ZACKLEON's Stars
apachecn/ailearning
AiLearning：数据分析+机器学习实战+线性代数+PyTorch+NLTK+TF2
fengdu78/deeplearning_ai_books
deeplearning.ai（吴恩达老师的深度学习课程笔记及资源）
NLP-LOVE/ML-NLP
此项目是机器学习(Machine Learning)、深度学习(Deep Learning)、NLP面试中常考到的知识点和代码实现，也是作为一个算法工程师必会的理论基础知识。
Mikoto10032/DeepLearning
深度学习入门教程, 优秀文章, Deep Learning Tutorial
DmitryUlyanov/deep-image-prior
Image restoration with neural networks but without learning.
aladdinpersson/Machine-Learning-Collection
A resource for learning about Machine learning & Deep Learning
czy36mengfei/tensorflow2_tutorials_chinese
tensorflow2中文教程，持续更新(当前版本:tensorflow2.0)，tag: tensorflow 2.0 tutorials
Alro10/deep-learning-time-series
List of papers, code and experiments using deep learning for time series forecasting
gempy-project/gempy
GemPy is an open-source, Python-based 3-D structural geological modeling software, which allows the implicit (i.e. automatic) creation of complex geological models from interface and orientation data. It also offers support for stochastic modeling to address parameter and model uncertainties.
LongxingTan/Time-series-prediction
tfts: Time Series Deep Learning Models in TensorFlow
EndlessSora/focal-frequency-loss
[ICCV 2021] Focal Frequency Loss for Image Reconstruction and Synthesis
KyleBing/english-vocabulary
英文单词，英语单词，英语四六级、考研、SAT单词，txt 文件, json 文件，CET4 CET6，乱序，单词
cszn/FFDNet
FFDNet: Toward a Fast and Flexible Solution for CNN based Image Denoising (TIP, 2018)
murufeng/FUIR
A Flexible and Unified Image Restoration Framework (PyTorch), including state-of-the-art image restoration model. Such as NAFNet, Restormer, MPRNet, MIMO-UNet, SCUNet, SwinIR, HINet, etc. ⭐⭐⭐⭐⭐⭐
SeisSol/SeisSol
A scientific software for the numerical simulation of seismic wave phenomena and earthquake dynamics
yuanli2333/Hadamard-Matrix-for-hashing
CVPR2020/TNNLS2023: Central Similarity Quantization/Hashing for Efficient Image and Video Retrieval
AI4EPS/DeepDenoiser
DeepDenoiser: Seismic Signal Denoising and Decomposition Using Deep Neural Networks
ramarlina/DenoisingAutoEncoder
Python implementation of Stacked Denoising Autoencoders for unsupervised learning of high level feature representation
chengtaipu/lowrankcnn
Low-rank convolutional neural networks
beala/deep-image-prior-tensorflow
An implementation of https://dmitryulyanov.github.io/deep_image_prior for tensorflow.
eunh/low_dose_CT
Deep Convolutional Framelet Denoising for Low-Dose CT via Wavelet Residual Network
sevenysw/MathGeo2018
MathGeo: A toolbox for seismic data processing
zhulingchen/SSSI
Seismic Simulation, Survey, and Imaging (SSSI)
3outeille/Research-Paper-Summary
Summary & Implementation of Deep Learning research paper in Tensorflow/Pytorch.
Hadryan/TFNet-for-Environmental-Sound-Classification
Learning discriminative and robust time-frequency representations for environmental sound classification: Convolutional neural networks (CNN) are one of the best-performing neural network architectures for environmental sound classification (ESC). Recently, attention mechanisms have been used in CNN to capture the useful information from the audio signal for sound classification, especially for weakly labelled data where the timing information about the acoustic events is not available in the training data, apart from the availability of sound class labels. In these methods, however, the inherent time-frequency characteristics and variations are not explicitly exploited when obtaining the deep features. In this paper, we propose a new method, called time-frequency enhancement block (TFBlock), which temporal attention and frequency attention are employed to enhance the features from relevant frames and frequency bands. Compared with other attention mechanisms, in our method, parallel branches are constructed which allow the temporal and frequency features to be attended respectively in order to mitigate interference from the sections where no sound events happened in the acoustic environments. The experiments on three benchmark ESC datasets show that our method improves the classification performance and also exhibits robustness to noise.
HyeongseokSon1/CNN_deconvolution
CNN based non-blind deconvolution (presented at ICCP 2017)
mikhailiuk/Deep-Learning-Applied-To-Seismic-Data
Seismic data interpolation. C++ implementation.
kerim371/G_Seis
Simple GUI application aimed at surface-consistent seismic static and amplitude correction. Interactive velocity model builder
simfei/denoising
This is the implementation of several supervised and unsupervised approaches for multiphoton microscopy image denoising, including CARE, DnCNN, ResNet, Noise2Noise, Noise2Void, Probabilistic Noise2Void, and structured Noise2Void.
harshit0511/Unsupervised-Image-denoising