bonjour-npy/ML02
Assignment 2 of Machine Learning for Computer Science Major, Grade of 2020.
This project helps computer science students visualize the performance of a neural network model for their machine learning coursework. It takes in a dataset and outputs various plots like Accuracy Curves, Loss Curves, and Confusion Matrices, allowing students to understand how different parameters affect model behavior. It is designed for students learning about neural networks and their practical application.
No commits in the last 6 months.
Use this if you are a computer science student needing to analyze and visualize the impact of learning rate and neuron count on a neural network's performance for an assignment.
Not ideal if you are looking for a production-ready machine learning application or a general-purpose library for complex deep learning research.
Stars
8
Forks
1
Language
Jupyter Notebook
License
—
Category
Last pushed
Jan 05, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/bonjour-npy/ML02"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
mathworks/MATLAB-Simulink-Challenge-Project-Hub
This MATLAB and Simulink Challenge Project Hub contains a list of research and design project...
MalayAgr/generative-ai-with-llms-notes
Notes for the course Generative AI With Large Language Models, offered by DeepLearning.AI on Coursera
TheAlgorithms/MATLAB-Octave
This repository contains algorithms written in MATLAB/Octave. Developing algorithms in the...
UBC-CS/cpsc330-2022W1
CPSC 330: Applied Machine Learning
apachecn/ntu-hsuantienlin-ml
:book: 台湾大学林轩田机器学习笔记