Implementation of Artificial Neural Networks using NumPy
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Updated
Jun 19, 2023 - Python
Implementation of Artificial Neural Networks using NumPy
Building a HTTP-accessed convolutional neural network model using TensorFlow NN (tf.nn), CIFAR10 dataset, Python and Flask.
Semantic Segmentation using Fully Convolutional Neural Network.
A numpy based CNN implementation for classifying images
Age estimation with PyTorch
Landcover classification using the fusion of HSI and LiDAR data.
MLP implementation in Python with PyTorch for the MNIST-fashion dataset (90+ on test)
Linear Regression, Logistic Regression, Fully Connected Neural Network, Recurrent Neural Network, Convolution Neural Network
A classical XOR neural network using pytorch
Deep learning applications with different datasets.
Simple Python implementation of a fully connected neural network
implementation of neural network from scratch only using numpy (Conv, Fc, Maxpool, optimizers and activation functions)
A framework for implementing convolutional neural networks and fully connected neural network.
ANN made from scratch implemented on mnist dataset for digit classification
This project was my final Bachelor's degree thesis. In it I decided to mix my passion, music, and the syllabus that I liked the most in my degree, deep learning.
Different kinds of deep neural networks (DNNs) implemented from scratch using Python and NumPy, with a TensorFlow-like object-oriented API.
Digit recognition (MNIST dataset) using a fully connected neural network (97+ on test)
Classification of different landcover classes using Hyperspectral data.
This is an implementation of a fully connected feedforward Neural Network (multi-layer perceptron) from scratch to classify MNIST hand-written digits
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