing and machine learning.

Each chapter is written by a leading figure in the field and covers machine learning, autonomous vehicles, the Internet of Things, future wireless communications, medical imaging, and more.

It is recommended that you can find the direction you are interested in according to the table of contents, read and study carefully, and you can wait until you have time to learn other directions slowly.

directory

1. Introduction to the theory of signal processing and machine learning
2. Continuous-time signals and systems
3. Discrete-time signals and systems
4. Random signals and random processes
5. Sampling and quantization
6. Digital filter structure and its implementation
7. Multi-rate signal processing for software-defined radio architectures
8. Modern transformation design for practical audio/image/video coding applications
9. Data Representation: From Multiscale Transformation to Neural Networks
10. Internet
11. Frames in signal processing
12. Parameter estimation
13. Adaptive filters
14. machine learning
15. Getting Started with Graph Signal Processing
16. Tensor methods in deep learning
17. Non-convex learning: sparsity, heavy tailing, and clustering
18. Machine Learning Dictionary

shortcoming

Full English (can be solved with deepl or other translation software)

Longer length (you can read the basic chapters first, and then choose the chapters you are interested in)

PDF has a total of 1235 pages, which is very, very comprehensive, and students who need PDF learning can share this article and get it in the group.

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