Deep Learning and Neural Networks

  1. Recurrent Neural Networks (RNNs)
    1. Sequential Data Processing
      1. Characteristics of Sequential Data
        1. Temporal Dependencies
          1. Variable-Length Sequences
            1. Applications of Sequential Data
            2. The Structure of RNNs
              1. Recurrent Connections
                1. Hidden State Concept
                  1. Mathematical Formulation
                    1. Unfolding Through Time
                      1. Computational Graphs for Sequences
                        1. Backpropagation Through Time (BPTT)
                      2. Types of RNN Architectures
                        1. One-to-One
                          1. One-to-Many
                            1. Many-to-One
                              1. Many-to-Many
                                1. Sequence-to-Sequence
                                2. Training RNNs
                                  1. Backpropagation Through Time
                                    1. Truncated BPTT
                                      1. Gradient Flow in Time
                                      2. Problems with Simple RNNs
                                        1. Vanishing Gradient Problem
                                          1. Exploding Gradient Problem
                                            1. Short-Term Memory Limitations
                                              1. Long-Term Dependencies Challenge
                                              2. Long Short-Term Memory (LSTM) Networks
                                                1. Motivation for LSTMs
                                                  1. LSTM Architecture
                                                    1. Cell State
                                                      1. Information Flow
                                                        1. Long-Term Memory Storage
                                                        2. Gate Mechanisms
                                                          1. Forget Gate
                                                            1. Function and Implementation
                                                              1. Selective Forgetting
                                                              2. Input Gate
                                                                1. Function and Implementation
                                                                  1. Information Selection
                                                                  2. Output Gate
                                                                    1. Function and Implementation
                                                                      1. Output Control
                                                                    2. Candidate Values
                                                                      1. Hidden State Updates
                                                                      2. LSTM Variants
                                                                        1. Peephole Connections
                                                                          1. Coupled Forget and Input Gates
                                                                        2. Gated Recurrent Units (GRUs)
                                                                          1. Simplified Gated Architecture
                                                                            1. GRU Components
                                                                              1. Update Gate
                                                                                1. Function and Implementation
                                                                                2. Reset Gate
                                                                                  1. Function and Implementation
                                                                                  2. Candidate Hidden State
                                                                                  3. Comparison to LSTM
                                                                                    1. Parameter Efficiency
                                                                                      1. Performance Trade-offs
                                                                                    2. Advanced RNN Architectures
                                                                                      1. Bidirectional RNNs
                                                                                        1. Forward and Backward Processing
                                                                                          1. Information Integration
                                                                                          2. Deep RNNs
                                                                                            1. Stacked RNN Layers
                                                                                              1. Hierarchical Representations
                                                                                              2. Attention Mechanisms in RNNs
                                                                                                1. Attention Weights
                                                                                                  1. Context Vectors
                                                                                                2. Applications of RNNs
                                                                                                  1. Natural Language Processing
                                                                                                    1. Language Modeling
                                                                                                      1. Text Generation
                                                                                                        1. Sentiment Analysis
                                                                                                          1. Named Entity Recognition
                                                                                                          2. Time-Series Forecasting
                                                                                                            1. Stock Price Prediction
                                                                                                              1. Weather Forecasting
                                                                                                              2. Speech Recognition
                                                                                                                1. Acoustic Modeling
                                                                                                                  1. Sequence Alignment
                                                                                                                  2. Machine Translation
                                                                                                                    1. Encoder-Decoder Architecture
                                                                                                                      1. Sequence-to-Sequence Models

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                                                                                                                  5. Convolutional Neural Networks (CNNs)

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                                                                                                                  7. The Transformer Architecture