This is a place to document my learning journey in Machine Learning!
I’ll be covering topics including:
1. Classical Machine Learning
2. Modern Deep Learning (CNN, GraphNN, Transformers)
3. Reinforcement Learning
4. Generative Models (GANs, Diffusion models)
5. Foundation Models (LLMs, VLMs, VLAs, Multi-modal models)
6. Future of Machine Learning
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Introduction to World Models (Part I): What’s a World Model?
If you have been following the artificial intelligence …
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Overfitting vs. Underfitting: Why It Happens & How to Fix It
Imagine you are studying for a massive robotics exam. I…
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Why “Deep & Narrow” Beats “Shallow & Wide” Neural Networks?
Why Deep Neural Networks? Imagine you are trying to tea…
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Why Self-Driving Cars Don’t Use Pixels Like Humans Do: From MLP to CNN
Have you ever wondered how a self-driving car actually …
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MLSD Notes
How to use this note: Machine Learning Life Cycle: Let&…
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Categories of Machine Learning
我和 ML 的相遇,最初是在新竹家的書桌前,記得應該是大二上結束的寒假,貴人紹齊賢拜分享給我這套最讚的入門影片…
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Nail the Machine Learning System Design Interview
The purpose of this post is to get you ramp up with you…
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The Universal Language: How VLMs Bridge the Gap Between Pixels and Prose
Good morning! It is January 28th, and we have entered t…
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Finding Order in Chaos: The Reverse-Entropy Magic of Diffusion Models
Good morning! It is January 27th, and we are on Day 07….
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The Art of Deception: How GANs Learn Through Competition
Good morning! It is January 26th, and we are moving int…
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The Reward Loop: How Agents Learn to Navigate the World through Reinforcement
Good morning! It is January 25th, and we are shifting g…
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Attention is All You Need: How Transformers Replaced the Sequence with the Relationship
Good morning! It is January 24th, and we’ve reached the…




