tweak chapter titles

content/ai-system/advanced-apis/index.md

@@ -5,7 +5,7 @@ description = ""
 weight = 12
 
 [extra]
-chapter = "A.2"
+chapter = "Module A.2"
 +++
 
 > **TL;DR:**

content/ai-system/advanced-deployment/index.md

@@ -5,7 +5,7 @@ description = ""
 weight = 32
 
 [extra]
-chapter = "C.10"
+chapter = "Module C.10"
 +++
 
 > **TL;DR:**

content/ai-system/ai-compute-hardware/index.md

@@ -5,7 +5,7 @@ description = ""
 weight = 24
 
 [extra]
-chapter = "B.4"
+chapter = "Module B.4"
 +++
 
 > **TL;DR:**

content/ai-system/api-fundamentals/index.md

@@ -5,7 +5,7 @@ description = ""
 weight = 11
 
 [extra]
-chapter = "A.1"
+chapter = "Module A.1"
 +++
 
 > **TL;DR:**

content/ai-system/cloud-deployment/index.md

@@ -5,7 +5,7 @@ description = ""
 weight = 26
 
 [extra]
-chapter = "B.6"
+chapter = "Module B.6"
 +++
 
 > **TL;DR:**

content/ai-system/edge-self-hosted-deployment/index.md

@@ -5,7 +5,7 @@ description = ""
 weight = 27
 
 [extra]
-chapter = "B.7"
+chapter = "Module B.7"
 +++
 
 > **TL;DR:** Cloud deployment isn't the only option. Learn about edge computing and self-hosted deployment: running AI systems closer to where data is generated or on your own hardware. Discover when these approaches make sense and how to implement them on devices like Raspberry Pi and home servers.

content/ai-system/high-availability/index.md

@@ -5,7 +5,7 @@ description = ""
 weight = 31
 
 [extra]
-chapter = "C.9"
+chapter = "Module C.9"
 +++
 > **TL;DR:**
 > When your service goes down, users leave. Learn how to measure availability with industry-standard metrics (MTBF, MTTR), and implement practical strategies like redundancy and backups to keep your AI API running reliably.

content/ai-system/infrastructure-deployment/index.md

@@ -5,7 +5,7 @@ description = ""
 weight = 20
 
 [extra]
-chapter = "B"
+chapter = "Phase B"
 +++
 
 > **TL;DR:**

content/ai-system/interact-with-ai-systems/index.md

@@ -5,7 +5,7 @@ description = ""
 weight = 10
 
 [extra]
-chapter = "A"
+chapter = "Phase A"
 +++
 > **TL;DR:**
 > Learn why standardized interactions between applications are essential for making AI models practical in real-world scenarios, moving beyond simple function calls to robust communication methods that work across different programming languages and distributed systems.

content/ai-system/mini-project/index.md

@@ -5,7 +5,7 @@ description = ""
 weight = 28
 
 [extra]
-chapter = "B.8"
+chapter = "Module B.8"
 +++
 Leveraging the knowledge from the [phase A and B](@/ai-system/_index.md) of this course (and optionally the advanced techniques from the phase C), we will develop and deploy a multi-functional AI API server in our mini project.
 

content/ai-system/packaging-containerization/index.md

@@ -5,7 +5,7 @@ description = ""
 weight = 25
 
 [extra]
-chapter = "B.5"
+chapter = "Module B.5"
 +++
 
 > **TL;DR:**

content/ai-system/production-ready-systems/index.md

@@ -5,7 +5,7 @@ description = ""
 weight = 30
 
 [extra]
-chapter = "C"
+chapter = "Phase C"
 +++
 Now you can deploy your AI system on different hardware infrastructures with ease, and also enable everyone in the world to access (and hopefully pay for) your AI services. Now you won't run into situations where your friends are calling you to play CS2 but your laptop is running an AI service so you cannot.
 

content/ai-system/wrap-ai-with-api/index.md

@@ -5,7 +5,7 @@ description = ""
 weight = 13
 
 [extra]
-chapter = "A.3"
+chapter = "Module A.3"
 +++
 > **TL;DR:**
 > Build your own APIs for serving AI models, covering everything from basic server setup and AI model integration to authentication, database-backed user management, and rate limiting—transforming you from an API consumer to an API producer.

content/dl4traj/end-to-end/index.md

@@ -2,10 +2,10 @@
 title = "End-to-end Learning of Trajectories"
 date = 2026-02-02
 description = ""
-weight = 2
+weight = 5
 
 [extra]
-chapter = "2"
+chapter = "Chapter 5"
 +++
 
 End-to-end learning means training a model to perform a task from input to output, supervising only on how the output aligns with the task's ground truth.

content/dl4traj/generation/index.md

@@ -2,10 +2,10 @@
 title = "Trajectory Generation"
 date = 2026-02-04
 description = ""
-weight = 4
+weight = 7
 
 [extra]
-chapter = "4"
+chapter = "Chapter 7"
 +++
 
 Trajectory generation, or trajectory synthesis, aims to generate trajectories that are not actually recorded in the available trajectory data, but are still realistic and follow a target distribution.

content/dl4traj/introduction/index.md

@@ -5,7 +5,7 @@ description = ""
 weight = 1
 
 [extra]
-chapter = "1"
+chapter = "Chapter 1"
 +++
 
 A spatiotemporal trajectory is a sequence, with each item being a timestamped location. It records the movement of an object or a human through time and space.

content/dl4traj/self-supervised/index.md

@@ -2,10 +2,10 @@
 title = "Self-supervised Learning of Trajectories"
 date = 2026-02-03
 description = ""
-weight = 3
+weight = 6
 
 [extra]
-chapter = "3"
+chapter = "Chapter 6"
 +++
 
 Self-supervised learning means training a model with unlabeled data, using supervisory signals extracted from the data itself. It usually does not set the model to perform a certain task, but aims to learn task-agnostic information in the data.