Predicting Song Popularity Using Neural Networks with Backpropagation Algorithm Based on Audio Features
This repository delves into the role of activation functions in perceptron-based classification models. It features a comprehensive Jupyter notebook demonstrating different activation functions, their mathematical foundations, and their impact on model performance.
Deep Learning model for predicting success after donation coded in Google Colab
I have implemented some AI projects from scratch implementation without explicit use of the built-in-libraries and thus added to this repo.
Time series forecast using RNN and LSTM
Comparison of common activation functions on MNIST dataset using PyTorch.
Developed Neural Network (NN) having one hidden layer, two hidden layers and four hidden layers, besides the input and output layers. Tested with Sigmoid, tanh and ReLu activation function. Used Scikit learn for pre-processing data.
Revising concepts of CNN by building them from scratch using NumPy.
Advance Machine Learning (CSL 712) Course Lab Assignments
2nd Project of Course 'Machine Learning' of the SMARTNET programme. Taken at the National and Kapodistrian University of Athens.
Handwritten digit recognition using feedforward neural networks on MNIST — 14 model experiments comparing batch size, activation functions (ReLU/Tanh/Sigmoid), hidden layer depth, and overfitting analysis with TensorFlow/Keras
Exploration of teamwork in neural networks
Feed Forward Neural Network to classify the FB post likes in classes of low likes or moderate likes or high likes, back propagtion is implemented with decay learning rate method
quick 'n' dirty neural network (for practical use)
An activation function in the context of neural networks is a mathematical function applied to the output of a neuron. The purpose of an activation function is to introduce non-linearity into the model, allowing the network to learn and represent complex patterns in the data.
Simple self-written ANN powered by NumPy to classify handwritten digits of the famous MNIST Dataset. ✍️
Neural Network implementation from scratch along with its analysis with different type of activation function and with variation in hidden layer size and depth.
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