Journey of Curiosity Arshad Kazi
Technical Blogs 6 essays · 4 notebooks

Technical Blogs

What I have learned about computer vision, the brain and machine learning, written down while I was learning it.

Essays
6
Notebooks
4
Days of notes
25
Hours of reading
2.4
Why I write these

Transfer learning works in brains too. The prefrontal cortex builds schema, mental frameworks that compress patterns from one domain and apply them to another. The more domains you feed it, the richer the schema. Convolutions in V1 and convolutions in CNNs are the same idea discovered independently. Attention in the parietal cortex and attention in transformers solve the same resource allocation problem. These are not metaphors. They are convergent solutions.

Leonardo studied anatomy to paint better and painted to understand anatomy better. Feynman learned to pick locks to understand information theory. Ramanujan found number theory in temple floor patterns. Cross-domain learning is not a distraction from depth. It is how depth actually works.

The time box is the point. Retrieval under pressure consolidates memory faster than passive review, and the hippocampus encodes better when a session has a clear boundary. You cannot passively read for sixty minutes and pretend you understood it. You have to write what you know, find the gaps, and come back the next day to fill them.

I am a Leonardo fanboy, not because I think I am smart like him because I am really not, but because the man had no formal education past age 14 and still taught himself anatomy and optics and geology and engineering from whatever books and mentors he could get his hands on, and that is the only way I have ever been able to learn anything either. He kept notebooks his entire life, thousands of pages, anatomy next to fluid dynamics next to machine designs, not organized by subject but by date, and he called himself "discepolo della esperienza" which means disciple of experience, and I think the notebook was never documentation for him, it was the actual thinking tool, the place where looking at something carefully enough turns into understanding it.

Essay
A single post.
Notebook
Daily notes on one topic, over a week or so.

Topics

20266 pieces

  1. Essay 15 min read

    The Visual Cortex as a Computer Vision Architecture: Dorsal and Ventral Streams

    How the brain's visual cortex uses two parallel streams, feedback loops, and relative perception to process vision. Everything rewritten for engineers who build vision systems.

    1. 01The Split You Did Not Know Was in Your Design
    2. 02The Dorsal Stream: Real-Time Spatial Inference
    3. 03The Ventral Stream: Hierarchical Object Recognition
    4. 04Implications for Computer Vision Engineering
  2. Essay 6 min read

    Temporal and Spatial Attention for 3D Pose Estimation

    How temporal and spatial attention mechanisms enable accurate 2D-to-3D human pose estimation, with a deep dive into DSTformer.

    1. 01The Ill-Posed Problem: Lifting 2D to 3D
    2. 02Temporal Attention: Understanding Motion Over Time
    3. 03Spatial Attention: Maintaining Anatomical Consistency
    4. 04Fusion of Temporal and Spatial Streams

20243 pieces

  1. Essay 5 min read

    Building a Sign Language Recognition System with CNNs and OpenCV

    A practical walkthrough of building a real-time ASL alphabet recogniser. Dataset, CNN architecture with the actual math, and live webcam inference using OpenCV.

    1. 01Dataset
    2. 02Build and Train the Model
    3. 03OpenCV
  2. Essay 5 min read

    ROC Curves and AUC: Picking the Right Threshold for Your Model

    Why accuracy fails on skewed data, how confusion matrices work, and how to use ROC curves and AUC to evaluate and compare probabilistic classifiers.

    1. 01Why Accuracy Is Not Enough
    2. 02True Positive, True Negative, False Positive, False Negative
    3. 03Confusion Matrix
    4. 04ROC Curve
    5. 05AUC (Area Under the Curve)
  3. Essay 6 min read

    Vector Database Management Systems: Architecture and Internals

    A technical overview of how Vector Database Management Systems work, covering vectorization, indexing, hardware tradeoffs, and the core challenges in building systems that search by meaning rather than exact match.

    1. 01What is a Vector Database?
    2. 02Why Not a Regular Database?
    3. 03General Architecture of a VDBMS
    4. 04Use Cases
    5. 05Challenges and Limitations

20231 pieces

  1. Essay 5 min read

    Probability Mass Functions, Expectation, and Variance

    Relearning probability from a machine learning perspective. What PMFs are, how to compute expectation and variance from first principles, and why any of this matters for ML.

    1. 01What is Probability?
    2. 02Probability Distribution
    3. 03Probability Mass Function
    4. 04Expected Value (Expectation)
    5. 05Variance
    6. 06Why does any of this matter for ML?

Nothing on that topic yet.