cnns

Notes, programming assignments and quizzes from all courses within the Coursera Deep Learning specialization offered by deeplearning.ai: (i) Neural Networks and Deep Learning; (ii) Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization; (iii) Structuring Machine Learning Projects; (iv) Convolutional Neural Networks; (v) Sequence Models

pip install antialiased-cnns to improve stability and accuracy

E(2)-Equivariant CNNs Library for Pytorch

🦖Pytorch implementation of popular Attention Mechanisms, Vision Transformers, MLP-Like models and CNNs.🔥🔥🔥

Revisions and implementations of modern Convolutional Neural Networks architectures in TensorFlow and Keras

Keras implementation of a ResNet-CAM model

Dilated CNNs for NER in TensorFlow

EEG Motor Imagery Tasks Classification (by Channels) via Convolutional Neural Networks (CNNs) based on TensorFlow

This repository contains my solutions to the assignments for Stanford's CS231n "Convolutional Neural Networks for Visual Recognition" (Spring 2020).

A quick view of high-performance convolution neural networks (CNNs) inference engines on mobile devices.

[NeurIPS '18] "Can We Gain More from Orthogonality Regularizations in Training Deep CNNs?" Official Implementation.

Code for the paper Language Identification Using Deep Convolutional Recurrent Neural Networks

Code repository of the paper "Wavelet Networks: Scale-Translation Equivariant Learning From Raw Time-Series, TMLR" https://arxiv.org/abs/2006.05259

Going deeper into Deep CNNs through visualization methods: Saliency maps, optimize a random input image and deep dreaming with Keras

A Novel Approach to Video Super-Resolution using Frame Recurrence and Generative Adversarial Networks | Python3 | PyTorch | OpenCV2 | GANs | CNNs

several basic neural networks[mlp, autoencoder, CNNs, recurrentNN, recursiveNN] implements under several NN frameworks[ tensorflow, pytorch, theano, keras]

Code repository for the paper "Attentive Group Equivariant Convolutional Neural Networks" published at ICML 2020. https://arxiv.org/abs/2002.03830

[CogSci'21] Study of human inductive biases in CNNs and Transformers.

The official PyTorch implementation for "Normalized Convolution Upsampling for Refined Optical Flow Estimation"

Presents comprehensive benchmarks of XLA-compatible pre-trained models in Keras.