Communication Principles for Data Science
portes grátis
Communication Principles for Data Science
Suh, Changho
Springer Verlag, Singapore
06/2023
283
Dura
Inglês
9789811980077
15 a 20 dias
Descrição não disponível.
Preface.- Acknowledgements.- Part 1. Communication over the Gaussian channel.- Chapter 1.Overview of the book.- Chapter 2. A statistical model for additive noise channels.- Chapter 3. Additive Gaussian noise model.- Problem Set 1.- Chapter 4. Optimal receiver: maximum A Posteriori (MAP) principle.- Chapter 5. Analysis of error probability.- Chapter 6. Multiple bits transmission via pulse amplitude modulation.- Problem Set 2.- Chapter 7. Multi-shot communication.- Chapter 8. Repetition coding.- Chapter 9: Capacity of the additive white Gaussian noise channel.- Problem Set 3.- Part 2. Communication over inter-symbol interference (ISI) channels.- Chapter 10. Signal conversion from discrete to continuous time (1/2).- Chapter 11. Signal conversion from discrete to continuous time (2/2).- Chapter 12. Optimal receiver architecture.- Problem Set 4.- Chapter 13. Optimal receiver in ISI channels: maximum likelihood (ML) sequence detection.- Chapter 14. Optimal receiver in ISI channels: Viterbi algorithm.- Problem Set 5.- Chapter 15.Orthogonal frequency division multiplexing (1/3).- Chapter 16. Orthogonal frequency division multiplexing (2/3).- Chapter 17. Orthogonal frequency division multiplexing (3/3).- Problem Set 6.- Part 3.Data science applications.- Chapter 18. Community detection as a communication problem.- Chapter 19. Community detection: ML principle.- Chapter 20. Community detection: An efficient algorithm.- Chapter 21. Community detection: Python implementation.- Problem Set 7.- Chapter 22.Haplotype phasing as a communication problem.- Chapter 23. Haplotype phasing: ML principle.- Chapter 24: Haplotype phasing: An efficient algorithm.
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Probability and Random Processes;Signals and Systems;Digital Communication;Data Science;Machine Learning;Community Detection;Computational Biology;Speech Recognition;Python;TensorFlow
Preface.- Acknowledgements.- Part 1. Communication over the Gaussian channel.- Chapter 1.Overview of the book.- Chapter 2. A statistical model for additive noise channels.- Chapter 3. Additive Gaussian noise model.- Problem Set 1.- Chapter 4. Optimal receiver: maximum A Posteriori (MAP) principle.- Chapter 5. Analysis of error probability.- Chapter 6. Multiple bits transmission via pulse amplitude modulation.- Problem Set 2.- Chapter 7. Multi-shot communication.- Chapter 8. Repetition coding.- Chapter 9: Capacity of the additive white Gaussian noise channel.- Problem Set 3.- Part 2. Communication over inter-symbol interference (ISI) channels.- Chapter 10. Signal conversion from discrete to continuous time (1/2).- Chapter 11. Signal conversion from discrete to continuous time (2/2).- Chapter 12. Optimal receiver architecture.- Problem Set 4.- Chapter 13. Optimal receiver in ISI channels: maximum likelihood (ML) sequence detection.- Chapter 14. Optimal receiver in ISI channels: Viterbi algorithm.- Problem Set 5.- Chapter 15.Orthogonal frequency division multiplexing (1/3).- Chapter 16. Orthogonal frequency division multiplexing (2/3).- Chapter 17. Orthogonal frequency division multiplexing (3/3).- Problem Set 6.- Part 3.Data science applications.- Chapter 18. Community detection as a communication problem.- Chapter 19. Community detection: ML principle.- Chapter 20. Community detection: An efficient algorithm.- Chapter 21. Community detection: Python implementation.- Problem Set 7.- Chapter 22.Haplotype phasing as a communication problem.- Chapter 23. Haplotype phasing: ML principle.- Chapter 24: Haplotype phasing: An efficient algorithm.
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