jakezhaojb Goto Github PK

20190507nyu

2019/05/07 Lab session for Deep learning

a4_jz1672

Deep learning assignment 4 submission

adaboost-py

A python project implementing the famous adaboost.

adversarial

Reproducing the results of "Intriguing Properties of Neural Networks" and beyond

adversarial_image_defenses

Countering Adversarial Image using Input Transformations.

arae

Code for the paper "Adversarially Regularized Autoencoders (ICML 2018)" by Zhao, Kim, Zhang, Rush and LeCun

backpropagation-c

Available when training a Neural Network, or an Autoencoder.

batch_cropping-opencv

This package is used to crop a batch of images, specifically for computer vision researches who are preparing their training samples.

c3d

C3D is a modified version of BVLC caffe to support 3D ConvNets.

ccv

C-based/Cached/Core Computer Vision Library, A Modern Computer Vision Library

cdev

Cross device prediction for ICDM kaggle competition

clweadv

Towards cross-lingual distributed representations without parallel text trained with adversarial autoencoders

cnn-relation-extraction

Tensorflow Implementation of Convolutional Neural Network for Relation Extraction (COLING 2014, NAACL 2015)

code-samples

Source code examples from the Parallel Forall Blog

convae

Convolutional Autoencoder

cpbd-sharpness

The code is used to evaluate the shapeness of images.

crepe

Text classification from character-level using convolutional networks

csnet

The deep Learning network based on Compressive Sensing

cuda-cnn

CNN accelerated by cuda. Test on mnist and finilly get 99.7%

cudnn.torch

Torch-7 FFI bindings for NVIDIA CuDNN

cunn

data2text-plan-py

datasciencecourse

This holds iPython notebooks and lecture slides for the Intro to Data Science Master's course I teach at NYU.

deep-music-autoencoder

The repo involves a Deep Learning method, Stacked Denoising Autoencoder. The repo exploits "paracel", which is a new parallel computing framework

deepdream

deeplearning

Deep Learning Code / Examples

deeplearntoolbox

Matlab/Octave toolbox for deep learning. Includes Deep Belief Nets, Stacked Autoencoders, Convolutional Neural Nets, Convolutional Autoencoders and vanilla Neural Nets. Each method has examples to get you started.

delta-t-prophet

Recommendation system for what universities you would apply.

diving_problem

Problem @ 2014 Data Analysis and Statistical Computing Immersion at PKU.

dl4mt-material