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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
5
CUDA Programming
Simulation & Modeling (CSC305)
6
Simulation & Modeling: Introduction
7
Single-Channel Queue Simulation
8
Simulation & Modeling Midterm 2025
Human Computer Interaction (SEN303)
9
Notes for Studying HCI
10
HCI Midterm 2025
11
Midterm Cheatsheet
12
Introduction to Human-Computer Interaction (Part 1)
13
Introduction to Human-Computer Interaction (Part 2)
14
Understanding and Conceptualizing Interaction (Part 1)
Software Requirements Engineering (SEN302)
15
Lecture 1: Introduction
16
Lecture 2: Introduction
17
Lecture 3: Software Requirements Specification
18
Lecture 4: Elicitation
Software Verification and Validation (SEN 304)
19
Lecture 1: Importance of Software Testing
20
Lecture 2: Software Testing Types and Techniques
21
Lecture 3: SDLC
22
Lecture 4: Test Planning
Advanced Database
23
Ebook Excercises Solutions
24
Final Exam Answers
Internships & Opportunities
25
Google Summer of Code 2026
26
Hong Kong University Internship
Papers
27
From Learning Models of Natural Image Patches to Whole Image Restoration
28
Natural Images, Gaussian Mixtures, and Dead Leaves
29
Deep Image Prior
30
Variational Inference with Normalizing Flows
31
Glow: Generative Flows with Invertible 1x1 Convolutions
32
Invertible Residual Networks
33
Modeling High-Dimensional Discrete Data with Multi-Layer Neural Networks
34
Pixel Recurrent Neural Networks
35
Improved Variational Inference with Inverse Autoregressive Flow
36
Language Model Beats Diffusion – Tokenizer is Key to Visual Generation
37
Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction
38
An Image is Worth 32 Tokens for Reconstruction and Generation
39
Autoregressive Image Generation without Vector Quantization
40
Transfusion: Predict the Next Token and Diffuse Images with One Multi-Modal Model
Stanford Deep Generative Models Course
41
Introduction
42
Background
43
Autoregressive Models
44
Maximum Likelihood Learning
45
Variational Autoencoders (VAE)
Stanford Machine Learning with Graphs
46
Graph Neural Networks
IELTS
47
General Notes and Tips
48
Liz IELTS Writing Task 1
49
Common Mistakes
ICLR 2026
50
ICLR 2026 Notes
51
ICLR 2026 Papers
References
Table of contents
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My Notes
Author
Ibrahim Habib
Notes
Welcome to my notes website!
1
Parallel Programming Section