MainakVerse/Activation-Infopedia
Learn about various types of activation functions with practical working and trials.
This project helps machine learning practitioners understand how different activation functions work within neural networks. It provides detailed explanations and practical demonstrations of various activation functions, allowing users to see their behavior with different inputs and observe their impact on model performance. Machine learning engineers, data scientists, and students building or studying neural networks would find this useful.
No commits in the last 6 months.
Use this if you need to deepen your understanding of activation functions and their practical application in neural network design.
Not ideal if you are looking for a tool to automatically select the best activation function for a specific dataset or problem.
Stars
14
Forks
—
Language
Python
License
—
Category
Last pushed
Feb 21, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/MainakVerse/Activation-Infopedia"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
digantamisra98/Mish
Official Repository for "Mish: A Self Regularized Non-Monotonic Neural Activation Function" [BMVC 2020]
Sentdex/nnfs_book
Sample code from the Neural Networks from Scratch book.
itdxer/neupy
NeuPy is a Tensorflow based python library for prototyping and building neural networks
vzhou842/cnn-from-scratch
A Convolutional Neural Network implemented from scratch (using only numpy) in Python.
nicklashansen/rnn_lstm_from_scratch
How to build RNNs and LSTMs from scratch with NumPy.