• A TPM error (6) occurred attempting to read a pcr value

• Supervised Transformer Network for Efficient Face Detection

• MSDN - Bash on Ubuntu on Windows
• Everything You Can Do With Windows 10’s New Bash Shell
• 如何评价 Bash for Windows？
• Windows Subsystem for Linux(WSL)的配置
• HOWTO: Ubuntu on Windows

post

• eGPU时代来临？笔记本外接显卡发展史

youtube videos

• https://www.youtube.com/watch?v=1OkQrzdE_I8
• https://www.youtube.com/watch?v=FZSMcKbuEnA
• https://www.youtube.com/watch?v=a4OoBfNL-GY
• https://www.youtube.com/watch?v=62z9Af3HoXg

speed up

• cascade

In file data/gae_proxy/manual.ini, write:

[hosts]
.google.com = direct
scholar.googleusercontent.com = direct

Adrien Gaidon

• xerox - Video Understanding

• http://book.pythontips.com/en/latest/index.html

concepts

• test case
• test
• test fixture

note:

• all names should not contain underscore

CLRS - Introduction to Algorithms

• https://github.com/gzc/CLRS

• Windows 7 下，有没有类似于 Dash 的软件？
• Dash for iOS, Android, Windows or Linux

• Learning about LSTMs using Torch

alias names:

• fat
• universal
• multi-architecture

refs

• Building a Universal Framework for iOS
• [CMake] iOS multi-arch library target and xcodebuild
• Combining multi-architecture static libraries (lipo & libtool - OS X)

• Mac OS X Setup Guide
• Mac OS X 设置指南
• Mac OS X 10.9 Mavericks

• 新入手MacBook Pro,从哪里入手?
• OS X 一无所知的初学者，如何才能快速上手这个系统？
• Mac for Developer
• Mac开发者2013年新机设置参考

GPUDirect vs CUDA-Aware MPI

• http://timdettmers.com/2014/09/21/how-to-build-and-use-a-multi-gpu-system-for-deep-learning/
• http://timdettmers.com/2014/11/09/model-parallelism-deep-learning/
• https://devblogs.nvidia.com/parallelforall/introduction-cuda-aware-mpi/

• Caffe Installation Steps
• Some notes about building Caffe RC3 with Mac OS X 10.11.3
• Theory of Building Caffe on OS X
• How to install Caffe on Mac OS X 10.10 for dummies (like me)
• caffe安装过程分享（for osx10.11.4）
• http://lizhecome.com/2016/04/05/deep-learning.html
• http://www.megastormsystems.com/news/how-to-install-caffe-on-mac-os-x-10-11

• caffe#1651 #1703
• shelhamer's Euclidean loss example

examples:

• python layer test and wiki
• python data layer
• python accuracy layer
• a blog post and this and this
• example in DIGITS

• Recommended TeX and LaTeX Books (and other printed resources)

• ctrlp.vim/issues#58

Bumblebee

• https://wiki.ubuntu.com/Bumblebee
• http://askubuntu.com/questions/452556/how-to-set-up-nvidia-optimus-bumblebee-in-14-04

• Accelerating the Super-Resolution Convolutional Neural Network

• #701

• https://www.v2ex.com/t/315357

• 像 IDE 一样使用 vim
• Use Vim as a Python IDE

• #3296

• Boost.Python Reference Manual
• register_ptr_to_python
• InternalDataStructures
  • instance.hpp

Heap vs BST

• What's the difference between a binary search tree and a binary heap?
• Heap vs Binary Search Tree (BST)
• Why is Binary Heap Preferred over BST for Priority Queue?

sed

• http://stackoverflow.com/questions/4412945/case-insensitive-search-replace-with-sed
• http://unix.stackexchange.com/questions/13711/differences-between-sed-on-mac-osx-and-other-standard-sed
• http://apple.stackexchange.com/questions/69223/how-to-replace-mac-os-x-utilities-with-gnu-core-utilities

• write a python layer for caffe
  • http://chrischoy.github.io/blog/research/caffe-python-layer/
  • https://groups.google.com/forum/#!topic/caffe-users/xEJJ560YN7g
  • https://github.com/BVLC/caffe/blob/master/python/caffe/test/test_python_layer.py

LuaJ

• https://bitbucket.org/sylvanaar2/lua-for-idea/issues/183/add-find-lua-sdk-when-installed-with-torch
• http://stackoverflow.com/questions/35562272/require-doesnt-find-shared-libraries
• https://groups.google.com/forum/#!topic/torch7/ByRDB3Q0E4I
• https://github.com/ET-Chan/th4j
• http://www.lighting-torch.com/2015/07/27/configuring-eclipse-with-torch/
• http://stackoverflow.com/questions/11253420/calling-a-lua-function-from-luaj

• macOS的Homebrew的mirror配置

• Thinkpad
  • X1 Carbon / Yoga
• Surface Book
• Macbook Pro

• 神经网络与深度学习 by 邱锡鹏

• http://selfboot.cn/2014/10/13/html2dash/
• https://medium.com/@pbojinov/creating-your-own-dash-docsets-255ac63fb83c#.jm57n8d00

architecture

• http://docs.nvidia.com/cuda/cuda-compiler-driver-nvcc/index.html#virtual-architecture-feature-list

• Pascal GPU 架构详解 by 卜居

Detection

PASCAL VOC

• 20 classes
• ~20k images
• 2.4 objects/images

ILSVRC 2014

• 200 classes
• ~470k images
• 1.1 objects/images

MS-COCO

• 80 classes
• ~120k images
• 7.2 objects/images

rng

• http://blogs.unity3d.com/2015/01/07/a-primer-on-repeatable-random-numbers/
• http://stackoverflow.com/questions/322938/recommended-way-to-initialize-srand
• https://www.guyrutenberg.com/2007/09/03/seeding-srand/
• http://stackoverflow.com/questions/20636859/random-number-generator-why-seed-every-time
• http://stackoverflow.com/questions/7343833/srand-why-call-it-only-once

• https://developer.apple.com/library/content/technotes/tn2339/_index.html
• https://www.cocoanetics.com/2012/07/you-dont-need-the-xcode-command-line-tools/
• Building from the Command Line with Xcode FAQ
• Developer Binaries on OS X, xcode-select and xcrun

see:

• Integral Channel Features

• BVLC/caffe#3227
• BVLC/caffe#2320 (comment)
• oracle/node-oracledb#231
• commercialhaskell/stack#1161

clj

• 【直播】我的OI生涯。。。

首先狂刷OJ追求刷题数量我觉得是很不可取的...
有些题目根本没什么意思还是为了题目数去刷显然是浪费时间(LZ自己怒中一枪)...
做题目说白了也就两个用处,锻炼算法能力后者锻炼代码能力,在实际比赛中,对于中等水平的人来说,反而是后者比较重要..
要如何锻炼代码能力呢?
如果你能够达到几百行写下来随便A的程度那就OK了,
这大概一是要美化代码风格方便调试,
二是要总结一些不容易写错的手法,
其实多多写写题目的时候注意注意就能知道哪里最容易犯SB了...
写的时候认真一点就行了...
另外就是多看看别人的代码,很多写的好代码看了之后也能积累点感觉...
个人推荐Petr啊,Egor啊,rng58啊之类人的代码...
算法能力么...
其实个人觉得刷题目完全没有帮助...
多看看题目和题解积累积累感觉然后自己试着去想一想怎么做...
压根就不用写出来...

speech recognition

• Soltau, Hagen, Hank Liao, and Hasim Sak. "Neural Speech Recognizer: Acoustic-to-Word LSTM Model for Large Vocabulary Speech Recognition." arXiv preprint arXiv:1610.09975 (2016).
• Joint CTC-attention Based End-to-end Speech Recognition Using Multi-task Learning

Node

struct Node {
    int data;
    shared_ptr<Node> left;
    shared_ptr<Node> right;
    Node(int val) : data(val) {}
};

• 从入门到精通: 最小费用流的“zkw算法”

to learn

• multi-threading

• #941

• Scipy Lecture Notes
• Python科学计算 第二版

• Wiki - Test functions for optimization
• Scipy - 2.7. Mathematical optimization: finding minima of functions
• Y2B - Visualize Back Propagation
• Visualizing and Animating Optimization Algorithms with Matplotlib
• The Concept of Conjugate Gradient Descent in Python
• Visualize Backpropagation Algorithms

• #710

• readthedocs/readthedocs.org/issues/662