Update README.md

README.md

@@ -1900,6 +1900,37 @@ Follow these steps:
 
 # Chapter 5 Optimization
 
+```mermaid
+flowchart LR
+    Inferencing-->MIGraphX-Introduction
+    MIGraphX-Introduction-->MIGraphX-Installation
+    MIGraphX-Installation--> Install-Binaries
+    MIGraphX-Installation--> Building-from-source
+    MIGraphX-Installation--> Docker
+    Install-Binaries --> MIGraphX-Example
+    Building-from-source --> MIGraphX-Example
+    Docker --> MIGraphX-Example
+    MIGraphX-Example--> MIGraphX-Python-API
+    MIGraphX-Example--> MIGraphX-C++-API
+    MIGraphX-Introduction--> Tuning-GraphX
+    Tuning-GraphX --> Tuned
+    Tuning-GraphX --> Untunned
+    
+    
+    click Inferencing href "https://github.com/anubhavamd/Deep-Learning-Updated#51-inferencing"
+    click MIGraphX-Introduction href "https://github.com/anubhavamd/Deep-Learning-Updated#511-migraphx-introduction"
+    click MIGraphX-Installation href "https://github.com/anubhavamd/Deep-Learning-Updated#512-migraphx-installation"
+    click Install-Binaries href "https://github.com/anubhavamd/Deep-Learning-Updated#5121-option-1-installing-binaries"
+    click Building-from-source href "https://github.com/anubhavamd/Deep-Learning-Updated#5122-option-2-building-from-source"
+    click Docker href "https://github.com/anubhavamd/Deep-Learning-Updated#5123-option-3-use-docker"
+    click MIGraphX-Example href "https://github.com/anubhavamd/Deep-Learning-Updated#513-migraphx-example"
+    click MIGraphX-Python-API href "https://github.com/anubhavamd/Deep-Learning-Updated#5131-migraphx-python-api"
+    click MIGraphX-C++-API href "https://github.com/anubhavamd/Deep-Learning-Updated#5132-migraphx-c-api"
+    click Tuning-GraphX href "https://github.com/anubhavamd/Deep-Learning-Updated#514-tuning-migraphx"
+    click Tuned href "https://github.com/anubhavamd/Deep-Learning-Updated#tuned"
+    click Untunned href "https://github.com/anubhavamd/Deep-Learning-Updated#untuned"
+```
+
 ## 5.1 Inferencing
 
 Inference is where capabilities learned during Deep Learning training are put to work. It refers to the use of a fully trained neural network to make conclusions (predictions) on unseen data that the model has not interacted ever before. Deep Learning inferencing is achieved by feeding new data, such as new images, to the network, giving the Deep Neural Network a chance to classify the image. Taking our previous example of MNIST, the DNN can be fed new images of hand- written digit images allowing the neural network to classify digits. A fully trained DNN should make accurate predictions as to what an image represents, and inference cannot happen without training.