Implementation of Artificial Neural Networks using NumPy
A numpy based CNN implementation for classifying images
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.
Landcover classification using the fusion of HSI and LiDAR data.
MLP implementation in Python with PyTorch for the MNIST-fashion dataset (90+ on test)
Age estimation with PyTorch
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.
A small fully-connected neural network that can run MNIST optimized using BOHB
Code for Point Cloud Generation
A classical XOR neural network using pytorch
LeNet5 from Scratch
An implementation for an FCNN from scratch, for educational purposes
A simple deep learning library for training end-to-end fully-connected Artificial Neural Networks (ANNs), primarily based on numpy and autograd.
Minimal, limited in features, deep learning library, created with the goal of understanding more of the field.
Generic L-layer 'straight in Python' fully connected Neural Network implementation using numpy.
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