minimal diffusion model for self-study
-
Updated
Jul 8, 2023 - Python
minimal diffusion model for self-study
Coursework for self-studying UC Berkeley's CS61A in Spring 2018 (WIP).
Python implementation of RL algorithms presented in Richard Sutton and Andrew Barto's book Reinforcement Learning: An Introduction (second edtion)
AI-powered study assistant for personalized learning, exam revision, and concept mastery. Streamlit web app integrating Gemini 2.5 Flash API for instant explanations, PDF summarization, smart quiz generation, and automated answer evaluation—built for students and educators.
Python code for chapter 2 in mml-book
I push my practice codes and experimental codes as I go through my learning process
Coding exercises in the book "Think Python: How to Think Like a Computer Scientist" written by Allen Downey.
Some Python mini projects I create when self-study Python.
Courses and learning materials followed for work and personal growth, focused on Computer Science and languages.
Open source code for my FaizaLingo (Japanese) Windows app (.exe)
PyGamgee runs DeepSeek LLM with Ollama, using PyMuPDF for PDF extraction and FAISS for fast vector search. With LangChain RAG and conversation memory, it enables efficient, private document understanding—fully offline.
The 3rd project of cs61c fall 2019. It's a self-study project for me
Python program to detect red and blue colors from the webcam. OpenCV and Numpy is used..
Unsupervised Feature Learning / Deep Learning Tutorial
Create a Numpy function outputing the mean, variance, standard deviation, max, min, and sum of the rows, columns, and elements in a 3 x 3 matrix.
This is my personal "notebook" of my learning path on the Platzi educational website.
Add a description, image, and links to the self-study topic page so that developers can more easily learn about it.
To associate your repository with the self-study topic, visit your repo's landing page and select "manage topics."