Neural Networks
32 videos • 12,583 views • by Jeff Orchard
1
Course Introduction
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2
Hodgkin-Huxley Neuron Model
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3
Simpler Neuron Models
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4
Synapses
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5
Learning Overview
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6
Universal Approximation Theorem
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7
Loss Functions
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8
Gradient Descent
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9
Error Backpropagation
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10
Automatic Differentiation
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11
Backprop with AD
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12
PyTorch
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13
Overfitting
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14
Combatting Overfitting
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15
Vanishing/Exploding Gradients
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16
Enhancing Optimization
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17
Autoencoders
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18
Your Visual System
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19
Convolutional Neural Networks
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20
Batch Normalization
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21
Hopfield Networks
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22
Week 08c: Hopfield Demonstration
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23
Variational Autoencoders
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24
RNNs and Backprop Through Time
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25
Long Short-Term Memory (LSTM)
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26
Adversarial Attacks
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27
Adversarial Defence
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28
Generative Adversarial Networks (GANs)
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29
Vector Embeddings
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30
Predictive Coding
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31
Loss Functions
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32
Transformations
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