A brief introduction Day 2 I was busy and managed only 15 mins for "Supervised Machine Learning" video + 15 mins watched But what is a neural network? | Chapter 1, Deep learning from 3blue1brown. Day 3 I managed 30+ mins on "Supervised Machine Learning", and some time spent on articles reading, like Parul Pandey's Understanding the Mathematics behind Gradient Descent, it's really good. I like math 😂 So this notes are mixed. Notes Implementing gradient descent Notation I was struggling in writting Latex for the formula, then found this table is useful (source is here): Andrew said I don't need to worry about the derivative and calculas at all, I trust him, next I dived into my bookcases and found out my advanced mathmatics books used in college, and spent 15 mins to review, yes, I don't need. Snapped two epic shots of my "Advanced Mathematics" book used in my college time to show off my killer skills in derivative and calculus - pretty sure I've unlocked Math Wizard status! Reading online Okay. Reading some online articles. If we are able to compute the derivative of a function, we know in which direction to proceed to minimize it (for the cost function). From Parul Pandey, Understanding the Mathematics behind Gradient Descent Parul briefly introduced Power rule and Chain rule, fortunately, I still remember them learnt from colleage. I am so proud. After reviewing various explanations of gradient descent, I truly appreciate Andrew's straightforward and precise approach! He was kiddish some times drawing a stick man walking down a hill…
The Beginning As I've been advancing technologies of my AI-powered product knowlege base chatbot which based on Django/LangChain/OpenAI/Chroma/Gradio which is sitting on AI application/framework layer, I also have kept an eye on how to build a pipeline for assessing the accuracy of machine learning models which is a part of AI Devops/infra. But I realized that I have no idea how to meature a model's accuracy. This makes me upset. Then I started looking for answers. My first google search on this is "how to measure llm accuracy", it brought me to Evaluating Large Language Models (LLMs): A Standard Set of Metrics for Accurate Assessment, it's informative. It's not a lengthy article and I read through it. This opens a new world to me. There are standard set of metrics for evaluating LLMs, including: I don't know all of them and where to start! I have to tell meself, "Man, you don't know machine learning..." So my next search was "machine learning course", Andrew Ng's Supervised Machine Learning: Regression and Classification now came on top of the google search results! It's so famous and I knew this before! Then I made a decision, I want to take action now and finish it thoroughly! I immedially enrolled into the course. Now let's start the journey! Day 1 Started Basics 1. What is ML? Defined by Arthur Samuel back in the 1950 😯 "Field of study that gives computers the ability to learn without being explicitly programmed." The above claims gaves the key point (The highlighted part) which could answer the question from…