Update ToC

Author: mikebo93

chapter_accelerator/Index.md

@@ -14,3 +14,13 @@ This chapter has the following learning objectives:
 
 3.  Understand the typical techniques used to optimize the performance
     of accelerators.
+
+```toc
+:maxdepth: 2
+
+Overview
+Components_of_Hardware_Accelerators
+Programming_Methods
+Performance_Optimization_Methods
+Chapter_Summary
+```

chapter_accelerator/index.md

@@ -0,0 +1,26 @@
+# Hardware Accelerator
+
+In the field of AI frameworks, hardware accelerators play a vital role
+in enabling efficient neural network computations. This chapter delves
+into the design of modern hardware accelerators, their programming
+techniques, and the typical approaches to optimize accelerator
+performance.
+
+This chapter has the following learning objectives:
+
+1.  Understand the architecture of a modern hardware accelerator.
+
+2.  Understand the methods of programming hardware accelerators.
+
+3.  Understand the typical techniques used to optimize the performance
+    of accelerators.
+
+```toc
+:maxdepth: 2
+
+Overview
+Components_of_Hardware_Accelerators
+Programming_Methods
+Performance_Optimization_Methods
+Chapter_Summary
+```

chapter_model_deployment/Index.md

@@ -20,3 +20,17 @@ The key aspects this chapter explores are as follows:
     optimization.
 
 4.  Common methods for model security protection.
+
+```toc
+:maxdepth: 2
+
+Overview
+Conversion_to_Inference_Model_and_Model_Optimization
+Model_Compression
+Advanced_Efficient_Techniques
+Model_Inference
+Security_Protection_of_Models
+Chapter_Summary
+Further_Reading
+```
+

chapter_model_deployment/index.md

@@ -0,0 +1,36 @@
+# Model Deployment {#ch:deploy}
+
+In earlier chapters, we discussed the basic components of the machine
+learning model training system. In this chapter, we look at the basics
+of model deployment, a process whereby a trained model is deployed in a
+runtime environment for inference. We explore the conversion from a
+training model into an inference model, model compression methods that
+adapt to hardware restrictions, model inference and performance
+optimization, and model security protection.
+
+The key aspects this chapter explores are as follows:
+
+1.  Conversion and optimization from a training model to an inference
+    model.
+
+2.  Common methods for model compression: quantization, sparsification,
+    and knowledge distillation.
+
+3.  Model inference process and common methods for performance
+    optimization.
+
+4.  Common methods for model security protection.
+
+```toc
+:maxdepth: 2
+
+Overview
+Conversion_to_Inference_Model_and_Model_Optimization
+Model_Compression
+Advanced_Efficient_Techniques
+Model_Inference
+Security_Protection_of_Models
+Chapter_Summary
+Further_Reading
+```
+