diff --git a/README.md b/README.md
index 851efec..ca8da55 100644
--- a/README.md
+++ b/README.md
@@ -5,24 +5,28 @@ different types of fruits. This library forms the core part of the larger Fruit
 
 ## Features:
 
-- Customizable Neural Network: Create neural networks with varying structures, including different numbers of layers,
+- *Customizable Neural Network*: Create neural networks with varying structures, including different numbers of layers,
   neurons per layer, and activation functions.
-- Multiple Activation Functions: Includes several built-in activation functions like step, sign, linear, ReLU, sigmoid,
+- *Multiple Activation Functions*: Includes several built-in activation functions like step, sign, linear, ReLU,
+  sigmoid,
   and tanh, each with its corresponding derivative for backpropagation.
-- Efficient Computation: Optimized for performance, utilizes WebAssembly to allow fast c++ near-native speed.
-- Gradient Descent Training: Utilizes gradient descent algorithm for network training.
+- *Efficient Computation*: Optimized for performance, utilizes WebAssembly to allow fast c++ near-native speed.
+- *Gradient Descent Training*: Utilizes gradient descent algorithm for network training.
 
 ## Components
 
-- **```Neuron```**: Represents a single neuron, encapsulating weights and bias.
+- **[```nn```](nn/nn.h)**: Main namespace containing all components.
+  Check [this](nn/nn.h) header for documentation.
+
+- **[```Neuron```](nn/neuron.h)**: Represents a single neuron, encapsulating weights and bias.
   Provides functionality for computing neuron's output.
 
 - **Layer Types**:
-    - **```Layer```**: Base class for layers in the neural network.
-    - **```HiddenLayer```**: Represents a hidden layer in the network.
-    - **```OutputLayer```**: Special layer type using Softmax activation.
+    - **[```Layer```](nn/layer.h)**: Base class for layers in the neural network.
+    - **[```HiddenLayer```](nn/hidden_layer.h)**: Represents a hidden layer in the network.
+    - **[```OutputLayer```](nn/output_layer.h)**: Special layer type using Softmax activation.
 
-- **```Network```**:Represents the entire neural network, a collection of layers.
+- **[```Network```](nn/network.h)**:Represents the entire neural network, a collection of layers.
   Implements forward and backward propagation methods for network training.
 
 - **Activation Functions**: Defined in the ```act``` namespace with built-in functions for use in network layers.
@@ -37,8 +41,8 @@ different types of fruits. This library forms the core part of the larger Fruit
 - To create a neural network:
     - Define Network Structure: Determine the number of layers and neurons in each layer.
     - Select Activation Functions: Choose activation functions for each hidden layer.
-    - Create Network: Use the ```make::network``` function to create a network instance.
-    - Train the Network: Provide training data and use ```Network::train``` method to train the network.
+    - Create Network: Use the ```nn::make::network``` function to create a network instance.
+    - Train the Network: Provide training data and use ```nn::Network::train``` method to train the network.
 
 - Example
 
@@ -47,16 +51,18 @@ different types of fruits. This library forms the core part of the larger Fruit
 
 int main() {
     // Create a neural network with specific dimensions and activation functions
-    // The following network has 3 inputs and 2 hidden layers with [6, 4] neurons respectively
-    // and output layer with 2 neurons. And uses sigmoid activation function.
-    auto network = nn::make::network({3, 6, 4, 2}, {nn::act::relu, nn::act::sigmoid}, 0.01);
+    // The following network has 3 inputs and 2 hidden layers with [6, 4] neurons respectively,
+    // and output layer with 2 neurons. With initial learning rate 0.01.
+    // First hidden layer uses ReLU activation function, second one uses Sigmoid.
+    nn::Network network = nn::make::network({3, 6, 4, 2}, {nn::act::relu, nn::act::sigmoid}, 0.01);
 
     // Train the network with your data...
     // For input {1, 2, 3} we expect an output {1, 0}
     network.train({1, 2, 3}, {1, 0});
-    
+
     // Now use it to predict outputs...
-    auto output = network.predict({2, 3, 1});
+    // vd_t is a vector of double values.
+    nn::vd_t output = network.predict({2, 3, 1});
     
     return 0;
 }