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

layers

QReLU and m-QReLU: Two novel quantum activation functions for Deep Learning in TensorFlow, Keras, and PyTorch

A facial emotion/expression recognition model created using CNN with Keras & Tensorflow

Super Resolution's the images by 3x using CNN

Convolutional Neural Network with just Numpy and no other MLLibs

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

Project inspired by a book titled, "Artificial Intelligence," by Copeland. One of the World's first Quantum Neural Networks ever invented.

The objective of this project is to identify the fraudulent transactions happening in E-Commerce industry using deep learning.

Using MNSIT as a training dataset, this model is trained to predict the handwritten digits.

This project creates a machine learning model that predicts the success of investing in a business venture.

Twitter Sentiment Extraction using Custom Roberta Transformer Model and using Pre-trained model weights for prediction