Book description
What Every Engineer Should Know About DataDriven Analytics provides a comprehensive introduction to the machine learning theoretical concepts and approaches that are used in predictive data analytics through practical applications and case studies.
Table of contents
 Cover Page
 Half Title page
 Series Page
 Title Page
 Copyright Page
 Dedication
 Contents
 Preface
 Acknowledgments
 About the Authors
 1 Data Collection and Cleaning
 2 Mathematical Background for Predictive Analytics
 3 Introduction to Statistics, Probability, and Information Theory for Analytics
 4 Introduction to Machine Learning
 5 Unsupervised Learning
 6 Supervised Learning
 7 Natural Language Processing for Analyzing Unstructured Data

8 Predictive Analytics Using Deep Neural Networks
 Introduction to Deep Learning
 The Deep Neural Networks and Its Architectural Variants
 Multilayer Perceptron (MLP)
 Convolutional Neural Networks (CNN)
 Recurrent Neural Networks (RNN)
 AlexNet
 VGGNet
 Inception
 ResNet and GoogLeNet
 Hyperparameters of DNN and Strategies for Tuning Them
 Activation Function
 Regularization
 Number of Hidden Layers
 Number of Neurons Per Layer
 Learning Rate
 Optimizer
 Batch Size
 Epoch
 Weight and Biases Initialization
 Grid Search
 Random Search
 Deep Belief Networks (DBN)
 Analyzing the Boston Housing Dataset Using DNN
 Summary
 Exercise
 References
 9 Convolutional Neural Networks (CNN) for Predictive Analytics
 10 Recurrent Neural Networks (RNNs) for Predictive Analytics
 11 Recommender Systems for Predictive Analytics
 12 Architecting Big Data Analytical Pipeline
 Glossary of Terms
 Index
Product information
 Title: What Every Engineer Should Know About DataDriven Analytics
 Author(s):
 Release date: April 2023
 Publisher(s): CRC Press
 ISBN: 9781000859720
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