Building Convolutional Neural Networks From Scratch using NumPy
Implementing Neural Networks for Computer Vision in autonomous vehicles and robotics for classification, pattern recognition, control. Using Python, numpy, tensorflow. From basics to complex project
Sentiment analysis for Twitter's tweet (in Indonesia language) was built with 3 models to get a comparison in determining which model gives the best results for predicting a tweet to have a positive or negative meaning.
Simple MATLAB toolbox for deep learning network: Version 1.0.3
QReLU and m-QReLU: Two novel quantum activation functions for Deep Learning in TensorFlow, Keras, and PyTorch
layers
A facial emotion/expression recognition model created using CNN with Keras & Tensorflow
Convolutional Neural Network with just Numpy and no other MLLibs
Super Resolution's the images by 3x using CNN
Library which can be used to build feed forward NN, Convolutional Nets, Linear Regression, and Logistic Regression Models.
Neural Network to predict which wearable is shown from the Fashion MNIST dataset using a single hidden layer
Corruption Robust Image Classification with a new Activation Function. Our proposed Activation Function is inspired by the Human Visual System and a classic signal processing fix for data corruption.
A classifier to differentiate between Cat and Non-Cat Images
A small walk-through to show why ReLU is non linear!
Neural Network from scratch without any machine learning libraries
The objective of this project is to identify the fraudulent transactions happening in E-Commerce industry using deep learning.
Evaluation of multiple graph neural network models—GCN, GAT, GraphSAGE, MPNN and DGI—for node classification on graph-structured data. Preprocessing includes feature normalization and adjacency-matrix regularization, and an ensemble of model predictions boosts performance. The best ensemble achieves 83.47% test accuracy.
Text Generation
Using MNSIT as a training dataset, this model is trained to predict the handwritten digits.
Lightweight neural network library written in ANSI-C supporting prediction and backpropagation for Convolutional- and Fully Connected neural networks
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