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Machine Learning for Time-Series with Python

You're reading from   Machine Learning for Time-Series with Python Forecast, predict, and detect anomalies with state-of-the-art machine learning methods

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Product type Paperback
Published in Oct 2021
Publisher Packt
ISBN-13 9781801819626
Length 370 pages
Edition 1st Edition
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Author (1):
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Ben Auffarth Ben Auffarth
Author Profile Icon Ben Auffarth
Ben Auffarth
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Table of Contents (15) Chapters Close

Preface 1. Introduction to Time-Series with Python 2. Time-Series Analysis with Python FREE CHAPTER 3. Preprocessing Time-Series 4. Introduction to Machine Learning for Time-Series 5. Forecasting with Moving Averages and Autoregressive Models 6. Unsupervised Methods for Time-Series 7. Machine Learning Models for Time-Series 8. Online Learning for Time-Series 9. Probabilistic Models for Time-Series 10. Deep Learning for Time-Series 11. Reinforcement Learning for Time-Series 12. Multivariate Forecasting 13. Other Books You May Enjoy
14. Index

Introduction to deep learning

Deep learning is based on fundamental concepts that find their roots early in the 20th century – the wiring between neurons. Neurons communicate chemically and electrically through so-called neurites.

This wiring was first described and drawn by Santiago Ramón y Cajal, a Spanish neuroscientist. He charted the anatomy of the brain and the structure of neural networks in the brain. He received the Nobel Prize in Physiology or Medicine in 1906, which he shared with Camillo Golgi, who invented the stains for neurons based on potassium dichromate and silver nitrate that Ramón y Cajal applied in his microscopy studies.

The chart below is just one of his elaborate drawings of the arborization of neural connections (called neurites – dendrites and axons) between neurons in the brain (source Wikimedia Commons):

ile:Debuixos Santiago Ramón y Cajal.jpg

Figure 10.1: Ramon y Cajal's drawing of networks of neurons in the brain

In the schematic, you can appreciate...

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