convolutional-layers

Acceleration package for neural networks on multi-core CPUs

An implementation of "MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing" (ICML 2019).

An example of visualization of weights and output of convolutional layers

Rede Neural Convolucional para reconhecimento de gestos em LIBRAS (Alfabeto) Projeto 01/2019 - Ciência da Computação (Universidade Anhembi Morumbi)

Using Tensorflow to classify the NIST Dataset 19 (Handwriting)

Tensorflow implementation for 'LCNN: Lookup-based Convolutional Neural Network'. Predict Faster using Models Trained Fast with Multi-GPUs

Octave convolution

Implemented Convolutional Neural Network, LSTM Neural Network, and Neural Network From Scratch in Python Language.

Multi class audio classification using Deep Learning (MLP, CNN): The objective of this project is to build a multi class classifier to identify sound of a bee, cricket or noise.

A look at some simple autoencoders for the Cifar10 dataset, including a denoising autoencoder. Python code included.

Caffe implementation for Active Shift Layer(ASL)

Tensorflow implementation for Active Shift Layer(ASL)

Pytorch Implementations of Common modules, blocks and losses for CNNs specifically for segmentation models

Code for the paper entitled "Deep neural network for traffic sign recognition systems: An analysis of spatial transformers and stochastic optimisation methods".

Implementing Neural Networks for Computer Vision in autonomous vehicles and robotics for classification, pattern recognition, control. Using Python, numpy, tensorflow. From basics to complex project

This library provides a set of basic functions for different type of deep learning (and other) algorithms in C.This deep learning library will be constatly updated

Deep learning architecture to help a drone target and follow people

Sub-Pixel Convolutional Layer with Tensorflow/Keras

A Python implementation of the InverSynth method (Barkan, Tsiris, Koenigstein, Katz)

In this project we have explored the use of imaging time series to enhance forecasting results with Neural Networks. The approach has revealed itself to be extremely promising as, both in combination with an LSTM architecture and without, it has out-performed the pure LSTM architecture by a solid margin within our test datasets.

Capstone Project 2 for Data Science Career Track: Image Classification of Dog Breeds

Simple MATLAB toolbox for deep learning network: Version 1.0.3

Visualization of the filters of VGG16, via gradient ascent in input space.

ResNet in tensorflow for CIFAR-100, CIFAR-10

Library which can be used to build feed forward NN, Convolutional Nets, Linear Regression, and Logistic Regression Models.

Winograd Convolution in Julia with CUDA kernels

Python module for image convolution and ML classification.

SANNet Neural Network Framework

A CNN implementation of Google's "Quick, Draw!"

Convolution 2D cuDNN C++ implement demo 二维卷积的cuDNN实现样例 2次元畳み込みのcuDNN実装例