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Practical Machine Learning Cookbook

You're reading from Practical Machine Learning Cookbook Supervised and unsupervised machine learning simplified

Product type Paperback

Published in Apr 2017

Publisher Packt

ISBN-13 9781785280511

Length 570 pages

Edition 1st Edition

Languages

Python

Tools

Apache Spark

Concepts

Machine Learning

Author (1):

Atul Tripathi

View More author details

Table of Contents (15) Chapters

Preface

1. Introduction to Machine Learning FREE CHAPTER

2. Classification

3. Clustering

4. Model Selection and Regularization

5. Nonlinearity

6. Supervised Learning

7. Unsupervised Learning

8. Reinforcement Learning

9. Structured Prediction

10. Neural Networks

11. Deep Learning

12. Case Study - Exploring World Bank Data

Exploring World Bank data

13. Case Study - Pricing Reinsurance Contracts

Pricing reinsurance contracts

14. Case Study - Forecast of Electricity Consumption

Recurrent neural networks - predicting periodic signals

Oscillators are circuits that produce specific, periodic waveforms such as square, triangular, sawtooth, and sinusoidal. In order to generate output, oscillators generally use some form of active device-lamp, which is surrounded by resistors, capacitors, and inductors. Two main classes of oscillators are relaxation and sinusoidal. Triangular, sawtooth and other non-sinusoidal waveforms are generated using relaxation oscillators, while sinusoidal oscillators consist of amplifiers with external components to generate oscillation. Normally, no harmonics are present in pure sine waves and they consist of a single frequency.

Getting ready...

The task is to predict a cosine from a noisy sine wave. 5Hz frequency waves are used for the sine wave with some normally distributed noise and a smooth cosine wave. The dataset created is a set of 10 sequences, each of which consists of 40 observations.

How to do it...

The following packages need to be...

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Authors (1)

Atul Tripathi

Atul Tripathi

Atul Tripathi has spent more than 11 years in the fields of machine learning and quantitative finance. He has a total of 14 years of experience in software development and research. He has worked on advanced machine learning techniques, such as neural networks and Markov models. While working on these techniques, he has solved problems related to image processing, telecommunications, human speech recognition, and natural language processing. He has also developed tools for text mining using neural networks. In the field of quantitative finance, he has developed models for Value at Risk, Extreme Value Theorem, Option Pricing, and Energy Derivatives using Monte Carlo simulation techniques.

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