Predicting Win/Loss/Draw on the Connect Four dataset with Tsetlin Machines
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Updated
Dec 20, 2019 - Python
Predicting Win/Loss/Draw on the Connect Four dataset with Tsetlin Machines
An investigation into the effects that image augmentation has on the accuracy and loss in Convolutional Neural Networks
A python package that'll help you train DCGAN models with your own image based data.
One of the most basic GAN setup. Training on MNIST dataset for generating handwritten digits, on Tensorflow.
This code trains a CNN in Keras to classify cell images (infected/uninfected). It sets up data generators, defines model architecture with convolutional layers, applies regularization, configures callbacks, and trains the model for binary classification.
2nd Project of Course 'Machine Learning' of the SMARTNET programme. Taken at the National and Kapodistrian University of Athens.
Introducing attention mechanism in convolutional recurrent neural networks
Tensorflow: MNIST Digit Image Recognition
Udacity Self-Driving Car Engineer Nanodegree - Term1 Project 3 (Behavior Cloning)
Recognizing link URLs from images and returning site data - for a more physically inclusive web experience. CRNNs with PyTorch
AI High-Performance Solution on FPGA
Master's Thesis project at University of Agder, Spring 2020. Classification with Tsetlin Machine on board game 'GO'.
Neural network for words recognition
Japanese Handwritten Character Recognition using DropBlock Regulzarization
AI High-Performance Solution on FPGA
Deep Learning (PyTorch) Models Deployment using SQL databases
Convolutional autoencoder for encoding/decoding RGB images in TensorFlow with high compression ratio
Visualize feature maps in convolutional neural networks.
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