vault backup: 2023-06-04 22:30:39

Affected files:
.obsidian/app.json
.obsidian/workspace-mobile.json
.obsidian/workspace.json
STEM/AI/Neural Networks/CNN/CNN.md
STEM/AI/Neural Networks/CNN/GAN/cGAN.md
STEM/AI/Neural Networks/MLP/Back-Propagation.md

AI/Neural Networks/CNN/CNN.md

@@ -42,13 +42,13 @@
 
 ![fine-tuning-freezing](../../../img/fine-tuning-freezing.png)
 # Training
-- Validation & training [loss](../Deep%20Learning.md#Loss Function)
+- Validation & training [loss](../Deep%20Learning.md#Loss%20Function)
 - Early
 	- Under-fitting
 	- Training not representative
 - Later
 	- Overfitting
-- V.[loss](../Deep%20Learning.md#Loss Function) can help adjust learning rate
+- V.[loss](../Deep%20Learning.md#Loss%20Function) can help adjust learning rate
 	- Or indicate when to stop training
 
 ![under-over-fitting](../../../img/under-over-fitting.png)

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

@@ -1,6 +1,6 @@
 Conditional [GAN](GAN.md)
 
-- Hard to control with [AM](../Interpretation.md#Activation Maximisation)
+- Hard to control with [AM](../Interpretation.md#Activation%20Maximisation)
 	- Unconditional [GAN](GAN.md)
 - Condition synthesis on a class label
 - Concatenate unconditional code with conditioning vector

AI/Neural Networks/MLP/Back-Propagation.md

@@ -22,16 +22,14 @@ $$\frac{\partial\mathfrak E(n)}{\partial w_{ji}(n)}=
 $$
 
 #### From 4
-$$\frac{\partial\mathfrak E(n)}{\partial e_j(n)}=
-e_j(n)$$
+$$\frac{\partial\mathfrak E(n)}{\partial e_j(n)}=e_j(n)$$
+
 #### From 1
 $$\frac{\partial e_j(n)}{\partial y_j(n)}=-1$$
 #### From 3 (note prime)
-$$\frac{\partial y_j(n)}{\partial v_j(n)}=
-\varphi_j'(v_j(n))$$
+$$\frac{\partial y_j(n)}{\partial v_j(n)}=\varphi_j'(v_j(n))$$
 #### From 2
-$$\frac{\partial v_j(n)}{\partial w_{ji}(n)}=
-y_i(n)$$
+$$\frac{\partial v_j(n)}{\partial w_{ji}(n)}=y_i(n)$$
 
 ## Composite
 $$\frac{\partial\mathfrak E(n)}{\partial w_{ji}(n)}=
@@ -40,10 +38,9 @@ $$\frac{\partial\mathfrak E(n)}{\partial w_{ji}(n)}=
 y_i(n)
 $$
 
-$$\Delta w_{ji}(n)=
--\eta\frac{\partial\mathfrak E(n)}{\partial w_{ji}(n)}$$
-$$\Delta w_{ji}(n)=
-\eta\delta_j(n)y_i(n)$$
+$$\Delta w_{ji}(n)=-\eta\frac{\partial\mathfrak E(n)}{\partial w_{ji}(n)}$$
+$$\Delta w_{ji}(n)=\eta\delta_j(n)y_i(n)$$
+
 ## Gradients
 #### Output Local
 $$\delta_j(n)=-\frac{\partial\mathfrak E (n)}{\partial v_j(n)}$$