Update ToC

Author: mikebo93

chapter_recommender_system/Index.md

@@ -30,3 +30,16 @@ This chapter has the following learning objectives:
 
 3.  Attain insights into the challenges faced by practical recommender
     systems and discover their corresponding solutions.
+
+```toc
+:maxdepth: 2
+
+Overview
+System_Components
+Recommendation_Pipeline
+Model_Update
+Supporting_Real-time_Machine_Learning
+Chapter_Summary
+Further_Reading
+```
+

chapter_recommender_system/index.md

@@ -0,0 +1,45 @@
+# Recommender System
+
+Recommender systems serve as intelligent agents, offering suggestions
+for items most relevant to a specific user. To do so, they scrutinize
+data items such as user characteristics, item features, and the
+interactions between the two. Over the past few years, powerhouse
+companies like Google, Facebook, and Alibaba have harnessed deep
+learning technologies to enhance the capabilities of recommender models.
+By deploying deep learning methodologies, these systems are endowed with
+the capability to effectively learn from data through gradient-based
+methods. Moreover, these systems are able to exploit large neural
+networks, including transformers and emerging large language models.
+This, in turn, bolsters the system's proficiency in dissecting complex,
+multi-modal data.
+
+In this chapter, we will delve into the foundational elements of deep
+learning recommender systems (DLRSs). We will elucidate key operational
+processes within these systems, primarily focusing on the multi-stage
+generation of recommendations and the updating of model parameters.
+Lastly, we will delve into a real-world recommender system, shedding
+light on strategic approaches used to tackle practical challenges.
+
+This chapter has the following learning objectives:
+
+1.  Understand the architecture of a recommender system and its
+    essential components.
+
+2.  Understand multi-stage recommendation and model update in a
+    recommender system.
+
+3.  Attain insights into the challenges faced by practical recommender
+    systems and discover their corresponding solutions.
+
+```toc
+:maxdepth: 2
+
+Overview
+System_Components
+Recommendation_Pipeline
+Model_Update
+Supporting_Real-time_Machine_Learning
+Chapter_Summary
+Further_Reading
+```
+

chapter_reinforcement_learning/Index.md

@@ -5,3 +5,15 @@ learning that focuses on how autonomous agents can learn to make optimal
 decisions through interactions with their environments. By designating
 rewards or penalties for each action taken, RL provides a framework for
 training intelligent systems to maximize cumulative reward over time.
+
+```toc
+:maxdepth: 2
+
+Overview
+Introduction_to_Reinforcement_Learning
+Single-Node_Reinforcement_Learning_System
+Distributed_Reinforcement_Learning_System
+Multi-agent_Reinforcement_Learning
+Multi-agent_Reinforcement_Learning_System
+Chapter_Summary
+```

chapter_reinforcement_learning/index.md

@@ -0,0 +1,19 @@
+# Reinforcement Learning System
+
+Reinforcement learning (RL) has emerged as a subfield of machine
+learning that focuses on how autonomous agents can learn to make optimal
+decisions through interactions with their environments. By designating
+rewards or penalties for each action taken, RL provides a framework for
+training intelligent systems to maximize cumulative reward over time.
+
+```toc
+:maxdepth: 2
+
+Overview
+Introduction_to_Reinforcement_Learning
+Single-Node_Reinforcement_Learning_System
+Distributed_Reinforcement_Learning_System
+Multi-agent_Reinforcement_Learning
+Multi-agent_Reinforcement_Learning_System
+Chapter_Summary
+```

chapter_robot/Index.md

@@ -9,3 +9,14 @@ chapter are as follows:
 -   Robotic perception system, planning system, and control system
 
 -   General Robot Operating System (ROS)
+
+```toc
+:maxdepth: 2
+
+Overview_of_Robotic_Systems
+Robot_Operating_System
+Case_Study
+Modern_Robot_Learning
+Chapter_Summary
+```
+

chapter_robot/index.md

@@ -0,0 +1,22 @@
+# Robotic System
+
+This chapter introduces robotics --- a major direction of machine
+learning --- and robotic systems. The key aspects explored in this
+chapter are as follows:
+
+-   Basic knowledge of robotic systems
+
+-   Robotic perception system, planning system, and control system
+
+-   General Robot Operating System (ROS)
+
+```toc
+:maxdepth: 2
+
+Overview_of_Robotic_Systems
+Robot_Operating_System
+Case_Study
+Modern_Robot_Learning
+Chapter_Summary
+```
+