Machine Learning: Origins, Developments and Implications
First published:
ISBN: 978-1-78272-445-2
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Applications of deep learning networks
Preface
Introduction: human-machine entanglement
Machine learning: origins
Useful tools
Decision trees
Introduction to neural networks
Back-propagation
Regularisation
Optimisation
Building neural networks
Early applications of machine learning
Interpreting neural network decisions
Predicting corporate bond returns
Deep learning networks
Applications of deep learning networks
Machine intelligence
Consciousness
The future and its challenges
Artificial intelligence and the military
Final thoughts
Appendix
Epilogue
Acknowledgements
Having presented an overview of deep learning networks, we now consider some applications of deep learning technologies. The studies described in this chapter use natural language processing (NLP) to predict corporate bond prices.11 As before, the examples necessarily contain work done by me and colleagues, as these are the only models to which we have access. Incidentally, a “natural language” is one that has developed over time (as contrasted with an artificial language or computer code). NLP is an interdisciplinary subfield of linguistics, computer science and artificial intelligence concerned with the interactions between computers and human language. In particular, NLP applications program computers to process and analyse large amounts of natural language data. In several studies cited below, NLP applications used sentiment data on bond-issuing firms to predict corporate bond prices. While not all of these studies were successful at price prediction, they can be used to trace the application of sentiment data from relatively simple models to very complex ones. Our discussion of these studies is preceded by an overview of NLP methods.
14.1 AN OVERVIEW OF NATURAL LANGUAGE
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