From bfdc107e5dccd17dd28de8085cf2a1a4f20aef06 Mon Sep 17 00:00:00 2001
From: andy <andy@sarsoo.xyz>
Date: Wed, 31 May 2023 22:11:34 +0100
Subject: [PATCH] vault backup: 2023-05-31 22:11:33

Affected files:
.obsidian/workspace.json
STEM/AI/Neural Networks/Activation Functions.md
STEM/AI/Neural Networks/Transformers/Attention.md
STEM/CS/ABI.md
---
 AI/Neural Networks/Activation Functions.md   | 4 ++--
 AI/Neural Networks/Transformers/Attention.md | 4 ++--
 CS/ABI.md                                    | 8 ++++----
 3 files changed, 8 insertions(+), 8 deletions(-)

diff --git a/AI/Neural Networks/Activation Functions.md b/AI/Neural Networks/Activation Functions.md
index f716bf7..5ffe7aa 100644
--- a/AI/Neural Networks/Activation Functions.md	
+++ b/AI/Neural Networks/Activation Functions.md	
@@ -38,7 +38,7 @@ y_j(n)(1-y_j(n))$$
 - Nice derivative
 - Max value of $\varphi_j'(v_j(n))$ occurs when $y_j(n)=0.5$
 - Min value of 0 when $y_j=0$ or $1$
-- Initial [[Weight Init|weights]] chosen so not saturated at 0 or 1
+- Initial [weights](Weight%20Init.md) chosen so not saturated at 0 or 1
 
 If $y=\frac u v$
 Where $u$ and $v$ are differential functions
@@ -53,7 +53,7 @@ Rectilinear
 - For deep networks
 - $y=max(0,x)$
 - CNNs
-	- Breaks associativity of successive [[convolution]]s
+	- Breaks associativity of successive [convolutions](../../Signal%20Proc/Convolution.md)
 		- Critical for learning complex functions
 	- Sometimes small scalar for negative
 		- Leaky ReLu
diff --git a/AI/Neural Networks/Transformers/Attention.md b/AI/Neural Networks/Transformers/Attention.md
index f7aeffa..851c730 100644
--- a/AI/Neural Networks/Transformers/Attention.md	
+++ b/AI/Neural Networks/Transformers/Attention.md	
@@ -6,8 +6,8 @@
 	- Sigma pi units
 	- Hyper-networks
 - Draw from relevant state at any preceding point along sequence
-	- Addresses [[RNN]]s vanishing gradient issues
-	- [[LSTM]] tends to poorly preserve far back [[Neural Networks#Knowledge|knowledge]]
+	- Addresses [RNNs](../RNN/RNN.md) vanishing gradient issues
+	- [LSTM](../RNN/LSTM.md) tends to poorly preserve far back [knowledge](../Neural%20Networks.md#Knowledge)
 - Attention layer access all previous states and weighs according to learned measure of relevance
 	- Allows referring arbitrarily far back to relevant tokens
 - Can be addd to [[RNN]]s
diff --git a/CS/ABI.md b/CS/ABI.md
index 8d3da10..5a49574 100644
--- a/CS/ABI.md
+++ b/CS/ABI.md
@@ -2,7 +2,7 @@
 	- Machine code therefore hardware-dependent
 - API defines this structure in source code
 - Adherence usually responsibility of
-	- [[Compilers]]
+	- [Compilers](Compilers.md)
 	- OS
 	- Library author
 
@@ -13,7 +13,7 @@
 	- Stack organisation
 	- Memory access types
 - Size, layouts and alignments of basic data types
-- [[Calling Conventions]]
+- [Calling Conventions](Calling%20Conventions.md)
 	- How function arguments are passed
 		- Stack or register
 		- Which registers for which function param
@@ -31,5 +31,5 @@
 # Embedded ABI
 - File format, data types, register usage, stack frame organisation, function parameter passing conventions
 	- For embedded OS
-- [[Compilers]] create object code compatible with code from other [[compilers]]
-	- Link libraries from different [[compilers]]
\ No newline at end of file
+- [Compilers](Compilers.md) create object code compatible with code from other [compilers](Compilers.md)
+	- Link libraries from different [compilers](Compilers.md)
\ No newline at end of file