Predictive Analytics

  1. Neural Networks and Deep Learning
    1. Neural Network Fundamentals
      1. Biological Inspiration
        1. Artificial Neuron Model
          1. Activation Functions
            1. Step Function
              1. Sigmoid Function
                1. Hyperbolic Tangent
                  1. ReLU and Variants
                    1. Softmax Function
                    2. Network Architecture
                      1. Input Layer
                        1. Hidden Layers
                          1. Output Layer
                            1. Fully Connected Networks
                          2. Single Layer Networks
                            1. Perceptron
                              1. Linear Separability
                                1. Learning Algorithm
                                  1. Convergence Properties
                                  2. Linear Regression as Neural Network
                                    1. Logistic Regression as Neural Network
                                    2. Multi-Layer Perceptrons
                                      1. Universal Approximation Theorem
                                        1. Hidden Layer Design
                                          1. Weight Initialization Strategies
                                            1. Forward Propagation
                                              1. Backpropagation Algorithm
                                                1. Chain Rule Application
                                                  1. Gradient Computation
                                                    1. Weight Update Rules
                                                  2. Training Neural Networks
                                                    1. Loss Functions
                                                      1. Mean Squared Error
                                                        1. Cross-entropy Loss
                                                          1. Hinge Loss
                                                          2. Optimization Algorithms
                                                            1. Gradient Descent
                                                              1. Stochastic Gradient Descent
                                                                1. Mini-batch Gradient Descent
                                                                  1. Adam Optimizer
                                                                    1. RMSprop
                                                                    2. Regularization Techniques
                                                                      1. L1 and L2 Regularization
                                                                        1. Dropout
                                                                          1. Batch Normalization
                                                                            1. Early Stopping
                                                                          2. Deep Learning Architectures
                                                                            1. Convolutional Neural Networks
                                                                              1. Convolution Operation
                                                                                1. Pooling Layers
                                                                                  1. Feature Maps
                                                                                    1. Applications in Structured Data
                                                                                    2. Recurrent Neural Networks
                                                                                      1. Sequence Processing
                                                                                        1. Vanishing Gradient Problem
                                                                                          1. LSTM Networks
                                                                                            1. GRU Networks
                                                                                            2. Autoencoders
                                                                                              1. Dimensionality Reduction
                                                                                                1. Anomaly Detection
                                                                                                  1. Feature Learning

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                                                                                              5. Ensemble Methods

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                                                                                              7. Time Series Analysis and Forecasting