| Neural Networks |
Perceptron Basics |
Both |
| Gradient Descent |
Both |
| Backpropagation |
Both |
| Activation Functions (ReLU, Sigmoid, Tanh) |
Both |
| Cost Functions (MSE, MAE, Cross Entropy,
etc.) |
Both |
| Deep Learning |
Multi-Layer Perceptrons (MLP) |
Both |
| Stochastic Gradient Descent (SGD), MiniBatch Gradient Descent |
Both |
| Momentum Methods (Adam, AdamW) |
Practice |
| Adaptive Learning Rates |
Practice |
| Convergence and Learning Rates |
Both |
| Weight Regularization |
Practice |
| Early Stopping |
Practice |
| Dropout, Gaussian Noise |
Practice |
| Weight Initialization |
Practice |
| Batch Normalization |
Practice |
| Autoencoders and Sparse Encoders |
Practice |
