Feed-Forward Artificial Neural Network entirely in C. Utilising backpropagation to train. Including an example implementation of the XOR function.
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Updated
Sep 11, 2018 - C
Feed-Forward Artificial Neural Network entirely in C. Utilising backpropagation to train. Including an example implementation of the XOR function.
Evolutionary Computation Framework in Java
PyTorch Tutorial
CSC3022H: Machine Learning Lab 5: Artificial Neural Networks II
Model neurons as elixir processes in feed forward network
a vectorized, python-based implementation of deep feed forward neural network for binary classification.
Using a deep neural network to predict the outcome of a statistic-based combat system
A Repo for Neural Network Assignment
A Neural Network Implementation with Feed Forward and Back-Propagation to be trained to draw a Curve.
Infer cell-cell communications based on feed-forward neural network
This project includes 4 models of sentiment classifiers using feed-forward neural networs for The Twitter sentiment analysis dataset and comparison of them.
Python implementation that explores how different parameters impact a single hidden layer of a feed-forward neural network using gradient descent
Python project based on PyTorch to build a feed-forward Neural Network that identifies between ball and a strike given baseball data.
Deep Learning image recognition with TensorFlow Keras. Created a Feed-Forward-Network from scratch and used pretrained networks MobileNetV2, ResNet50, and VGG16.
Official project website for the CVPR 2020 paper (Oral Presentation) "Cascaded deep monocular 3D human pose estimation wth evolutionary training data"
Sentiment Classifier using: Softmax-Regression, Feed-Forward Neural Network, Bidirectional stacked LSTM/GRU Recursive Neural Network, fine-tuning on BERT pre-trained model. Question Answering using BERT pre-trained model and fine-tuning it on various datasets (SQuAD, TriviaQA, NewsQ, Natural Questions, QuAC)
A Feed-forward Neural Network model in JAVA for Intelligent Character Recognition - trained and tested on the MNIST dataset. Classification accuracy up to 98.22%.
Machine learning algorithms for the calibration of epidemiological compartmental models: application to the Italian COVID-19 outbreaks in Italy and to the newly developed SUIHTER model.
Implementation of a neural network in python which can predict handwritten numbers from the MNIST dataset.
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