http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
1 Introduction -- 1.1 Data-Driven Decision-Making and Society -- 1.2 Overview of Deep Learning -- 1.3 Bias and Variance -- 1.3.1 Skewness of Data -- 1.3.2 Bias -- 1.3.3 Variance -- 1.3.4 Trade-Off Between Bias and Variance -- 1.4 Supervised and Unsupervised Learning -- 1.5 Supportive Tools and Libraries -- 1.5.1 TensorFlow -- 1.5.2 Keras -- 1.5.3 PyTorch -- 1.5.4 Jupyter Notebook -- 1.5.5 NumPy and Pandas -- 1.5.6 Tensor Hub -- Review Questions
2 Concepts and Terminology -- 2.1 Understanding Neural Networks -- 2.2 Regression -- 2.2.1 Linear Regression -- 2.2.2 Logistic Regression -- 2.2.3 Other Regression Methods -- 2.3 Classification -- 2.4 Hyperparameters -- 2.4.1 Overview -- 2.4.2 Weight Initialization -- 2.4.3 Activation Function -- 2.4.4 Learning Rate -- 2.4.5 Loss Function -- 2.4.6 Other Hyperparameters -- 2.5 Model Training -- 2.5.1 Model Selection -- 2.5.2 Model Convergence -- 2.5.3 Overfitting and Underfitting -- 2.5.4 Regularization -- 2.5.5 Network Gradients -- Review Questions
3 State-Of-The-Art Deep Learning Models: Part I -- 3.1 Overview of Neural Networks -- 3.2 Artificial Neural Networks -- 3.3 Recurrent Neural Network (RNN) -- 3.4 Convolutional Neural Networks -- 3.4.1 Overview of Convolutional Neural Network -- 3.4.2 Concepts of CNN -- 3.4.3 Convolutional Layer -- 3.4.4 Pooling Layer -- 3.4.5 Fully Connected Layer -- 3.5 Comparison of ANN, RNN, and CNN -- Review Questions -- 4 State-Of-The-Art Deep Learning Models: Part II -- 4.1 Feed-Forward Neural Network -- 4.2 Multi-Layer Perceptrons -- 4.3 Generative Adversarial Network (GAN) -- 4.4 Variations of CNNs
4.4.1 Residual Networks (ResNet) -- 4.4.2 Inception Model -- 4.4.3 GoogLeNet -- 4.4.4 Xception Model -- 4.4.5 DenseNet Model -- 4.4.6 MobileNet Model -- 4.4.7 VGG Model -- 4.4.8 Comparison of CNN Architectures -- 4.5 Capsule Network -- 4.6 Autoencoders -- 4.7 Transformers -- Review Questions -- 5 Advanced Learning Techniques -- 5.1 Transfer Learning -- 5.1.1 Overview of Transfer Learning -- 5.1.2 Transfer Learning Process -- 5.1.3 Transfer Learning Types, Categories, and Strategies -- 5.1.4 Transfer Learning Applications -- 5.1.5 Transfer Learning Challenges -- 5.2 Reinforcement Learning
5.2.1 Overview of Reinforcement Learning -- 5.2.2 Reinforcement Learning Process -- 5.2.3 Implementation and Scheduling Types -- 5.2.4 Applications of Reinforcement Learning -- 5.2.5 Challenges of Reinforcement Learning -- 5.3 Federated Learning -- 5.3.1 Overview of Federated Learning -- 5.3.2 Federated Learning Process -- 5.3.3 Types and Properties of Federated Learning -- 5.3.4 Applications of Federated Learning -- 5.3.5 Challenges of Federated Learning -- 5.4 Multi-Modeling With Ensemble Learning -- 5.4.1 Overview of Ensemble Learning -- 5.4.2 Ensemble Learning Process -- 5.4.3 Ensemble Learning Techniques
Includes bibliographical references and index.