ZhiZhi Gewu

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ZhiZhi Gewu

The cost of building is the lowest it has ever been

The cost of building is the lowest it has ever been

AI tools have opened up a world of possibilities, enabling us to accomplish tasks more efficiently and creatively. Here are some of the exciting things I've tried recently with AI: 1. Create a Webpage with Just a Chatbox Using tools like Cursor, you can: * Build a webpage by

Bayes’ Rule Demystified: Five Intuitive Perspectives

Bayes’ Rule Demystified: Five Intuitive Perspectives

Example Consider the following scenario: * The probability that a person has a disease is 0.01. * The probability that a person with the disease tests positive is 0.90. * The probability that a person without the disease tests positive is 0.10. Question: What is the probability that a person

Beyond the Numbers: A Practitioner's Guide to Meaningful Data Analysis

Beyond the Numbers: A Practitioner's Guide to Meaningful Data Analysis

This article presents my personal perspective on approaching data analysis. While there are many methodologies and best practices in the field, the following framework represents my thoughts on providing a structured approach to guide your analytical process. These views are based on my experience and should be taken as one

Why Supervised Learning Works?

Why Supervised Learning Works?

In essence, "Low Training Error + Sufficient Data Relative to Model Complexity → Low Test Error". Supervised learning must work on the SAME distribution. Key Concepts * Training Error (\(\text{Train}_S(f)\)): The error of a model \(f\) evaluated on the training dataset \(S\). * Test Error (\(\text{Test}_D(f)

Matrices Explained

Matrices Explained

Linear Transformations and Matrices Linear Transformations A transformation \(T\) is linear if it satisfies the following two properties: 1. Additivity: For all vectors \(x, y\) in the domain of \(T\), we have \(T(x + y) = T(x) + T(y)\). 2. Homogeneity: For all vectors \(x\) in the domain of \(T\

[Study Note] V and Q Function in RF

[Study Note] V and Q Function in RF

UC Berkeley's CS285 course: lecture 4. The goal of reinforcement learning is to find an optimal policy \(\pi^*\) that maximises the cumulative rewards over time. Define the trajectory \(\tau = (s_0,a_0,s_1,a_1,...) \), which is a sequence of states and actions. The probability under

One Month Later: Getting Back on Track with Updates

One Month Later: Getting Back on Track with Updates

Dear readers, It's been nearly a month since my last post, and I wanted to update you on my recent activities. While my day job has kept me quite busy, I've still managed to explore some fascinating topics in my spare time. I'm pleased