Deep Learning Foundations [electronic resource] / Taeho Jo.

Intro
Preface
Part I: Foundation
Part II: Deep Machine Learning
Part III: Deep Neural Networks
Part IV: Textual Deep Learning
Contents
Part I Foundation
1 Introduction
1.1 Definition of Deep Learning
1.2 Swallow Learning
1.2.1 Supervised Learning
1.2.2 Unsupervised Learning
1.2.3 Semi-supervised Learning
1.2.4 Reinforcement Learning
1.3 Deep Supervised Learning
1.3.1 Input Encoding
1.3.2 Output Encoding
1.3.3 Unsupervised Layer
1.3.4 Convolution
1.4 Advanced Learning Types
1.4.1 Ensemble Learning
1.4.2 Local Learning

1.4.3 Kernel-Based Learning
1.4.4 Incremental Learning
1.5 Summary and Further Discussions
References
2 Supervised Learning
2.1 Introduction
2.2 Simple Supervised Learning Algorithms
2.2.1 Rule-Based Approach
2.2.2 Naive Retrieval
2.2.3 Data Similarity
2.2.4 One Nearest Neighbor
2.3 Neural Networks
2.3.1 Artificial Neuron
2.3.2 Activation Functions
2.3.3 Neural Connection
2.3.4 Perceptron
2.4 Advanced Supervised Learning Algorithms
2.4.1 Naive Bayes
2.4.2 Decision Tree
2.4.3 Random Forest
2.4.4 Support Vector Machine

2.5 Summary and Further Discussions
References
3 Unsupervised Learning
3.1 Introduction
3.2 Simple Unsupervised Learning Algorithms
3.2.1 AHC Algorithm
3.2.2 Divisive Algorithm
3.2.3 Online Linear Clustering Algorithm
3.2.4 K Means Algorithm
3.3 Kohonen Networks
3.3.1 Initial Version
3.3.2 Learning Vector Quantization
3.3.3 Semi-supervised Model
3.3.4 Self-Organizing Map
3.4 EM Algorithm
3.4.1 Cluster Distributions
3.4.2 Notations
3.4.3 E-Step
3.4.4 M-Step
3.5 Summary and Further Discussions
Reference
4 Ensemble Learning

4.1 Introduction
4.2 Partition
4.2.1 Training Set
4.2.2 Attribute Set
4.2.3 Array Partition
4.2.4 Partition Schemes
4.3 Supervised Combination Schemes
4.3.1 Voting
4.3.2 Expert Gate
4.3.3 Cascading
4.3.4 Cellular Learning
4.4 Multiple Viewed Learning
4.4.1 Views
4.4.2 Multiple Encodings
4.4.3 Multiple Viewed Supervised Learning
4.4.4 Multiple Viewed Unsupervised Learning
4.5 Summary and Further Discussions
Part II Deep Machine Learning
5 Deep KNN Algorithm
5.1 Introduction
5.2 Swallow Version
5.2.1 KNN Algorithm
5.2.2 KNN Variants

5.2.3 Trainable KNN Algorithm
5.2.4 Radius Nearest Neighbor
5.3 Basic Deep Versions
5.3.1 Feature Reduction
5.3.2 Kernel-Based KNN Algorithm
5.3.3 Output Decoded KNN
5.3.4 Pooled KNN
5.4 Advanced Deep Versions
5.4.1 Unsupervised Layer
5.4.2 Unsupervised KNN
5.4.3 Stacked KNN
5.4.4 Convolutional KNN Algorithm
5.5 Summary and Further Discussions
Reference
6 Deep Probabilistic Learning
6.1 Introduction
6.2 Swallow Version
6.2.1 Normal Distribution
6.2.2 Bayes Classifier
6.2.3 Naive Bayes
6.2.4 Bayesian Networks
6.3 Basic Deep Versions