Machine Learning with Python

  1. Introduction to Deep Learning
    1. Deep Learning Fundamentals
      1. From Shallow to Deep Learning
        1. Limitations of Traditional ML
          1. Representation Learning
            1. Feature Hierarchy
            2. Neural Network Basics
              1. Biological Inspiration
                1. Artificial Neurons
                  1. Network Architecture
                  2. Deep Learning Applications
                    1. Computer Vision
                      1. Natural Language Processing
                        1. Speech Recognition
                          1. Recommendation Systems
                        2. Artificial Neural Networks
                          1. Perceptron
                            1. Single Layer Perceptron
                              1. Linear Separability
                                1. Perceptron Learning Rule
                                  1. Limitations
                                  2. Multi-Layer Perceptrons
                                    1. Hidden Layers
                                      1. Universal Approximation Theorem
                                        1. Network Depth vs Width
                                        2. Activation Functions
                                          1. Linear Activation
                                            1. Sigmoid Function
                                              1. Vanishing Gradient Problem
                                              2. Hyperbolic Tangent
                                                1. Zero-Centered Output
                                                2. ReLU Family
                                                  1. Rectified Linear Unit
                                                    1. Leaky ReLU
                                                      1. Parametric ReLU
                                                        1. ELU
                                                          1. Swish
                                                        2. Forward Propagation
                                                          1. Input Processing
                                                            1. Layer-wise Computation
                                                              1. Output Generation
                                                              2. Backpropagation
                                                                1. Chain Rule Application
                                                                  1. Gradient Computation
                                                                    1. Weight Updates
                                                                      1. Computational Efficiency
                                                                      2. Loss Functions
                                                                        1. Regression Losses
                                                                          1. Mean Squared Error
                                                                            1. Mean Absolute Error
                                                                              1. Huber Loss
                                                                              2. Classification Losses
                                                                                1. Binary Cross-Entropy
                                                                                  1. Categorical Cross-Entropy
                                                                                    1. Sparse Categorical Cross-Entropy
                                                                                  2. Optimization Algorithms
                                                                                    1. Gradient Descent Variants
                                                                                      1. Batch Gradient Descent
                                                                                        1. Stochastic Gradient Descent
                                                                                          1. Mini-Batch Gradient Descent
                                                                                          2. Adaptive Optimizers
                                                                                            1. AdaGrad
                                                                                              1. RMSprop
                                                                                                1. Adam
                                                                                                  1. AdamW
                                                                                                  2. Learning Rate Scheduling
                                                                                                    1. Step Decay
                                                                                                      1. Exponential Decay
                                                                                                        1. Cosine Annealing
                                                                                                      2. Regularization Techniques
                                                                                                        1. Weight Decay
                                                                                                          1. Dropout
                                                                                                            1. Batch Normalization
                                                                                                              1. Early Stopping

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                                                                                                          5. Unsupervised Learning Algorithms

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                                                                                                          7. Deep Learning with Python Frameworks