For a while, I wanted to build a complete autograd engine. What is an autograd engine, you might ask? To find the answer, we first must know what a neural network is. Neural Network Crash Course: A neural network can just be seen as a black-box function. …

Lexical Analysis and ASTs: Recently I was going through Thorsten Ball’s “Writing An Interpreter in Go”. In this book, you create a basic interpreted language and write a lexer, parser, evaluator, and REPL for it. A Lexer takes in source code and turns it …

I found that my life has largely been shaped by the books I’ve read (especially when I was younger). I’ve aggregated them all here. Below is a list of all the books I’ve read since I was 13. The ones in italics are ones that stood out to me.

Here’s a list of quotes I’ve collected from over the years. General: “I believe that a man should strive for only one thing in life, and that is to have a touch of greatness” — Félix Martí-Ibáñez “I wish to preach, not the doctrine of ignoble ease, but the …

Theses are some of my over Qiang Liu’s course, Machine Learning II. Gradient Descent: Gradient Descent is a fundamental, first-order iterative optimization algorithm designed for minimizing a function. The primary objective of Gradient Descent is to find …

These are a few of my notes from Eunsol Choi’s NLP class at UT Austin. Word Embeddings: Word embeddings are a type of word representation that captures the semantic meaning of words in a continuous vector space. Unlike one-hot encoding, where each word is …

These are a few of my notes from Eunsol Choi’s NLP class at UT Austin. Recurrent Neural Networks (RNNs): Recurrent Neural Networks (RNNs) are a class of artificial neural networks specifically designed to tackle sequence-based problems. Unlike traditional …

These are a few of my notes from Eunsol Choi’s NLP class at UT Austin. Generative Models vs. Discriminative Models: When it comes to classification, models are broadly categorized into Generative Models and Discriminative Models. Generative Models: In …

My notes over Mark Maxwell’s course, Probability I, and his textbook, Probability & Statistics with Applications, Second Edition. Combinatorial Probability: The fundamental theorem of counting is also known as the multiplication principle. Given that …

These are my notes over my review of Linear Algebra, going through Gilbert Strang’s Introduction To Linear Algebra. Introduction to Vectors: The core of linear algebra is vector addition and scalar multiplication. Combining these two operations gives us a …