vault backup: 2023-06-01 08:11:37
Affected files: .obsidian/graph.json .obsidian/workspace.json Money/Assets/Derivative.md STEM/AI/Neural Networks/CNN/Examples.md STEM/AI/Neural Networks/Deep Learning.md STEM/AI/Neural Networks/MLP/Decision Boundary.md STEM/CS/Languages/dotNet.md STEM/Semiconductors/Equations.md Tattoo/Engineering.md
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# LeNet
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# LeNet
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- 1990's
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- 1990's
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![[lenet-1989.png]]
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- 1989
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- 1989
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![[lenet-1998.png]]
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- 1998
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- 1998
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# AlexNet
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# AlexNet
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- [[Activation Functions#ReLu|ReLu]]
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- [[Activation Functions#ReLu|ReLu]]
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- Normalisation
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- Normalisation
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![[alexnet.png]]
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# VGG
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# VGG
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2015
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2015
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- Similar kernel size throughout
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- Similar kernel size throughout
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- Gradual filter increase
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- Gradual filter increase
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![[vgg-spec.png]]
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![[vgg-arch.png]]
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# GoogLeNet
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# GoogLeNet
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2015
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2015
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- [[Inception Layer]]s
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- [[Inception Layer]]s
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- Multiple [[Deep Learning#Loss Function|Loss]] Functions
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- Multiple [[Deep Learning#Loss Function|Loss]] Functions
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![[googlenet.png]]
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## [[Inception Layer]]
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## [[Inception Layer]]
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![[googlenet-inception.png]]
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## Auxiliary [[Deep Learning#Loss Function|Loss]] Functions
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## Auxiliary [[Deep Learning#Loss Function|Loss]] Functions
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- Two other SoftMax blocks
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- Two other SoftMax blocks
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- Help train really deep network
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- Help train really deep network
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- Vanishing gradient problem
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- Vanishing gradient problem
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![[googlenet-auxilliary-loss.png]]
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![[deep-digit-classification.png]]
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# Loss Function
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# Loss Function
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Objective Function
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Objective Function
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- [[Back-Propagation]]
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- [Back-Propagation](MLP/Back-Propagation.md)
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- Difference between predicted and target outputs
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- Difference between predicted and target outputs
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![[deep-loss-function.png]]
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- Test accuracy worse than train accuracy = overfitting
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- Test accuracy worse than train accuracy = overfitting
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- [[MLP|Dense]] = [[MLP|fully connected]]
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- [[MLP|Dense]] = [[MLP|fully connected]]
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- Automates feature engineering
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- Automates feature engineering
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![[ml-dl.png]]
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These are the two essential characteristics of how deep learning learns from data: the incremental, layer-by-layer way in which increasingly complex representations are developed, and the fact that these intermediate incremental representations are learned jointly, each layer being updated to follow both the representational needs of the layer above and the needs of the layer below. Together, these two properties have made deep learning vastly more successful than previous approaches to machine learning.
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These are the two essential characteristics of how deep learning learns from data: the incremental, layer-by-layer way in which increasingly complex representations are developed, and the fact that these intermediate incremental representations are learned jointly, each layer being updated to follow both the representational needs of the layer above and the needs of the layer below. Together, these two properties have made deep learning vastly more successful than previous approaches to machine learning.
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Evaluate
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Evaluate
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# Data Structure
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# Data Structure
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- [[Tensor]] flow = channels last
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- [Tensor](../../Maths/Tensor.md) flow = channels last
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- (samples, height, width, channels)
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- (samples, height, width, channels)
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- Vector data
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- Vector data
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- 2D [[tensor]]s of shape (samples, features)
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- 2D [tensors](../../Maths/Tensor.md) of shape (samples, features)
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- Time series data or sequence data
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- Time series data or sequence data
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- 3D [[tensor]]s of shape (samples, timesteps, features)
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- 3D [tensors](../../Maths/Tensor.md) of shape (samples, timesteps, features)
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- Images
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- Images
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- 4D [[tensor]]s of shape (samples, height, width, channels) or (samples, channels, height, Width)
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- 4D [tensors](../../Maths/Tensor.md) of shape (samples, height, width, channels) or (samples, channels, height, Width)
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- Video
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- Video
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- 5D [[tensor]]s of shape (samples, frames, height, width, channels) or (samples, frames, channels , height, width)
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- 5D [tensors](../../Maths/Tensor.md) of shape (samples, frames, height, width, channels) or (samples, frames, channels , height, width)
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![[photo-tensor.png]]
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![[matrix-dot-product.png]]
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![[hidden-neuron-decision.png]]
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![[mlp-xor.png]]
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![[mlp-xor-2.png]]
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- Portable executable (PE)
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- Portable executable (PE)
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- DLL, EXE
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- DLL, EXE
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![[cli-infrastructure.png]]
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$$V_{bi} = \frac{kT}{q}ln(\frac{N_D N_A}{n_i^2})$$
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$$V_{bi} = \frac{kT}{q}ln(\frac{N_D N_A}{n_i^2})$$
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- $V_{bi}$ = Built-in Potential
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- $V_{bi}$ = Built-in Potential
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[[Doping]]
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[Doping](Doping.md)
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$$J=nev$$
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$$J=nev$$
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- $n$ = Charge Density
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- $n$ = Charge Density
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- $e$ = Charge
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- $e$ = Charge
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