"Elements: SGD, Adam, RProp, L-BFGS
Components: Gradients, Weights, Biases
Functions: Parameter update, Model improvement
Qualities: Online, Batch, Mini-batch

Data Pre-processing:

Elements: Normalization, Standardization, Encoding, Imputation
Components: Mean, Standard deviation, One-hot encoding, Imputation strategy
Functions: Data cleaning, Data preparation
Qualities: Numeric, Categorical, Ordinal

Evaluation Metrics:

Elements: Accuracy, Precision, Recall, F1-Score, ROC-AUC
Components: Confusion matrix, True positive, False positive, True negative, False negative
Functions: Model performance evaluation
Qualities: Binary, Multi-class, Regression

Data Augmentation:

Elements: Random cropping, Flipping, Rotation, Scaling, Gaussian noise
Components: Image data, Augmentation techniques
Functions: Data expansion, Data diversification
Qualities: Image, Audio, Text

Embeddings:

Dependencies: Numpy, PyTorch
Elements: Embedding layer
Components: Vocabulary, Pretrained embeddings
Functions: Forward pass, Backward pass
Qualities: Trainable, Non-trainable

Attention:

Dependencies: Numpy, PyTorch
Elements: Attention mechanism
Components: Scaled dot-product attention
Functions: Forward pass, Backward pass
Qualities: Trainable, Non-trainable

Layers:

Dependencies: Numpy, PyTorch
Elements: Linear layer
Components: Non-linear activation function
Functions: Forward pass, Backward pass
Qualities: Trainable, Non-trainable

Loss Function:

Dependencies: Numpy, PyTorch
Elements: Cross entropy loss
Components: Log softmax function
Functions: Loss calculation, Backward pass
Qualities: Trainable, Non-trainable

Optimizers:

Dependencies: PyTorch
Elements: Adam, SGD
Components: Learning rate schedule
Functions: Parameter update
Qualities: Trainable, Non-trainable"
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