vault backup: 2023-06-06 11:48:49

Affected files:
STEM/AI/Neural Networks/CNN/Examples.md
STEM/AI/Neural Networks/CNN/FCN/FCN.md
STEM/AI/Neural Networks/CNN/FCN/ResNet.md
STEM/AI/Neural Networks/CNN/FCN/Skip Connections.md
STEM/AI/Neural Networks/CNN/GAN/DC-GAN.md
STEM/AI/Neural Networks/CNN/GAN/GAN.md
STEM/AI/Neural Networks/CNN/Interpretation.md
STEM/AI/Neural Networks/CNN/UpConv.md
STEM/AI/Neural Networks/Deep Learning.md
STEM/AI/Neural Networks/MLP/MLP.md
STEM/AI/Neural Networks/Properties+Capabilities.md
STEM/AI/Neural Networks/SLP/Least Mean Square.md
STEM/AI/Neural Networks/SLP/SLP.md
STEM/AI/Neural Networks/Transformers/Transformers.md
STEM/AI/Properties.md
STEM/CS/Language Binding.md
STEM/CS/Languages/dotNet.md
STEM/Signal Proc/Image/Image Processing.md

AI/Neural Networks/CNN/Examples.md

@@ -8,7 +8,7 @@
 # AlexNet
 2012
 
-- [[Activation Functions#ReLu|ReLu]]
+- [ReLu](../Activation%20Functions.md#ReLu)
 - Normalisation
 
 ![alexnet](../../../img/alexnet.png)
@@ -29,13 +29,13 @@
 2015
 
 - [Inception Layer](Inception%20Layer.md)s
-- Multiple [[Deep Learning#Loss Function|Loss]] Functions
+- Multiple [Loss](../Deep%20Learning.md#Loss%20Function) Functions
 
 ![googlenet](../../../img/googlenet.png)
 
 ## [Inception Layer](Inception%20Layer.md)
 ![googlenet-inception](../../../img/googlenet-inception.png)
-## Auxiliary [[Deep Learning#Loss Function|Loss]] Functions
+## Auxiliary [Loss](../Deep%20Learning.md#Loss%20Function) Functions
 - Two other SoftMax blocks
 - Help train really deep network
 	- Vanishing gradient problem

AI/Neural Networks/CNN/FCN/FCN.md

@@ -1,6 +1,6 @@
 Fully [Convolution](../../../../Signal%20Proc/Convolution.md)al Network
 
-[[Convolutional Layer|Convolutional]] and [[UpConv|up-convolutional layers]] with [[Activation Functions#ReLu|ReLu]] but no others (pooling)
+[Convolutional](../Convolutional%20Layer.md) and [up-convolutional layers](../UpConv.md) with [ReLu](../../Activation%20Functions.md#ReLu) but no others (pooling)
 - All some sort of Encoder-Decoder
 
 Contractive → [UpConv](../UpConv.md)
@@ -19,13 +19,13 @@ Contractive → [UpConv](../UpConv.md)
 - Rarely from scratch
 - Pre-trained weights
 - Replace final layers
-	- [[MLP|FC]] layers
+	- [FC](../../MLP/MLP.md) layers
 	- White-noise initialised
 - Add [UpConv](../UpConv.md) layer(s)
 	- Fine-tune train
 	- Freeze others
 	- Annotated GT images
-- Can use summed per-pixel log [[Deep Learning#Loss Function|loss]]
+- Can use summed per-pixel log [loss](../../Deep%20Learning.md#Loss%20Function)
 
 # Evaluation
 ![fcn-eval](../../../../img/fcn-eval.png)

AI/Neural Networks/CNN/FCN/ResNet.md

@@ -12,18 +12,18 @@
 
 # Design
 
-- Skips across pairs of [[Convolutional Layer|conv layers]]
+- Skips across pairs of [conv layers](../Convolutional%20Layer.md)
 	- Elementwise addition
 - All layer 3x3 kernel
 - Spatial size halves each layer
 - Filters doubles each layer
-- [[FCN|Fully convolutional]]
+- [Fully convolutional](FCN.md)
 	- No fc layer
-	- No [[Max Pooling|pooling]]
+	- No [pooling](../Max%20Pooling.md)
 		- Except at end
 	- No dropout
 
-[[Datasets#ImageNet|ImageNet]] Error:
+[ImageNet](../../CV/Datasets.md#ImageNet) Error:
 ![imagenet-error](../../../../img/imagenet-error.png)
 
 ![resnet-arch](../../../../img/resnet-arch.png)

AI/Neural Networks/CNN/FCN/Skip Connections.md

@@ -1,4 +1,4 @@
-- Output of [[Convolutional Layer|conv]], c, layers are added to inputs of [UpConv](../UpConv.md), d, layers
+- Output of [conv](../Convolutional%20Layer.md), c, layers are added to inputs of [UpConv](../UpConv.md), d, layers
 - Element-wise, not channel appending
 - Propagate high frequency information to later layers
 - Two types

AI/Neural Networks/CNN/GAN/DC-GAN.md

@@ -13,10 +13,10 @@ Deep [Convolutional](../../../../Signal%20Proc/Convolution.md) [GAN](GAN.md)
 - Discriminator
 	- Contractive
 	- Cross-entropy [loss](../../Deep%20Learning.md#Loss%20Function)
-	- [Conv](../Convolutional%20Layer.md) and leaky [[Activation Functions#ReLu|ReLu]] layers only
-	- Normalised output via [[Activation Functions#Sigmoid|sigmoid]]
+	- [Conv](../Convolutional%20Layer.md) and leaky [ReLu](../../Activation%20Functions.md#ReLu) layers only
+	- Normalised output via [sigmoid](../../Activation%20Functions.md#Sigmoid)
 
-## [[Deep Learning#Loss Function|Loss]]
+## [Loss](../../Deep%20Learning.md#Loss%20Function)
 $$D(S,L)=-\sum_iL_ilog(S_i)$$
 - $S$
 	- $(0.1, 0.9)^T$

AI/Neural Networks/CNN/GAN/GAN.md

@@ -1,12 +1,12 @@
 # Fully [Convolution](../../../../Signal%20Proc/Convolution.md)al
 - Remove [Max Pooling](../Max%20Pooling.md)
 	- Use strided [UpConv](../UpConv.md)
-- Remove [[MLP|FC]] layers
+- Remove [FC](../../MLP/MLP.md) layers
 	- Hurts convergence in non-classification
 - Normalisation tricks
 	- Batch normalisation
 		- Batches of 0 mean and variance 1
-	- Leaky [[Activation Functions#ReLu|ReLu]]
+	- Leaky [ReLu](../../Activation%20Functions.md#ReLu)
 
 # Stages
 ## Generator, G
@@ -27,5 +27,5 @@
 
 # Code Vector Math for Control
 ![cvmfc](../../../../img/cvmfc.png)
-- Do [[Interpretation#Activation Maximisation|AM]] to derive code for an image
+- Do [AM](../Interpretation.md#Activation%20Maximisation) to derive code for an image
 ![code-vector-math-for-control-results](../../../../img/code-vector-math-for-control-results.png)

AI/Neural Networks/CNN/Interpretation.md

@@ -1,13 +1,13 @@
 # Activation Maximisation
 - Synthesise an ideal image for a class
 	- Maximise 1-hot output
-	- Maximise [[Activation Functions#SoftMax|SoftMax]]
+	- Maximise [SoftMax](../Activation%20Functions.md#SoftMax)
 
 ![am](../../../img/am.png)
 - **Use trained network**
 	- Don't update weights
-- [[Architectures|Feedforward]] noise
-	- [[Back-Propagation|Back-propagate]] [[Deep Learning#Loss Function|loss]]
+- [Feedforward](../Architectures.md) noise
+	- [Back-propagate](../MLP/Back-Propagation.md) [loss](../Deep%20Learning.md#Loss%20Function)
 		- Don't update weights
 		- Update image
 
@@ -17,7 +17,7 @@
 - Prone to high frequency noise
 	- Minimise
 - Total variation
-	- $x^*$ is the best solution to minimise [[Deep Learning#Loss Function|loss]]
+	- $x^*$ is the best solution to minimise [loss](../Deep%20Learning.md#Loss%20Function)
 
 $$x^*=\text{argmin}_{x\in \mathbb R^{H\times W\times C}}\mathcal l(\phi(x),\phi_0)$$
 - Won't work

AI/Neural Networks/CNN/UpConv.md

@@ -9,7 +9,7 @@
 - Could specify kernel
 	- Or learn
 - Can have multiple upconv layers
-	- Separated by [[Activation Functions#ReLu|ReLu]]
+	- Separated by [ReLu](../Activation%20Functions.md#ReLu)
 	- For non-linear up-sampling conv
 	- Interpolation is linear
 

AI/Neural Networks/Deep Learning.md

@@ -8,7 +8,7 @@ Objective Function
 ![deep-loss-function](../../img/deep-loss-function.png)
 
 - Test accuracy worse than train accuracy = overfitting
-- [[MLP|Dense]] = [[MLP|fully connected]]
+- [Dense](MLP/MLP.md) = [fully connected](MLP/MLP.md)
 - Automates feature engineering
 
 ![ml-dl](../../img/ml-dl.png)

AI/Neural Networks/MLP/MLP.md

@@ -1,15 +1,15 @@
--   [[Architectures|Feedforward]]
+-   [Feedforward](../Architectures.md)
 -   Single hidden layer can learn any function
 	-   Universal approximation theorem
 -   Each hidden layer can operate as a different feature extraction layer
--   Lots of [[Weight Init|weights]] to learn
+-   Lots of [weights](../Weight%20Init.md) to learn
 -   [Back-Propagation](Back-Propagation.md) is supervised
 
 ![mlp-arch](../../../img/mlp-arch.png)
 
 # Universal Approximation Theory
-A finite [[Architectures|feedforward]] MLP with 1 hidden layer can in theory approximate any mathematical function
--   In practice not trainable with [[Back-Propagation|BP]]
+A finite [feedforward](../Architectures.md) MLP with 1 hidden layer can in theory approximate any mathematical function
+-   In practice not trainable with [BP](Back-Propagation.md)
 
 ![activation-function](../../../img/activation-function.png)
 ![mlp-arch-diagram](../../../img/mlp-arch-diagram.png)
@@ -19,4 +19,4 @@ A finite [[Architectures|feedforward]] MLP with 1 hidden layer can in theory app
 ![tlu](../../../img/tlu.png)
 - $o_1$ to $o_4$ must all be one to overcome -3.5 bias and force output to 1
 ![mlp-non-linear-decision](../../../img/mlp-non-linear-decision.png)
-- Can generate a non-linear [[Decision Boundary|decision boundary]]
+- Can generate a non-linear [decision boundary](Decision%20Boundary.md)

AI/Neural Networks/Properties+Capabilities.md

@@ -45,7 +45,7 @@
 	- Confidence value
 
 # Contextual Information
-- [[Neural Networks#Knowledge|Knowledge]] represented by structure and activation weight
+- [Knowledge](Neural%20Networks.md#Knowledge) represented by structure and activation weight
 	- Any neuron can be affected by global activity
 - Contextual information handled naturally
 

AI/Neural Networks/SLP/Least Mean Square.md

@@ -20,7 +20,7 @@ $$\frac{\partial \mathfrak{E}(w)}{\partial w(n)}=-x(n)\cdot e(n)$$
 $$\hat{g}(n)=-x(n)\cdot e(n)$$
 $$\hat{w}(n+1)=\hat{w}(n)+\eta \cdot x(n) \cdot e(n)$$
 
--   Above is a [[Architectures|feedforward]] loop around weight vector, $\hat{w}$
+-   Above is a [feedforward](../Architectures.md) loop around weight vector, $\hat{w}$
 	-   Behaves like low-pass filter
 		-   Pass low frequency components of error signal
 	-   Average time constant of filtering action inversely proportional to learning-rate

AI/Neural Networks/SLP/SLP.md

@@ -4,4 +4,4 @@ $$=w^T(n)x(n)$$
 ![slp-hyperplane](../../../img/slp-hyperplane.png)
 Perceptron learning is performed for a finite number of iteration and then stops
 
-[[Least Mean Square|LMS]] is continuous learning that doesn't stop
+[LMS](Least%20Mean%20Square.md) is continuous learning that doesn't stop

AI/Neural Networks/Transformers/Transformers.md

@@ -1,4 +1,4 @@
-- [[Attention|Self-attention]]
+- [Self-attention](Attention.md)
 	- Weighting significance of parts of the input
 		- Including recursive output
 - Similar to [RNN](../RNN/RNN.md)s
@@ -35,5 +35,5 @@
 	- Uses incorporated textual information to produce output
 	- Has attention to draw information from output of previous decoders before drawing from encoders
 - Both use [Attention](Attention.md)
-- Both use [[MLP|dense]] layers for additional processing of outputs
+- Both use [dense](../MLP/MLP.md) layers for additional processing of outputs
 	- Contain residual connections & layer norm steps

AI/Properties.md

@@ -1,7 +1,7 @@
 # Three Key Components
 
 1.  Representation
-	-   Declarative & Procedural [[Neural Networks#Knowledge|knowledge]]
+	-   Declarative & Procedural [knowledge](Neural%20Networks/Neural%20Networks.md#Knowledge)
 	-   Typically human-readable symbols
 2.  Reasoning
 	-   Ability to solve problems
@@ -36,13 +36,13 @@ Explanation-based learning uses both
 ## Level of Explanation
 -   Classical has emphasis on building symbolic representations
 	-   Models cognition as sequential processing of symbolic representations
--   [[Properties+Capabilities|Neural nets]] emphasis on parallel distributed processing models
+-   [Neural nets](Neural%20Networks/Properties+Capabilities.md) emphasis on parallel distributed processing models
 	-   Models assume information processing takes place through interactions of large numbers of neurons
 
 ## Processing style
 -   Classical processing is sequential
 	-   Von Neumann Machine
--   [[Properties+Capabilities|Neural nets]] use parallelism everywhere
+-   [Neural nets](Neural%20Networks/Properties+Capabilities.md) use parallelism everywhere
 	-   Source of flexibility
 	-   Robust
 
@@ -50,7 +50,7 @@ Explanation-based learning uses both
 -   Classical emphasises language of thought
 	-   Symbolic representation has quasi-linguistic structure
 	-   New symbols created from compositionality
--   [[Properties+Capabilities|Neural nets]] have problem describing nature and structure of representation
+-   [Neural nets](Neural%20Networks/Properties+Capabilities.md) have problem describing nature and structure of representation
 
 Symbolic AI is the formal manipulation of a language of algorithms and data representations in a top-down fashion
 

CS/Language Binding.md

@@ -24,5 +24,5 @@
 - Adobe Flash Player
 	- Tamarin
 - JVM
-- [[Compilers#LLVM|LLVM]]
+- [LLVM](Compilers.md#LLVM)
 - Silverlight

CS/Languages/dotNet.md

@@ -10,7 +10,7 @@
 -   JIT managed code into machine instructions
 -   Execution engine
 	-   VM
-	- [[Language Binding#Virtual Machines]]
+	- [Language Binding](../Language%20Binding.md#Virtual%20Machines)
 -   Services
 	-   Memory management
 	-   Type safety

Signal Proc/Image/Image Processing.md

@@ -1 +1 @@
-[[Convolution#Discrete]]
+[Convolution](../Convolution.md#Discrete)