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University Notes (Feb 2026 - Jun 2026)
1
Parallel Programming Section
Parallel Programming (CSC304)
2
Chapter 1: Why Parallel Computing?
3
Chapter 2: Parallel Hardware and Parallel Software
4
Chapter 3: Distributed Memory Programming with MPI
Simulation & Modeling (CSC305)
5
Simulation & Modeling: Introduction
6
Single-Channel Queue Simulation
7
Simulation & Modeling Midterm 2025
Human Computer Interaction (SEN303)
8
Notes for Studying HCI
9
HCI Midterm 2025
10
Midterm Cheatsheet
11
Introduction to Human-Computer Interaction (Part 1)
12
Introduction to Human-Computer Interaction (Part 2)
13
Understanding and Conceptualizing Interaction (Part 1)
Software Requirements Engineering (SEN302)
14
Lecture 1: Introduction
15
Lecture 2: Introduction
16
Lecture 3: Software Requirements Specification
17
Lecture 4: Elicitation
Software Verification and Validation (SEN 304)
18
Lecture 1: Importance of Software Testing
19
Lecture 2: Software Testing Types and Techniques
20
Lecture 3: SDLC
21
Lecture 4: Test Planning
Advanced Database
22
Ebook Excercises Solutions
23
Final Exam Answers
Internships & Opportunities
24
Google Summer of Code 2026
25
Hong Kong University Internship
Papers
26
From Learning Models of Natural Image Patches to Whole Image Restoration
27
Natural Images, Gaussian Mixtures, and Dead Leaves
28
Deep Image Prior
29
Variational Inference with Normalizing Flows
30
Glow: Generative Flows with Invertible 1x1 Convolutions
31
Invertible Residual Networks
32
Modeling High-Dimensional Discrete Data with Multi-Layer Neural Networks
33
Pixel Recurrent Neural Networks
34
Improved Variational Inference with Inverse Autoregressive Flow
35
Language Model Beats Diffusion – Tokenizer is Key to Visual Generation
36
Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction
37
An Image is Worth 32 Tokens for Reconstruction and Generation
38
Autoregressive Image Generation without Vector Quantization
39
Transfusion: Predict the Next Token and Diffuse Images with One Multi-Modal Model
Stanford Deep Generative Models Course
40
Introduction
41
Background
42
Autoregressive Models
43
Maximum Likelihood Learning
44
Variational Autoencoders (VAE)
Stanford Machine Learning with Graphs
45
Graph Neural Networks
IELTS
46
General Notes and Tips
47
Liz IELTS Writing Task 1
48
Common Mistakes
References
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My Notes
Author
Ibrahim Habib
Notes
Welcome to my notes website!
1
Parallel Programming Section