Sentiment Analysis

  1. Deep Learning for Sentiment Analysis
    1. Neural Network Fundamentals
      1. Perceptrons and Multi-layer Perceptrons
        1. Activation Functions
          1. Loss Functions
            1. Optimization Algorithms
              1. Regularization Techniques
              2. Recurrent Neural Networks
                1. Vanilla RNNs
                  1. Architecture
                    1. Vanishing Gradient Problem
                    2. Long Short-Term Memory
                      1. LSTM Architecture
                        1. Forget Gate
                          1. Input Gate
                            1. Output Gate
                              1. Bidirectional LSTM
                                1. Stacked LSTM
                                2. Gated Recurrent Units
                                  1. GRU Architecture
                                    1. Reset Gate
                                      1. Update Gate
                                        1. Comparison with LSTM
                                      2. Convolutional Neural Networks for Text
                                        1. 1D Convolutions
                                          1. Multiple Filter Sizes
                                            1. Pooling Operations
                                              1. Multi-channel CNNs
                                                1. CNN-LSTM Combinations
                                                2. Attention Mechanisms
                                                  1. Attention Concept
                                                    1. Self-Attention
                                                      1. Multi-Head Attention
                                                        1. Attention Visualization
                                                        2. Transformer Architecture
                                                          1. Encoder-Decoder Structure
                                                            1. Position Encoding
                                                              1. Layer Normalization
                                                                1. Feed-Forward Networks
                                                                2. Pre-trained Language Models
                                                                  1. Transfer Learning Principles
                                                                    1. Fine-tuning Strategies
                                                                      1. Domain Adaptation
                                                                        1. Few-Shot Learning
                                                                        2. Advanced Architectures
                                                                          1. Hierarchical Attention Networks
                                                                            1. Memory Networks
                                                                              1. Graph Neural Networks
                                                                                1. Capsule Networks
                                                                                2. Training Considerations
                                                                                  1. Data Augmentation
                                                                                    1. Batch Processing
                                                                                      1. Learning Rate Scheduling
                                                                                        1. Early Stopping
                                                                                          1. Model Checkpointing