Advanced decision sciences based on deep learning and ensemble learning algorithms : a practical approach using Python / S. Sumathi, PhD. [and three others].

Format:

Book

Author/Creator:

Sumathi, S., 1968- author.

Series:

Computer science, technology and applications.

Computer science, technology and applications

Language:

English

Subjects (All):

Decision support systems.

Physical Description:

1 online resource (370 pages)

Place of Publication:

New York : Nova Science Publishers, Inc., [2021]

Summary:

"Advanced Decision Sciences Based on Deep Learning and Ensemble Learning Algorithms: A Practical Approach Using Python describes the deep learning models and ensemble approaches applied to decision-making problems. The authors have addressed the concepts of deep learning, convolutional neural networks, recurrent neural networks, and ensemble learning in a practical sense providing complete code and implementation for several real-world examples. The authors of this book teach the concepts of machine learning for undergraduate and graduate-level classes and have worked with Fortune 500 clients to formulate data analytics strategies and operationalize these strategies. The book will benefit information professionals, programmers, consultants, professors, students, and industry experts who seek a variety of real-world illustrations with an implementation based on machine learning algorithms"-- Provided by publisher.

Contents:

Intro

Contents

Preface

Acknowledgments

Chapter 1

Introduction

Learning Outcomes

1.1. Introduction

1.2. Rationale

1.3. Linear Algebra for Decision Science

1.3.1. Eigenvectors in Data Science

1.4. Fundamentals of Machine Learning

1.4.1. How Deep Learning Works

1.4.2. How Artificial Intelligence Deep Learning and Machine Learning Interconnected with Each Other?

1.5. History of Deep Learning

1.6. Fundamentals of Neural Networks

1.6.1. Advantages

1.6.2. Disadvantages

1.6.3. Applications

1.7. Shallow Neural Networks

1.7.1. Activation Functions

1.7.2. Weight Initialization

1.7.3. Forward and Backward Propagation

1.8. Deep Neural Networks

1.8.1. Deep L-layer Neural Network

1.8.2. Forward and Backward Propagation

1.8.3. Deep Representations

1.9. Ensemble Learning

1.10. Real World Examples

1.10.1. Self Driving Cars

1.10.2. Natural Language Processing

1.10.3. Image and Visual Recognition

1.10.4. Fraud Detection

1.10.5. Virtual Assistants

1.10.6. Healthcare

1.10.7. Developmental Disorders in Children

Summary

Review Questions

Chapter 2

Deep Learning

2.1. Introduction

2.2. Implementation Aspects of Deep Learning

2.2.1. Train/Dev/Test Data Sets

2.2.1.1.Training Data

2.2.2. Bias and Variance

2.2.3. Regularisation and Dropout

2.2.3.1. The Mathematical Background of the Dropout Concept

2.2.3.2. Dropout Equivalent to Regularized Network

2.2.4. Rectified Linear Units (ReLU)

2.2.4.1. ReLU Activation Function

2.2.4.2. ReLU in Python

2.2.4.3. Advantages of the ReLU

Cheaper Computation

Representational Sparsity

Linear Behaviour

Effective Training of Deep Neural Networks

Effects of Rectified Linear Activation

Default Activation Function

Effect of Bias Input Value on ReLU.

ReLU for MLPs, CNNs, and Not for RNNs

Weight Initialization

Alternative Activation Functions to ReLU

2.2.5. Multi-Class Neural Networks: Softmax

2.2.5.1. Softmax Example

2.2.5.2. Softmax Using Python

2.3. Training a Deep Neural Network

2.3.1. Training Data

2.3.2. Choice of Activation Functions

2.3.3. Number of Hidden Units and Layers

2.3.4. Weight Initialization

2.3.5. Learning Rates

2.3.6. Hyperparameter Tuning

2.3.7. Learning Methods

2.3.7.1. Keep Dimensions of Weights in the Exponential Power of 2

2.3.7.2. Unsupervised Pretraining

2.3.7.3. Mini-Batch vs. Stochastic Learning

2.3.8. Dropout for Regularization

2.3.9. Training Iterations

2.4. Introduction to TensorFlow and Keras

2.4.1. TensorFlow

2.4.1.1.Getting Started with Tensorflow

2.4.1.2. Installing TensorFlow

2.4.1.3. Run a TensorFlow Container

2.4.1.4. Creating the First Program in Tensorflow

2.4.2. Keras

2.4.2.1. When to Use Keras

2.4.2.2. Getting Started with Keras

STEP 1: Installation of Keras on a System

STEP 2: Loading Data to Be Processed by the Deep Learning Net

STEP 3: Splitting the Data into Training and Testing Sets

STEP 4: Defining the Keras Based Deep Learning Model Architecture

STEP 5: Compiling the Keras Based Deep Learning Model

STEP 6: Training the Deep Learning Model on Your Training Data

STEP 7: Evaluating the Deep Learning Model on the Test Data

2.5. Autoencoders

2.5.1. Properties of Autoencoders

2.5.2. Types of Autoencoders

2.5.2.1. Under Complete Autoencoders

2.5.2.2. Overcomplete Autoencoders

2.5.2.3. Denoising Autoencoders

2.5.2.4. Sparse Autoencoders

2.5.2.5. Contractive Autoencoders

2.5.3. AutoEncoders - A Practical Example

2.6. Introduction to Microsoft Azure AI and ML Framework

2.6.1. Azure Machine Learning Model Workflow.

2.6.2. Tools for Azure Machine Learning

Chapter 3

Convolutional Neural Networks

3.1. Introduction

3.2. The Convolution Process

3.3. Convolutional Layer - The Kernel

3.4. Pooling Layer

3.5. The Architecture of CNN

3.6. CNN Training: Optimization

3.7. AlexNet

3.7.1. The Architecture

3.7.2. Training

3.7.3. AlexNet - A Practical Example

3.8. VGGNet

3.8.1. The Architecture

3.8.2. Training

3.8.3. Testing

3.8.4. VGGNet - A Practical Example

3.9. Residual Network

3.9.1. The Residual Block

3.9.2. ResNet Architecture

3.9.3. Training

3.9.4. ResNet - A Practical Example

STEP 1: Importing the libraries (Keras and its APIs)

STEP 2: Setting up Hyperparameters &amp

Data Pre-proceSsing

STEP 3: Setting Learning Rate for Different Number of Epochs

STEP 4: Basic ResNet Building Block

Output

3.10. Inception Network

3.10.1. The Effect of 1 × 1 Convolution

3.10.2. Inception Module

3.10.3. The Architecture

3.10.4. Training

3.10.5. InceptionNet - A Practical Example

STEP 1: Importing the Required Module

STEP 2: Creating Directories to Prepare for the Dataset

STEP 3: Storing the Dataset in the Directories and Plot Some Sample Images

STEP 4: Data Augmentation to Increase the Data Samples in the Dataset

STEP 5: Define the Base Model Using Inception API and a Callback Function to Train the Model

STEP 6: Plot the Training and Validation Accuracy along with Training and Validation Loss

Chapter 4

Recurrent Neural Networks

4.1. Introduction

4.2. The Architecture of Recurrent Neural Network

4.3. Types of RNN Architectures

4.4. Problems with RNNs

4.4.1. Vanishing Gradient Problem

4.4.2. Exploding Gradients Problem.

4.4.3. Long Term Dependency Problem

4.5. Long Short-Term Memory (LSTM)

4.5.1. An Improvement over RNN: LSTM

4.5.2. Architecture

4.5.2.1. Forget Gate

4.5.2.2. Input Gate

STEP 1: Regulating the information that has to be added to the cell state

STEP 2: Adding new information by creating a vector

STEP 3: Combining the regulated information and the new information and updating the cell state

4.5.2.3. Output Gate

4.6. Variants of LSTM

4.6.1. Peephole Connections

4.6.2. Coupled Gates

4.6.3. Gated Recurrent Unit

4.6.3.1. Update Gate

4.6.3.2. Reset Gate

4.6.3.3. Current Memory Content

4.6.3.4. Final Memory at Current Time Step

4.7. RNN - A Practical Example

STEP 1: Data Cleanup and Pre Processing

STEP 2: Making Data into Right Structure to Include Timesteps

STEP 3: Recurrent Neural Network setup

STEP 4: Prediction

STEP 5: Plotting the Data in Matplotlib

Chapter 5

Ensemble Learning

5.1. Introduction

5.2. Ensemble Learning Methods

5.2.1. Hard Voting

5.2.2. Weighted Majority Voting

5.2.3. Soft Voting

5.2.4. Averaging and Weighted Averaging

5.2.5. Stacking

5.3. Bagging

5.3.1. Bagging Steps

5.3.2. Advantages

5.3.3. Disadvantages

5.3.4. Python Syntax

5.4. Boosting

5.4.1. Difference between Bagging and Boosting

5.5. Ensemble Learning Algorithms

5.5.1. Bagging and Random Forest Algorithm

Algorithm 1 Bagging

Random Forests

Bagging in Random Forest

5.5.2. Boosting Algorithm

5.6. AdaBoost

5.6.1. AdaBoost Algorithm

AdaBoost.M1

5.6.2. AdaBoost Ensemble

5.6.3. Making Predictions with AdaBoost

5.7. XGBoost

5.7.1. XGBoost Algorithm

5.8. Boosting and Problem Motivation

5.8.1. Pipeline Description

5.9. Ensemble Methods Using AdaBoost: A Practical Example.

5.9.1. Regression for AdaBoost

5.10. Applications of Ensemble Methods

Chapter 6

Implementing DL and Ensemble Learning Models: Real World Use Cases

6.1. Introduction

6.2. Use Case 1: Plant Species Identification Using Image Classifier

6.2.1. The Python Program

# Introduction: Tea Leaves Classification - Understanding the Data

# Data Preparation

# Model Building

# Comparing Multiple Classifiers for Accuracy

6.2.2. Conclusion

6.3. Use Case 2: Using Ensemble Methods to Predict Customer Churn

6.3.1. Understanding the Data

6.3.2. Problem Statement

6.4. Use Case 3: Using Long Short-Term Memory (LSTM) RNN in Keras for Sequence Classification Using IMDB Movie Review Database

6.4.1. Background

6.4.2. Understanding the Data

6.4.3. Summary

6.5. Use Case 4: Loan Eligibility Prediction by Employing Gradient Boosting Classifier

6.5.1. Background

6.5.2. Understanding of the Data

6.5.3. Conclusion

6.6. Use Case 5: Resume Parsing with NLP Python OCR and Spacy

6.6.1. Background

6.6.2. Understanding the Data

6.6.3. Results and Discussion

6.6.4. Summary

Appendix

Deep Learning Cheat Sheets

Using KERAS

Data Load

Data Preprocessing

Creating the Train and Test Datasets into X and y Variables

Model Architecture

Binary Classification

Multi-Class Classification

Regression

Convolution Neural Network (CNN)

Recurrent Neural Network (RNN)

Model Compilation

ANN: Multi-Class Classification

ANN: Regression

Model Training

Model Prediction

Save/Reload Models

Using OpenCV

Playing with Images

Image Resize

Image Rotation

B &amp

W Images

Drawing Bounding Box in the Image

Face Detection

Saving the Image

Suggested Reading

References

Websites.

About the Authors.

Notes:

Description based on print version record.

Includes bibliographical references and index.

ISBN:

1-68507-207-0

OCLC:

1281957273