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Intro
Preface
Reference
Acknowledgement
Contents
1 Communication over the AWGN Channel
1.1 Overview of the Book
1.2 A Statistical Model for Additive Noise Channels
1.3 Additive Gaussian Noise Channel and Python Exercise
1.4 Optimal Receiver Principle
1.5 Error Probability Analysis and Python Simulation
1.6 Multiple Bits Transmission using PAM
1.7 Multi-shot Communication: Sequential Coding
1.8 Multi-shot Communication: Repetition Coding
1.9 Capacity of the AWGN Channel
References
2 Communication over ISI Channels
2.1 Waveform Shaping (1/2)

2.2 Waveform Shaping (2/2)
2.3 Optimal Receiver Architecture
2.4 Optimal Receiver in ISI Channels
2.5 The Viterbi Algorithm
2.6 The Viterbi Algorithm: Python Implementation
2.7 OFDM: Principle
2.8 OFDM: Extension to General L-tap ISI Channels
2.9 OFDM: Transmission and Python Implementation
References
3 Data Science Applications
3.1 Community Detection as a Communication Problem
3.2 Community Detection: The ML Principle
3.3 An Efficient Algorithm and Python Implementation
3.4 Haplotype Phasing as a Communication Problem

3.5 Haplotype Phasing: The ML Principle
3.6 An Efficient Algorithm and Python Implementation
3.7 Speech Recognition as a Communication Problem
3.8 Speech Recognition: Statistical Modeling
3.9 Speech Recognition: The Viterbi Algorithm
3.10 Machine Learning: Connection with Communication
3.11 Logistic Regression and the ML Principle
3.12 Machine Learning: TensorFlow Implementation
References
Appendix A Python Basics
A.1 Jupyter Notebook
A.2 Basic Syntaxes of Python
A.2.1 Data Structure
A.2.2 Package
A.2.3 Visualization

Appendix B TensorFlow and Keras Basics
Reference

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