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dcgan-completion.tensorflow

Image Completion with Deep Learning in TensorFlow

dcgan-tensorflow

A tensorflow implementation of "Deep Convolutional Generative Adversarial Networks"

deep-learning-for-image-retrieval

Page to list the related works on deep learning for image retrieval.

deep-reinforcement-learning-papers

A list of recent papers regarding deep reinforcement learning

deep-residual-networks

Deep Residual Learning for Image Recognition

deep-visualization-toolbox

DeepVis Toolbox

deep-viz-keras

Implementations of some popular Saliency Maps in Keras

deepcompression-caffe

Caffe for Deep Compression

deepconcolic

Concolic Testing for Deep Neural Networks

deeplearning_tutorials

The deeplearning algorithms implemented by tensorflow

deeprl

deep reinforcement learning for personal research

deformable-convnets

Deformable Convolutional Networks

deformable-convolution-v2-pytorch

Deformable ConvNets V2 in PyTorch

dncnn

Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising (TIP, 2017)

face_alignment_two_stage_re-initialization

Test code of the CVPR 2017 paper "A Deep Regression Architecture with Two-Stage Re-initialization for High Performance Facial Landmark Detection"

facenet

Face recognition using Tensorflow

fastannotationtool

A tool using OpenCV to annotate images for image classification, optical character reading, ...

fb.resnet.torch

Torch implementation of ResNet from http://arxiv.org/abs/1512.03385 and training scripts

gcn

Implementation of Graph Convolutional Networks in TensorFlow

glove-python

Toy Python implementation of http://www-nlp.stanford.edu/projects/glove/

graph-neural-networks

graph_nets

Build Graph Nets in Tensorflow

hiddenlayer

Neural network graphs and training metrics for PyTorch, Tensorflow, and Keras.

icml-2017-papers

https://2017.icml.cc/Conferences/2017/Schedule

icml-2018-papers

https://icml.cc/Conferences/2018/Schedule

imageprocessing-python

该资源为作者在CSDN的撰写Python图像处理文章的支撑，主要是Python实现图像处理、图像识别、图像分类等算法代码实现，希望该资源对您有所帮助，一起加油。

improved-gan

code for the paper "Improved Techniques for Training GANs"

interpretable-research

I collected some papers about interpretable CNN and reorganized them here.

introduction-to-time-series-analysis-in-python

From stock prices to climate data, time series data are found in a wide variety of domains, and being able to effectively work with such data is an increasingly important skill for data scientists. This course will introduce you to time series analysis in Python. After learning about what a time series is, you'll learn about several time series models ranging from autoregressive and moving average models to cointegration models. Along the way, you'll learn how to estimate, forecast, and simulate these models using statistical libraries in Python. You'll see numerous examples of how these models are used, with a particular emphasis on applications in finance.

inverse-reinforcement-learning

Implementations of selected inverse reinforcement learning algorithms.