jdc08161063 Goto Github PK

cyclemlp

Implementation of "CycleMLP: A MLP-like Architecture for Dense Prediction"

cytokit

Microscopy Image Cytometry Toolkit

d-cnn

Depth CNNs for RGB-D Scene Recognition: Learning From Scratch Better Than Transferring From RGB-CNNs

d2l-en

Dive into Deep Learning (CV chapters coming soon)

d3net_depth_estimation

Dense Deep Depth Estimation Network (D3-Net) in PyTorch.

d4pg

Tensorflow implementation of a Deep Distributed Distributional Deterministic Policy Gradients (D4PG) network, trained on OpenAI Gym environments.

da-faster-rcnn

An implementation of our CVPR 2018 work 'Domain Adaptive Faster R-CNN for Object Detection in the Wild'

da-rnn

Semantic Mapping with Data Associated Recurrent Neural Networks

daae_

Denoising Adversairal Autoencoders

dab-and-tpose-controlled-lights

Control your lights with dab and t-pose, duh

daft

Code for the NeurIPS 2019 paper: "Learning Dynamics of Attention: Human Prior for Interpretable Machine Reasoning"

dag-gnn

dagan

[ACM MM 2020] Dual Attention GANs for Semantic Image Synthesis

dagcn

Dual Attention Graph Convolutional Network

dagstuhlgan

dal

Repository for Deep Active Localization research and benchmarks

dali

DALI: a large Dataset of synchronised Audio, LyrIcs and vocal notes.

dampr

Python Data Processing library

dan

Demo code for the paper ''Distributional Adversarial Networks''

dancenet

DanceNet - Dance generator using Autoencoder, LSTM and Mixture Density Network. (Keras)

danet

Dual Attention Network for Scene Segmentation

danser-www-19

darkflow

Translate darknet to tensorflow. Load trained weights, retrain/fine-tune using tensorflow, export constant graph def to mobile devices

darknet

Convolutional Neural Networks

darkon

Performance hacking for your deep learning models

dash

Interactive, reactive web apps in pure python :star:

data-competition-topsolution

Data competition Top Solution 数据竞赛top解决方案开源整理

data-poisoning-release

data-science-ipython-notebooks

Continually updated data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.

data-science-primer

A set of self paced resources for anyone looking to get into data science. The materials assume an absolute beginner and are intended to prepare students for the Galvanize Data Science interview process: http://www.galvanize.com/courses/data-science/