Advanced Decision Sciences Based on Deep Learning and Ensemble Learning Algorithms: A Practical Approach Using Python

Name: Advanced Decision Sciences Based on Deep Learning and Ensemble Learning Algorithms: A Practical Approach Using Python
Brand: Nova Science Publishers Inc
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Paneerselvam Surekha(Author)

Nova Science Publishers Inc

Published on 20. September 2021

357 pages

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978-1-68507-207-0 (ISBN)

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Content

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
Learning Outcomes
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
Summary
Review Questions
Chapter 3
Convolutional Neural Networks
Learning Outcomes
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 & 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
Summary
Review Questions
Chapter 4
Recurrent Neural Networks
Learning Outcomes
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
Summary
Review Questions
Chapter 5
Ensemble Learning
Learning Outcomes
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
Summary
Review Questions
Chapter 6
Implementing DL and Ensemble Learning Models: Real World Use Cases
Learning Outcomes
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
Review Questions
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 & W Images
Drawing Bounding Box in the Image
Face Detection
Saving the Image
Suggested Reading
References
Websites
About the Authors
His Core Leadership Skills include:
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Advanced Decision Sciences Based on Deep Learning and Ensemble Learning Algorithms: A Practical Approach Using Python

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