#计算机科学#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 Neur...
pip install antialiased-cnns to improve stability and accuracy
🦖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
#自然语言处理#Dilated CNNs for NER in TensorFlow
EEG Motor Imagery Tasks Classification (by Channels) via Convolutional Neural Networks (CNNs) based on TensorFlow
#计算机科学#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.
#计算机科学#[NeurIPS '18] "Can We Gain More from Orthogonality Regularizations in Training Deep CNNs?" Official Implementation.
#计算机科学#Code for the paper Language Identification Using Deep Convolutional Recurrent Neural Networks
#时序数据库#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.