Abstract
This chapter will focus on theory behind some bleeding-edge techniques and will prepare you to solve problems in univarient and multivarient data for time-series models using deep learning (covered in Chapters 6 and 7). In this chapter, we’ll start with an introduction to neural networks by reviewing perceptrons, activation functions, backpropagation, types of gradient descent, recurrent neural networks, long short-term memory, gated recurrent units, convolutional neural networks, and auto-encoders.
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© 2020 B V Vishwas and Ashish Patel
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Vishwas, B.V., Patel, A. (2020). Bleeding-Edge Techniques. In: Hands-on Time Series Analysis with Python. Apress, Berkeley, CA. https://doi.org/10.1007/978-1-4842-5992-4_5
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DOI: https://doi.org/10.1007/978-1-4842-5992-4_5
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Publisher Name: Apress, Berkeley, CA
Print ISBN: 978-1-4842-5991-7
Online ISBN: 978-1-4842-5992-4
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