5.3 Neural Networks Nomenclature (Glossary)
5.3.1 Activation Function
5.3.2 Dense Layer
5.3.3 Perceptron
5.3.4 Loss Function
5.3.5 Gradient Descent
5.3.6 Epoch
5.3.7 Backpropagation
5.3.8 Learning Rate
5.3.9 Batch
5.3.10 Bias
5.3.11 Weight
5.3.12 Validation
5.3.13 Generalization
5.3.14 Overfitting
5.3.15 Underfitting
5.3.16 Regression
5.3.17 Target/Desired output
5.3.18 Test Set
5.3.19 Mini Batch
5.3.20 Regularization
5.3.21 L1 and L2 Norms
5.3.22 Hyper Parameters
5.3.23 Grid Search
5.3.24 Confusion Matrix
5.3.25 Supervised and Unsupervised Learning
5.3.26 Classification
5.3.27 One-hot
5.3.28 Softmax
5.3.29 Cross Entropy
5.3.30 SVM
5.3.31 MSE and MAE
5.3.32 Dropout
5.3.33 Feature Vector
5.3.34 Minima & Maxima
5.3.35 Linear separability
5.3.36 Global Minima/Maxima
5.3.37 Local Minima/Maxima
5.3.38 Strong Minima/Maxima
5.3.39 Weak Minima/Maxima
5.3.40 Data Augmentation
5.3.41 Tensor
5.3.42 Cost Function
5.3.43 Hidden layer
5.3.44 Initialization
5.3.45 Hyperplane
5.3.46 MNIST
5.3.47 Multilayer Perceptron (MLP)
5.3.48 Normalization
5.3.49 Vanishing Gradient
5.3.50 Exploding Gradient
5.3.51 VGG
5.3.52 Word2vec
5.3.53 Annotation
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