Updated directory format

README.md

@@ -1,37 +1,12 @@
 # Machine-Learning-concepts
 This repository contains explanation for various Machine Learning and Deep Learning concepts with Python code implementations from scratch and/or using PyTorch.  
 
-## Non-Linearities  
-- [ ] Sigmoid  
-- [ ] Tanh  
-- [ ] ReLU  
-- [ ] LeakyReLU
+## [Non-Linearities](non-linearities)
 
-## Optimizers  
-- [ ] Stochastic Gradient Descent
-- [ ] Adam
-- [ ] AdaGrad
-- [ ] RMSprop
+## [Optimizers](optimizers)
 
-## Losses  
-- [ ] Mean Squared Error Loss/ L2 Loss  
-- [ ] Mean Absolute Error/ L1 Loss  
-- [ ] Cross Entropy Loss  
-- [ ] Hinge Loss  
-- [ ] Kullback-Leibler Loss  
-- [ ] Softmax  
-- [ ] AM-Softmax  
-- [ ] AAM-Softmax  
-- [ ] Prototypical Loss  
-- [ ] Angular Prototypical Loss  
+## [Losses](losses)
 
-## Architectures  
-- [ ] LeNet
-- [ ] AlexNet  
-- [ ] VGG-19  
-- [ ] ResNet-34  
-- [ ] ResNet-50  
+## [Architectures](architectures)
 
-## Normalization
-- [ ] Batch Normalization  
-- [ ] Layer Normalization
+## [Normalization](normalization)

architectures/README.md

@@ -0,0 +1,7 @@
+# Architectures  
+- [ ] LeNet
+- [ ] AlexNet  
+- [ ] VGG-19  
+- [ ] ResNet-18
+- [ ] ResNet-34  
+- [ ] ResNet-50 

losses/README.md

@@ -0,0 +1,13 @@
+# Losses  
+- [x] Mean Squared Error Loss/ L2 Loss  
+- [x] Mean Absolute Error/ L1 Loss  
+- [ ] Cross Entropy Loss  
+- [ ] Hinge Loss  
+- [ ] Kullback-Leibler Loss  
+- [ ] Softmax  
+- [ ] AM-Softmax  
+- [ ] AAM-Softmax  
+- [ ] Prototypical Loss  
+- [ ] Angular Prototypical Loss  
+- [ ] Si-SDR  
+- [ ] Si-SNR

non-linearities/README.md

@@ -0,0 +1,8 @@
+# Non-Linearities  
+- [x] Sigmoid  
+- [x] Tanh  
+- [x] ReLU  
+- [x] LeakyReLU
+- [ ] ELU
+- [ ] pReLU
+- [ ] GELU

normalization/README.md

@@ -0,0 +1,4 @@
+# Normalization
+- [ ] Batch Normalization  
+- [ ] Layer Normalization
+- [ ] Group Normalization

optimizers/README.md

@@ -0,0 +1,5 @@
+# Optimizers  
+- [ ] Stochastic Gradient Descent
+- [ ] Adam
+- [ ] AdaGrad
+- [ ] RMSprop