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<< Neural Network & Generative Modelling >>

Week 7

CCAI 9012

GenAI Solutions to Global Challenges: �Using AI Creatively and Responsibly

Kam-Ming Mark Tam

Class Coordinator

Impressionist-style scene of human–AI co-existence

Generated with ChatGPT (OpenAI), 2026.

Kanxuan HE

Naixiang GAO

Yifan XIE

Chao TANG

May Loaay Mohamed EL-HADIDI

Joseph Jing Hymn WONG

Christina Ka Man CHU

Charles Wai Lam TO

Shum Nga MAN

BT 1 (ARCH 2056/7330) Building Technology 1 : Building Principles

25/03/2026

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General Matters

All About Neural Networks

Basics

Extension

[Break]

Generative Modelling

Traditional

LLMs

[Break]

Course Project

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2026.03.04

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General Matters

All About Neural Networks

Basics

Extension

[Break]

Generative Modelling

Traditional

LLMs

[Break]

Course Project

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Review of Timeline

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Milestones & Corresponding Items

A.1.1: Responsibility and AI

A.1.2: Mechanics of AI

A.1.3: Datasets in AI

2026.02.26

2026.04.01

2026.04.29

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Case Study General Rubric

Four Components to Deliver

  • i. Annotation & Title
    • 500 words

​

  • ii. Key Artefact
    • flexible: can take the form of a document, research poster, website, video, etc

​

  • iv. Evidences (Assignment-Specific)

​

  • iii. Vignette (Short-Form Video)
    • 90-second Long
    • highlight key insights & significance

i

ii

iii

iv

&

ii

iv

i

iii

+ image slides with explanatory context

Column 3

feel free to incorporate content into video to support

storytelling

Column 1

​

Column 3

​

Column 2

​

Column 2

Column 1

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Milestones & Corresponding Items

Team Formation Form

Mid-term Pecha-Kucha Presentation

Crit-Style Peer Review (at mid-term & finals)

Final Presentation Submission

Individual Reflection

Peer Assessment

2026.02.25

2026.03.18

by tutorial sign-up

2026.04.22

2026.05.06

2026.05.06

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A.2 Course Project - Mid-term Pecha-Kucha Presentation

Deadline: 📅 2026.03.18 ⏰ 00:00

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The Hook: A little motivation…

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Imagine you are a LLM… how will you answer this?

1+1=

3

0

1

2

4

…

#

#

5

?

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1+1=

3

0

1

2

4

…

#

#

5

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Imagine you are a LLM… how will you answer this?

2+2=

3

0

1

2

4

…

#

#

5

?

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General Matters

All About Neural Networks

Basics

Extension

[Break]

Generative Modelling

Traditional

LLMs

[Break]

Course Project

CCAI 9012 : GenAI Solutions to Global Challenges: Using AI Creatively and Responsibly

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Linear Regression

y=wx+b

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ℳ

x

y

IN

x

y

OUT

IN

x

y

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Neural Network Motivation: Beyond Linearity

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Neural Network Motivation: Beyond Linearity

+

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Neural Network Motivation: Beyond Linearity

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Neural Network Motivation: Beyond Linearity

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Neural Network Motivation: Beyond Linearity

 

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Neural Networks Introduces Non-linearity

 

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Activation Functions

Neural Networks Introduces Non-linearity

.

.

.

σ(·)

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Neural Networks Introduces Non-linearity

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Universal Approximation Theorem

Neural Networks Introduces Non-linearity

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Single Neuron

Anatomy of a Neural Network

Σ

w

ϕ(·)

×

b

x

y

INPUT

HIDDEN

OUTPUT

LAYERS

neuron

activation

function

bias

weight

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Single Neuron & Multi-Variate Inputs

Anatomy of a Neural Network

INPUT

HIDDEN

OUTPUT

LAYERS

neuron

activation

function

Σ

ϕ(·)

y

w

×

w1

b

x2

x1

w2

xD

×

xD

×

…

D

1

x1

x2

+

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Single Neuron & Multi-Variate Inputs

Anatomy of a Neural Network

INPUT

HIDDEN

OUTPUT

LAYERS

neuron

activation

function

Σ

ϕ(·)

y

w

×

w1

b

xD

x1

wD

···

xD

×

xD

×

…

D

1

x1

xD

…

+

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Neural Network Motivation: Beyond Linearity

 

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Neural Network Motivation: Beyond Linearity

 

2

1

x1

x2

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Neural Network Motivation: Beyond Linearity

 

…

D

1

x1

xD

…

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Neural Network Motivation: Beyond Linearity

 

…

D

1

2

1

x1

xD

…

x1

x2

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Neural Network Motivation: Beyond Linearity

 

…

D

1

…

K

1

x1

xD

…

x1

xk

…

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Artifical neural network

……

INPUT

HIDDEN

OUTPUT

l = 0

l = 1

l = L

l = 2

W(0)

W(1)

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From Single Neuron & Multi-Variate Inputs to Multiple Neurons & Multi-Variate Inputs

Anatomy of a Neural Network

dim_in = 2

dim_out = 1

n_hidden_layer = 1

dim_hidden = 1

dim_in = 2

dim_out = 1

n_hidden_layer = 1

dim_hidden =4

dim_in = 2

dim_out = 1

n_hidden_layer = 2

dim_hidden = 4

dim_in = 2

dim_out = 2

n_hidden_layer = 2

dim_hidden = 4

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What about for classification problems?

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x

category

ℳ

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any value x

larger than 0?

ℳ

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Classification

TRUE

FALSE

INPUT

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Classification

+1

-1

INPUT

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Classification

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Neural Networks Introduces Non-linearity

 

CCAI 9012 : GenAI Solutions to Global Challenges: Using AI Creatively and Responsibly

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Neural Networks Introduces Non-linearity

 

CCAI 9012 : GenAI Solutions to Global Challenges: Using AI Creatively and Responsibly

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Positive

Negative

Positive

Negative

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Positive

Negative

Positive

Negative

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Neural Network Playground

Tensorflow Neural Network Playground

https://playground.tensorflow.org/

INTERACTIVE

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General Matters

All About Neural Networks

Basics

Extension

[Break]

Generative Modelling

Traditional

LLMs

[Break]

Course Project

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Vectors, Matrices & Tensors

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Multiple Data Points

(x1,1,…, x1,D)

(x2,1,…, x2,D)

…

(xN,1,…, xN,D)

…

…

…

D

1

1

N

x1

x2

​

xD

…

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Regular Grids: Pixels & Voxels

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Image as Data Matrices

https://ai.stanford.edu/~syyeung/cvweb/tutorial1.html

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Pixels Math in Image Editing

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Neural Network Paradigms: NN

https://ai.stanford.edu/~syyeung/cvweb/tutorial1.html

Standard NN learns each data entry independently from one another.

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Neural Network Paradigms: CNN

https://ai.stanford.edu/~syyeung/cvweb/tutorial1.html

Convolutional Neural Networks leverages spatial relation of pixel entries and learn over neighbourhoods…

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Source: https://setosa.io/ev/image-kernels/

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Convolution Neural Network

Output/Feature Map Size:

H(l+1) × W(l+1)

Kernel Size

Padding

Stride

Size Hl) × W()

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Analogy to Image Filters

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Image as RGB Channels

Source: https://dev.to/sandeepbalachandran/machine-learning-convolution-with-color-images-2p41

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Convolutional Neural Network

Source: https://www.analyticsvidhya.com/blog/2020/10/what-is-the-convolutional-neural-network-architecture/

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Convolutional Neural Network

Source: https://www.analyticsvidhya.com/blog/2020/10/what-is-the-convolutional-neural-network-architecture/

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Convolutional Neural Network

Low Level Features

Mid-Level Features

High Level Features

Source: https://www.analyticsvidhya.com/blog/2020/10/what-is-the-convolutional-neural-network-architecture/

edges, dark spots

eyes, ears, nose

facial structures

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CNN Explaner

Visualisation of Image Kernels

https://setosa.io/ev/image-kernels/

INTERACTIVE

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CNN Explaner

Visualisation on the Learning of CNN Classifers

https://poloclub.github.io/cnn-explainer/

INTERACTIVE

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Beyond Euclidean Data

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Euclidean Data

Non-Euclidean Data

  • Defined in a regular geometric space
  • Structure defined by coordinates / feature vectors
  • Consistent neighbourhood structure
  • Defined in irregular or relational space
  • Structure defined by relations of elements & their features
  • Neighbourhood structure varies across elements

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Learning directly from reality, which is discrete*

*real-life phenomena are more appropriately described as discrete systems

RELATION

DECISIONS

TEMPORAL

SYSTEMS ACROSS SPATIAL SCALES

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Two Graphs

Motivations for Graph ML

node

data

pattern

pattern

edge

data

node

data

edge

data

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Connectivity Differences

Motivations for Graph ML

node

data

pattern

pattern

edge

data

node

data

edge

data

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Comparison Difficulties

Motivations for Graph ML

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Graph Representation

Adjacency

Features

?

?

=

and

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Graph Representation

0

1

2

Adjacency

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Graph Representation

0

1

2

Adjacency

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Graph Representation

0

1

2

Adjacency

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Graph Representation

0

1

2

Adjacency

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Graph Representation

Adjacency

Features

…

…

…

…

…

…

…

…

…

…

…

…

…

…

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Artifical neural network

……

INPUT

HIDDEN

OUTPUT

l = 0

l = 1

l = L

l = 2

W(0)

W(1)

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Geometric message passing network

W(0)

…

HIDDEN

OUTPUT

l = 0

l = 1

l = L

l = 2

W(1)

INPUT

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Geometric message passing network

…

INPUT

W(0)

HIDDEN

OUTPUT

l = 0

l = 1

l = L

l = 2

W(1)

W(l)

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Geometric message passing network

W(0)

…

HIDDEN

OUTPUT

l = 0

l = 1

l = L

l = 2

W(1)

INPUT

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Graph Explanation

A Gentle Introduction to GNN

https://distill.pub/2021/gnn-intro/

INTERACTIVE

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Attendance Check

Let’s switch things up a bit ;)

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Course Project Support

Course Project Discussions

Thoughtful instructor listening as student teams discuss their projects in a bright university classroom. One team presents near a whiteboard with simple AI diagrams (data → model → output) while other teams watch attentively. Students are engaged, smiling, and collaborative. Natural candid classroom photography.

Generated with ChatGPT (OpenAI), 2026.

ACTIVITY

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General Matters

All About Neural Networks

Basics

Extension

[Break]

Generative Modelling

Traditional

LLMs

[Break]

Course Project

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General Matters

All About Neural Networks

Basics

Extension

[Break]

Generative Modelling

Traditional

LLMs

[Break]

Course Project

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On Distribution

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On Distribution

Thought Experiment: �Let’s generate a typical Hong Konger…

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On Distribution

What are key attributes that describe a person?

e.g. age, gender, income, height, weight….

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On Distribution

Features live in data distributions with different shapes…

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What do we mean when we are creating a generator?

Note: synthetic data generated by ChatGPT

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Data Distribution

Conceptual Illustration of Data Generator

Note: synthetic data generated by ChatGPT

Where in the design space should our generator create its designs?

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Valid Generated Data Point

Conceptual Illustration of Data Generator

Note: synthetic data generated by ChatGPT

✅ 👍 😃

This looks like a plausible generated data point.

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Questionable Generated Data Point

Conceptual Illustration of Data Generator

Note: synthetic data generated by ChatGPT

❌ 👎 🤨

This is clearly outside of the distribution… not plausible.

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Generative Modelling

A Tight Definition

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Structural Design Space Example

Material Minimisation in 2-Bar Sysem

  • Example Adapted from Mueller (2014)

x2

x1

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Simple Structural Design Space Example

Material Minimisation in 2-Bar Sysem

  • Example Adapted from Mueller (2014)

x2

x1

Low

High

Let’s create some design options

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Simple Structural Design Space Example

Material Minimisation in 2-Bar Sysem

  • Example Adapted from Mueller (2014)

Low

High

Structural Material Requirement

Where are the good designs?

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Simple Structural Design Space Example

Material Minimisation in 2-Bar Sysem

  • Example Adapted from Mueller (2014)

x2

x1

Data points of interest tend to live within complexly shaped distributions…

Low

High

Structural Material Requirement

Where in the design space should our generator create its designs?

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Simple Structural Design Space Example

Material Minimisation in 2-Bar Sysem

  • Example Adapted from Mueller (2014)

x2

x1

Data points of interest tend to live within complexly shaped distributions…

+

+

+

+

+

+

+

+

+

+

Low

High

probability

Structural Material Requirement

Where in the design space should our generator create its designs?

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Variational Auto-Encoders

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Dimensionality Reduction by Manifold Learning : Concepts

H = W = 28

​

X ∈ ℝ H × W = 784

0

H

W

x1

x784

…

+

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Variational Auto-Encoder

Dimensionality Reduction

ENC

z

 

xD

…

x1

x1

xD

…

+

+

+

+

+

+

+

+

+

+

+

+

+

+

DEC

 

Decoder

…

…

Encoder

z1

z2

+

+

+

+

+

+

+

+

+

+

+

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Variational Auto-Encoder

Dimensionality Reduction

ENC

 

xD

…

x1

x1

xD

…

+

+

+

+

+

+

+

+

+

+

+

+

+

+

DEC

 

Decoder

…

…

Encoder

z1

z2

+

+

+

+

+

+

+

+

+

+

+

z

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“Serenity Visions: AI-Generated Patterns for a Calming Audio-Visual Experience”

​

Yuheng Karina Xiang

RESEARCH

INITIAL IMAGES

INTERATIVE IMAGES DEVELOPMENT

VIDEO

Design Space: Generative Image-to-Image Video Interpolation

Process: Visual Pattern Generation Pipeline

Design Space: AI-Generated Visual Pattern

Example of Generative Interpolation

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Variational Auto-Encoder

Encoding & Decoding MNIST

…/weekly_scripts/S2_generative_NN/vae.ipynb

VAE�DEC

INTERACTIVE

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VAE Applied to Structural Design

zLoadPath

…

zk

Structural Performance Latent Dimension

Other Latent Dimensions set to 0.0

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VAE Applied to Structural Design

Interactive Design Tool for Performance-Based Design Interpolation

2× playback speed

Structural Performance

Other Latent Dimensions

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Generative Adversarial Network

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z1

z2

?

z1

z2

x1

x2

x1

x2

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z1

z2

?

z1

z2

x1

x2

x1

x2

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z1

z2

?

z1

z2

x1

x2

x1

x2

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z1

z2

?

z1

z2

x1

x2

x1

x2

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General Matters

All About Neural Networks

Basics

Extension

[Break]

Generative Modelling

Traditional

LLMs

[Break]

Course Project

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1+1=

3

0

1

2

4

…

#

#

5

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The capital city of China is

Chongqing

Beijing

Shanghai

Xian

Xianggang

…

Washington, D.C.

Paris

?

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The capital city of China is

Chongqing

Beijing

Shanghai

Xian

Xianggang

…

Washington, D.C.

Paris

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Imagine you are a LLM… how will you answer this?

2+2=

3

0

1

2

4

…

#

#

5

?

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Imagine you are a LLM… how will you answer this?

*see George Orwell’s 1984 or the song by Radiohead inspired by it. 🤓

2+2=

3

0

1

2

4

…

#

#

5

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LLM does not learn truth… �it learns statistical structure from large text corpus…

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What are tokens?

ℳ

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Text Completion Model

床前明月__

光

An Intuitive Example

《静夜思》, 李白 (Li Bai)

ℳ

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Imagine you are a LLM… how will you answer this?

如

光

照

清

在

…

霜

秋

似

床前明月__

《静夜思》, 李白 (Li Bai)

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Text Completion Model

To be or not __

to be…

An Intuitive Example

<<Hamlet>>, William Shakespeare

ℳ

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Switzerland is…

orderly

scenic

beautiful

wealthy

demorcratic

…

poor

dirty

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Generative Modelling : Top-K Sampling

The coolest city of China is

Beijing

Shanghai

Xian

Chongqing

Xianggang*

…

Washington, D.C.

Paris

The coolest city in China is Chongqing, known for its stunning mountainous landscapes, spicy cuisine, and vibrant nightlife scene,…

The coolest city in China is Beijing, known for its rich history, vibrant culture, and bustling street life…

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How do we train LLMs?

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It was the best of times, it was the worst of times…

<<A Tale of Two Cities>>, Charles Dickens

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Text Completion Model

An Intuitive Example

<<A Tale of Two Cities>>, Charles Dickens

It was the best of times, it was the worst of times…

was

the

best

times

worst

…

how

they

It …

ℳ

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Text Completion Model

An Intuitive Example

<<A Tale of Two Cities>>, Charles Dickens

It was the best of times, it was the worst of times…

was

the

best

times

worst

…

how

they

It …

ℳ

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Text Completion Model

An Intuitive Example

<<A Tale of Two Cities>>, Charles Dickens

It was the best of times, it was the worst of times…

was

the

best

times

worst

…

how

they

It

ℳ

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Text Completion Model

An Intuitive Example

<<A Tale of Two Cities>>, Charles Dickens

It was the best of times, it was the worst of times…

was

the

best

times

worst

…

how

they

It

was

ℳ

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Text Completion Model

An Intuitive Example

<<A Tale of Two Cities>>, Charles Dickens

It was the best of times, it was the worst of times…

was

the

best

times

worst

…

how

they

It

the

was

ℳ

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So we continue to infer… one token at a time.

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What about embeddings?

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Artifical Neural Network

……

INPUT

HIDDEN

OUTPUT

l = 0

l = 1

l = L

l = 2

W(0)

W(1)

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Text Completion Model

An Intuitive Example

<<A Tale of Two Cities>>, Charles Dickens

It was the best of times, it was the worst of times…

was

the

best

times

worst

…

how

they

It

the

was

x1

xD

…

x1

xD

…

+

ℳ

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But what was wrong with our silly example?

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Does “It was the” always need to be followed by the “best of times”?

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A language model is not learning a single sentence.�It is learning the statistical structure of language across billions of contexts.

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Context Matters!

​

Words do not follow each other deterministically.�Their probabilities depend on context.

�Give LLMs specificies!

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General Matters

All About Neural Networks

Basics

Extension

[Break]

Generative Modelling

Traditional

LLMs

[Break]

Course Project

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General Matters

All About Neural Networks

Basics

Extension

[Break]

Generative Modelling

Traditional

LLMs

[Break]

Course Project

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Course Project Support

Course Project Discussions

Thoughtful instructor listening as student teams discuss their projects in a bright university classroom. One team presents near a whiteboard with simple AI diagrams (data → model → output) while other teams watch attentively. Students are engaged, smiling, and collaborative. Natural candid classroom photography.

Generated with ChatGPT (OpenAI), 2026.

ACTIVITY

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Course Project Support

Course Project Discussions

  • Paste your problems statements on to: Team Formation Form
  • Talk with your team; quick brainstom re:
    • What problem?
    • For whom?
    • Have you looked for precedents?
    • What small test will you run?
    • Is it achievable within 5 weeks?
    • How will you know if it helped?
  • Let’s chat about it…

ACTIVITY

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Thank You for Your Attention!

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