Deep Learning Theory
Core ideas behind how neural networks learn
20 cards · first 3 free · flip each card to test yourself
04
Bias–Variance Tradeoff
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05
Regularization
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06
Normalization
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07
Weight Initialization
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08
Activation Functions
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09
Universal Approximation
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10
Generalization
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11
Optimizers: Momentum & Adam
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12
Learning-Rate Schedules
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13
Vanishing & Exploding Gradients
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14
Residual Connections
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15
Attention
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16
Convolutions & Inductive Bias
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17
Loss Functions
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18
Softmax & Temperature
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19
Batch Size & Gradient Noise
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20
Embeddings & Representation Learning
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