Machine Learning: Origins, Developments and Implications
First published:
ISBN: 978-1-78272-445-2
If you are a Risk.net subscriber you are entitled to 20% off your Risk books purchases. Please email enquiries@riskbooks.com for more information.
As part of your Risk.net subscription you are entitled to 20% off all of your Risk Books purchases. If you would like to place an order please email enquiries@riskbooks.com
Deep learning networks
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
The emergence of deep learning network applications has created a new era in machine learning. Speech recognition, language translation, image processing, autonomous driving and chatbots are examples of the potential of deep learning neural networks. And these are just the beginning. This chapter presents an overview of the general types of deep learning networks and their potential and considers examples of each of these applications. Such networks include multilayer perceptrons, convolutional networks, recurrent neural networks and long short-term memory (LSTM) networks.
13.1 AN OVERVIEW OF DEEP LEARNING NETWORKS
What is meant by “deep learning” neural networks? Leaders and experts in the field have various ideas of what deep learning is. The term deep learning originated with Hinton et al (2006), who used the phrase “deep” to describe the development of large artificial neural networks. Hinton and Salakhutdinov (2006) stuck with the term “deep” to describe their approach to developing networks with many more hidden layers than was previously typical. Thus, the name deep originated to describe neural networks with more than one hidden layer, as shown in Figure 13.1. Deep neural
Copyright Infopro Digital Limited. All rights reserved.
As outlined in our terms and conditions, https://www.infopro-digital.com/terms-and-conditions/subscriptions/ (point 2.4), printing is limited to a single copy.
If you would like to purchase additional rights please email info@risk.net
Copyright Infopro Digital Limited. All rights reserved.
You may share this content using our article tools. As outlined in our terms and conditions, https://www.infopro-digital.com/terms-and-conditions/subscriptions/ (clause 2.4), an Authorised User may only make one copy of the materials for their own personal use. You must also comply with the restrictions in clause 2.5.
If you would like to purchase additional rights please email info@risk.net
