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Machine Learning Quick Reference

You're reading from Machine Learning Quick Reference

Product type Book

Published in Jan 2019

Publisher Packt

ISBN-13 9781788830577

Pages 294 pages

Edition 1st Edition

Languages

Concepts

Machine Learning

Author (1):

Rahul Kumar

Table of Contents (18) Chapters

Title Page

Copyright and Credits

About Packt

Contributors

Preface

1. Quantifying Learning Algorithms

2. Evaluating Kernel Learning

3. Performance in Ensemble Learning

4. Training Neural Networks

5. Time Series Analysis

6. Natural Language Processing

7. Temporal and Sequential Pattern Discovery

8. Probabilistic Graphical Models

9. Selected Topics in Deep Learning

10. Causal Inference

11. Advanced Methods

1. Other Books You May Enjoy

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Index

Summary

In this chapter, we have learned about neural networks along with their working, and were introduced to backward propagation and the activation function. We studied network initialization and how can we initialize the different types of models. We learned about overfitting and dropouts in the neural network scenario.

We introduced the concept of RNN, and studied a use case regarding the Google stock price dataset. In the next chapter, we will study time series analysis.

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Authors (1)

Rahul Kumar

Rahul Kumar has got more than 10 years of experience in the space of Data Science and Artificial Intelligence. His expertise lies in the machine learning and deep learning arena. He is known to be a seasoned professional in the area of Business Consulting and Business Problem Solving, fuelled by his proficiency in machine learning and deep learning. He has been associated with organizations such as Mercedes-Benz Research and Development (India), Fidelity Investments, Royal Bank of Scotland among others. He has accumulated a diverse exposure through industries like BFSI, telecom and automobile. Rahul has also got papers published in IIM and IISc Journals.

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