Deep learning applications with different datasets.
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Updated
Aug 31, 2019 - Python
Deep learning applications with different datasets.
Partial port of capmangrad to the Rust programming language
Sentimental analysis on IMDB using tflearn Deep Neural Network
Digit recognition fully-connected neural network using the MNIST dataset, with support for batch and stochastic descent.
This repository contains my project for computer vision.
Image classification using FC and CNN networks on CIFAR-10.
Easy to use library to create Neural Networks in C++
Classification of different landcover classes using Hyperspectral data.
Classification of wine quality based on its parameters using fully connected artificial neural network
Simple Python implementation of a fully connected neural network
Python illustration of Neural net from scratch
Yet another basic neural network implementation heavily inspired by, and based on, micrograd
Fundamentals of Artificial Intelligence and Deep Learning Frameworks
This project aims to classify blood cell images from the BloodMNIST dataset using various machine learning models. Implemented classifiers include Logistic Regression, Fully Connected Neural Networks, Convolutional Neural Networks, and MobileNet. The dataset is pre-processed, and models are trained and evaluated to determine their effectiveness.
A fully connected neural net implemented by Numpy
Deep Learning in Computer Vision algorithms. Implementations of relevant codes, some from scratch others re-used and modified.
MINIST Image Digit Recognition by means of a MPL fully connected neural network
experiments with various configurations of CNN networks in order to achieve best results on CIFAR10
A project for my Advanced Artificial Intelligence class to apply AI methods to a real-world problem.
Fully connected deep net written in Java
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