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.
20266 pieces
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Overview of Modern Nets
Revisiting transformers, tokenizers, attention, and the GPT family. Intuitive understanding for interviews and curiosity.
- Day 1The encoder block, BPE, and how tokens are made2 min
- Day 2Multi-head attention and why we use dot products2 min
- Day 3Masking, padding, and BERT vs GPT2 min
- Day 4The transformer architecture, drawn from scratch2 min
- Day 5Pretraining, feedforward, residual connections, and layer norm3 min
- Day 6LLaMA, Mistral, and the road to reasoning models2 min
- Day 7RAG, LangChain, LangGraph, and the LLM tooling landscape5 min
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Understanding Emotion
What are emotions, really? The neuroscience of feeling, and why an autistic brain might process them differently.
- Day 1What even is an emotion?3 min
- Day 2Are guilt and love just layered explanations?3 min
- Day 3Why did evolution bother building emotions?5 min
- Day 4Guilt, cheating, and why monogamy won10 min
- Day 5What is happiness and where does emotional pressure go?7 min
- Day 6Beyond language: the other tools that shape how we feel6 min
- Day 7How emotions control what you think, who you trust, and what you remember11 min
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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.
- 01The Split You Did Not Know Was in Your Design
- 02The Dorsal Stream: Real-Time Spatial Inference
- 03The Ventral Stream: Hierarchical Object Recognition
- 04Implications for Computer Vision Engineering
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Search Images with Words
From neuroscience to CLIP to building a working on-device search prototype. How do VLMs bind words to pixels?
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Understanding the Visual Cortex
How does the brain process what the eye sees? Tracing the path from retina through two parallel streams, the mechanisms at each stage, and the computations behind perception.
- Day 1How does the brain begin processing vision?2 min
- Day 2What mechanisms power each visual stream?2 min
- Day 3How do neurons detect and assemble edges?2 min
- Day 4How do neurons use context from their neighbours?2 min
- Day 5How does the brain perceive depth?6 min
- Day 6How does seeing become knowing?5 min
- Day 7The complete architecture — how vision flows through the brain9 min
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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.
- 01The Ill-Posed Problem: Lifting 2D to 3D
- 02Temporal Attention: Understanding Motion Over Time
- 03Spatial Attention: Maintaining Anatomical Consistency
- 04Fusion of Temporal and Spatial Streams
20243 pieces
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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.
- 01Dataset
- 02Build and Train the Model
- 03OpenCV
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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.
- 01Why Accuracy Is Not Enough
- 02True Positive, True Negative, False Positive, False Negative
- 03Confusion Matrix
- 04ROC Curve
- 05AUC (Area Under the Curve)
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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.
- 01What is a Vector Database?
- 02Why Not a Regular Database?
- 03General Architecture of a VDBMS
- 04Use Cases
- 05Challenges and Limitations
20231 pieces
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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.
- 01What is Probability?
- 02Probability Distribution
- 03Probability Mass Function
- 04Expected Value (Expectation)
- 05Variance
- 06Why does any of this matter for ML?
Nothing on that topic yet.