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.
A numpy based CNN implementation for classifying images
Semantic Segmentation using Fully Convolutional Neural Network.
Landcover classification using the fusion of HSI and LiDAR data.
Age estimation with PyTorch
MLP implementation in Python with PyTorch for the MNIST-fashion dataset (90+ on test)
Different kinds of deep neural networks (DNNs) implemented from scratch using Python and NumPy, with a TensorFlow-like object-oriented API.
implementation of neural network from scratch only using numpy (Conv, Fc, Maxpool, optimizers and activation functions)
Linear Regression, Logistic Regression, Fully Connected Neural Network, Recurrent Neural Network, Convolution Neural Network
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.
LeNet5 from Scratch
A classical XOR neural network using pytorch
A small fully-connected neural network that can run MNIST optimized using BOHB
Code for Point Cloud Generation
An implementation for an FCNN from scratch, for educational purposes
Minimal, limited in features, deep learning library, created with the goal of understanding more of the field.
Digit recognition (MNIST dataset) using a fully connected neural network (97+ on test)
This is an implementation of a fully connected feedforward Neural Network (multi-layer perceptron) from scratch to classify MNIST hand-written digits
This repository holds one of my first Deep Learning projects. The project implements an MNIST classifying fully-connected neural network from scratch (in python) using only NumPy for numeric computations. For further information, please see README.
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