7. Covered Topics
Category
Topic
Key Concepts
D2L Reference
Foundations
Single Neuron Model
Inputs, weights, bias
Ch. 3.1
 
 
 
 		 Net value
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Ch. 3.1
 
 
 
 
Activation (transfer) functions
Ch. 3.1
 
 
 
 
Linear vs sigmoid activation
Ch. 3.1
Geometry
Geometric Interpretation
Net value as a hyperplane
Ch. 3.1.1
 
 
 
 
Decision boundaries
Ch. 3.1.1
Regression
Linear Regression
Model formulation
Ch. 3.1
 
 
 
 
Neural networks for regression
Ch. 3.1
Error Metrics
Error Calculation
Sample-wise error
Ch. 3.1.
 
 
Mean Squared Error (MSE)
Title: TexMaths - Description: 12§display§\frac{1}{N}\sum (y-\hat{y})^2§svg§600§FALSE§
Ch. 3.1.
 
 
Mean Absolute Error (MAE)
Title: TexMaths - Description: 12§display§\frac{1}{N}\left\lvert \sum (y-\hat{y})\right\rvert§svg§600§FALSE§
 
 
Training Concepts
Epoch
One full pass over training data
Ch. 3.1
 
 
Numerical Derivatives
Finite / centered difference
Ch. 3.2
Multi-Layer Networks
Multi-layer Neurons
Layered architectures
Ch. 5.1
 
 
Weight Matrices
Matrix-based formulation
Ch. 5.1
 
 
Bias in Weight Matrix
Augmented input representation
Ch. 5.1
Computational Graphs
 Forward propagation
Backward propagation
 Chain rule
Local derivatives
Ch. 5.3
 
 
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