Project inspired by a book titled, "Artificial Intelligence," by Copeland. One of the World's first Quantum Neural Networks ever invented.
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Updated
Jun 9, 2024 - Jupyter Notebook
Project inspired by a book titled, "Artificial Intelligence," by Copeland. One of the World's first Quantum Neural Networks ever invented.
QReLU and m-QReLU: Two novel quantum activation functions for Deep Learning in TensorFlow, Keras, and PyTorch
Building Convolutional Neural Networks From Scratch using NumPy
Text Generation
layers
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.
Building Convolution Neural Networks from Scratch
A small walk-through to show why ReLU is non linear!
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
This project creates a machine learning model that predicts the success of investing in a business venture.
A facial emotion/expression recognition model created using CNN with Keras & Tensorflow
Twitter Sentiment Extraction using Custom Roberta Transformer Model and using Pre-trained model weights for prediction
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.
Traffic signal identification using Keras LeNet architecture. Identify 43 different classes of images with over 90% accuracy.
Sequential Convolutional Neural Network for handwritten digits recognition trained on MNIST dataset using keras API
Channelwise Partial Convolutions for hardware aware applications
Simple DNN code, adapted from Nielsen
Backward pass of ReLU activation function for a neural network.
Neural Network from scratch without any machine learning libraries
Neural Network to predict which wearable is shown from the Fashion MNIST dataset using a single hidden layer
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