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2017年5月2日 星期二

Deep Learning Reading List (Tools, Tutorials, Papers)

Deep Learning Fundamentals
R. Pieters
Python for image understanding link
A good talk for beginners
Chih-Fan Hsu, Chun-Ming Chang
手把手的深度學習實務
(slides) (labs) (github)
Great training course in Chinese, made by Data Insights Research Lab in Academia Sinica
Deep learning tutorials
Provide in-depth introduction to deep learning theories and implementation with Theano.
Neural networks and deep learnings
A very comprehensive tutorial written by Michael Nielsen
Christopher Olah
A good blog to visualize NN
Geoffrey E. Hinton et al.
The breakthrough paper that introduces restricted Boltzmann machines and stacked deep-belief networks
Y LeCun,
Y Bengio,
G Hinton
Deep Learning, Nature, 2015
Review paper written by fathers of neural networks
Deep Learning Tools
TensorFlow
Open source library developed by Google with C++ core and Python interface
Keras
A wrapper for Theano and Tensorflow, very user friendly.
Theano
DL library for Python developed by Université de Montréal
Caffe2
A fast and scalable C++ DL framework for visual recognition. Based on Caffe from UC Berkely
Deep Reinforcement Learning
Tambet Matiisen
Great introduction. Algorithms are implemented with Neon
Arhtur Juliani

Open AI Gym
A framework let your RL algorithms play video games
Mnih, et al.
Human-Level Control through Deep Reinforcement Learning, Nature, 2015
Deep Q-network (DQN) by Google Deep Mind (code)
David Sliver, Aja Huang et al.
Paper of AlphaGo, period.
DL for Image Classification and Object Detection
An image database with 14 million images organized according to the WordNet hierarchy
Hold the Large Scale Visual Recognition Challenge (ILSVRC) challenges every year
Alex Krizhevsky
Ilya Sutskever
Geoffrey E. Hinton
First successful deep network for image classification, aka AlexNet

Karen Simonyan, Andrew Zisserman
Winner of ILSVRC 2014
C. Szegedy et al.
Google Research
Going Deeper with Convolutions, CVPR 2015
GoogLeNet
K. He et al.
Microsoft Research
CVPR 2016 best paper and winner of ILSVRC 2015, aka ResNet
DL for Image Annotations
A. Karpathy
Li Fei-Fei
Deep Visual-Semantic Alignments for Generating Image Descriptions, CVPR 2015

Justin Johnson
Andrej Karpathy
Li Fei-Fei
DenseCap: Fully Convolutional Localization Networks for Dense Captio, CVPR 2016

DL for Video Applications (CVPR 2016)
Ting Yao et al.
Highlight Detection with Pairwise Deep Ranking for First-Person Video Summarization [pdf

Michael Cgyli et al.
Video2GIF: Automatic Generation of Animated GIFs from Video [pdf
B. Tekin et al.
Direct Prediction of 3D Body Poses from Motion Compensated Sequences [pdf

Bingbing Li. et al.
Progressively Parsing Interactional Objects for Fine Grained Action Detection [pdf

Pingbo Pan et al.
Hierarchical Recurrent Neural Encoder for Video Representation With Application to Captioning [pdf

Zheng Shou, Dongang Wang, Shih-Fu Chang
Temporal Action Localization in Untrimmed Videos via Multi-stage CNNs [pdf

Ziwei Liu et al.
DeepFashion: Powering Robust Clothes Recognition and Retrieval With Rich Annotations [pdf]

Xiaofan Zhang et al.
Embedding Label Structures for Fine-Grained Feature Representation [pdf]

Other Video Applications (CVPR 2016)
Jingjing Meng et al.
From Keyframes to Key Objects:
Video Summarization by Representative Object Proposal Selection [pdf


K.  Grauman et al.
Summary Transfer: Exemplar-based Subset Selection for Video Summarization [pdf

Feng Zhou, Yuanqing Lin
Fine-Grained Image Classification by Exploring Bipartite-Graph Labels [pdf]

2017年3月27日 星期一

Build a Nvidia CUDA server with Ubuntu 16.04 in 4 steps



*First of all, if you start from a brand new server, I suggest to install Ubuntu 16.04 WITHOUT NVIDIA graphics cards first. This will prevent Ubuntu from automatically installing open-source NVIDIA driver Nouveau. Nouveau may cause issues like black screen, lightdm crash, ..., to name a few. It's highly possible that you will see NOTHING at the inital boot if you install Ubuntu 16.04 directly with NVIDIA cards.

Once you can login the Ubuntu server, install CUDA following the 4 steps below:

1. Disable Nouveau
If you are running Desktop version, enter the terminal screen by typing
Ctrl+Alt+F1

Open or create "blacklist-nouveau.conf":

sudo vim /etc/modprobe.d/blacklist-nouveau.conf

Add following commands to the file:

blacklist nouveau
blacklist lbm-nouveau
options nouveau modeset=0
alias nouveau off
alias lbm-nouveau off

Then build the new kernel:

sudo update-initramfs -u


2. Install NVIDIA driver
You can try install NVIDIA driver directly:

sudo apt-get install nvidia-375

*If apt-get cannot find nvidia driver
we need to add the ppa manually. The commands below are referred from here:
Although you can also install the drivers included in the CUDA toolkit. I suggest to install from Ubuntu ppa:
sudo apt-add-repository ppa:graphics-drivers/ppa
sudo apt-get update
sudo service lightdm stop
sudo apt-get purge nvidia-*
sudo apt-get install nvidia-375


Once the driver is installed, reboot your system, then test the driver by typing:

nvidia-smi

And you will see the NVIDIA cards installed in your system:

+-----------------------------------------------------------------------------+
| NVIDIA-SMI 375.39                 Driver Version: 375.39                    |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|===============================+======================+======================|
|   0  GeForce GTX 1080    Off  | 0000:02:00.0      On |                  N/A |
|  0%   43C    P8     8W / 200W |    294MiB /  8107MiB |      0%      Default |
+-------------------------------+----------------------+----------------------+
|   1  GeForce GTX 1080    Off  | 0000:82:00.0     Off |                  N/A |
|  0%   38C    P8     8W / 200W |      1MiB /  8114MiB |      0%      Default |
+-------------------------------+----------------------+----------------------+
                                                                            
+-----------------------------------------------------------------------------+
| Processes:                                                       GPU Memory |
|  GPU       PID  Type  Process name                               Usage      |
|=============================================================================|
|    0      1430    G   /usr/lib/xorg/Xorg                             144MiB |
|    0      2549    G   /usr/bin/compiz                                148MiB |
+-----------------------------------------------------------------------------+



3. Install CUDA Toolkit
Download CUDA toolkit from NVIDIA official site:
https://developer.nvidia.com/cuda-downloads

Remember to select runfile (local)

sudo ./cuda_8.0.61_375.26_linux.run --override

As we already installed NVIDIA driver, we choose "NOT" to install driver this time:
Install NVIDIA Accelerated Graphics Driver for Linux-x86_64 361.62?
(y)es/(n)o/(q)uit: n


Because Ubuntu 16.04 has latest gcc 6, which is not supported by CUDA. The --override flag force the installer to ignore unsupported gcc version.

Once installation is done, you may notice there is a warning:
***WARNING: Incomplete installation! This installation did not install the CUDA Driver. A driver of version at least 361.00 is required for CUDA 8.0 functionality to work.
To install the driver using this installer, run the following command, replacing with the name of this run file:
sudo .run -silent -driver


Don't worry, we can ignore this message.


4. Downgrade gcc to 4.9
Finally, we need to downgrade the gcc/g++ version in Ubuntu to < 5.0. Somebody may suggest to remove check line in CUDA library's header file. Don't do this, it will cause compiler error. Downgrade the gcc with "update-alternatives":


sudo apt-get install g++-4.9 gcc-4.9 libgcc-4.9

sudo update-alternatives --remove-all gcc 
sudo update-alternatives --remove-all g++

sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-4.9 20
sudo update-alternatives --install /usr/bin/g++ g++ /usr/bin/g++-4.9 20

sudo update-alternatives --query gcc
sudo update-alternatives --query g++