Introduction to Artificial Intelligence

  1. Neural Networks and Deep Learning
    1. Biological Inspiration
      1. Neuron Structure and Function
        1. Synaptic Transmission
          1. Neural Networks in the Brain
            1. Artificial Neuron Model
            2. Perceptron and Linear Models
              1. Single Perceptron
                1. Mathematical Model
                  1. Learning Algorithm
                    1. Geometric Interpretation
                      1. Limitations
                      2. Multi-Layer Perceptrons
                        1. Network Architecture
                          1. Universal Approximation Theorem
                            1. XOR Problem Solution
                          2. Feedforward Neural Networks
                            1. Network Architecture
                              1. Input Layer
                                1. Hidden Layers
                                  1. Output Layer
                                    1. Weight Matrices
                                    2. Activation Functions
                                      1. Sigmoid Function
                                        1. Hyperbolic Tangent
                                          1. ReLU and Variants
                                            1. Softmax Function
                                              1. Choosing Activation Functions
                                              2. Forward Propagation
                                                1. Matrix Operations
                                                  1. Layer-by-Layer Computation
                                                    1. Output Generation
                                                  2. Training Neural Networks
                                                    1. Backpropagation Algorithm
                                                      1. Chain Rule Application
                                                        1. Gradient Computation
                                                          1. Weight Update Rules
                                                            1. Implementation Details
                                                            2. Gradient Descent Variants
                                                              1. Batch Gradient Descent
                                                                1. Stochastic Gradient Descent
                                                                  1. Mini-Batch Gradient Descent
                                                                    1. Momentum
                                                                      1. Adam Optimizer
                                                                      2. Regularization Techniques
                                                                        1. L1 and L2 Regularization
                                                                          1. Dropout
                                                                            1. Batch Normalization
                                                                              1. Early Stopping
                                                                              2. Hyperparameter Tuning
                                                                                1. Learning Rate Selection
                                                                                  1. Network Architecture Design
                                                                                    1. Validation Strategies
                                                                                  2. Deep Learning Architectures
                                                                                    1. Convolutional Neural Networks
                                                                                      1. Convolution Operation
                                                                                        1. Feature Maps
                                                                                          1. Pooling Layers
                                                                                            1. CNN Architecture Design
                                                                                              1. Applications in Computer Vision
                                                                                              2. Recurrent Neural Networks
                                                                                                1. Sequence Modeling
                                                                                                  1. Hidden State
                                                                                                    1. Vanishing Gradient Problem
                                                                                                      1. LSTM Networks
                                                                                                        1. GRU Networks
                                                                                                          1. Applications in NLP
                                                                                                          2. Advanced Architectures
                                                                                                            1. Autoencoders
                                                                                                              1. Generative Adversarial Networks
                                                                                                                1. Transformer Networks
                                                                                                                  1. Attention Mechanisms
                                                                                                                2. Deep Learning in Practice
                                                                                                                  1. Data Preprocessing
                                                                                                                    1. Normalization
                                                                                                                      1. Data Augmentation
                                                                                                                        1. Handling Missing Data
                                                                                                                        2. Transfer Learning
                                                                                                                          1. Pre-trained Models
                                                                                                                            1. Fine-tuning
                                                                                                                              1. Feature Extraction
                                                                                                                              2. Model Deployment
                                                                                                                                1. Model Compression
                                                                                                                                  1. Inference Optimization
                                                                                                                                    1. Production Considerations

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                                                                                                                                6. Natural Language Processing