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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
Internships & Opportunities
22
Google Summer of Code 2026
23
Hong Kong University Internship
Papers
24
From Learning Models of Natural Image Patches to Whole Image Restoration
25
Natural Images, Gaussian Mixtures, and Dead Leaves
26
Deep Image Prior
27
Variational Inference with Normalizing Flows
28
Glow: Generative Flows with Invertible 1x1 Convolutions
29
Invertible Residual Networks
30
Modeling High-Dimensional Discrete Data with Multi-Layer Neural Networks
31
Pixel Recurrent Neural Networks
32
Improved Variational Inference with Inverse Autoregressive Flow
33
Language Model Beats Diffusion – Tokenizer is Key to Visual Generation
34
Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction
35
An Image is Worth 32 Tokens for Reconstruction and Generation
36
Autoregressive Image Generation without Vector Quantization
37
Transfusion: Predict the Next Token and Diffuse Images with One Multi-Modal Model
Stanford Deep Generative Models Course
38
Introduction
39
Background
40
Autoregressive Models
41
Maximum Likelihood Learning
42
Variational Autoencoders (VAE)
Stanford Machine Learning with Graphs
43
Graph Neural Networks
IELTS
44
General Notes and Tips
45
Liz IELTS Writing Task 1
46
Common Mistakes
References
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My Notes
Author
Ibrahim Habib
Notes
Welcome to my notes website!
1
Parallel Programming Section