| # -*- coding: utf-8 -*- | |
| """ | |
| codegen | |
| ~~~~~~~ | |
| Extension to ast that allow ast -> python code generation. | |
| :copyright: Copyright 2008 by Armin Ronacher. | |
| :license: BSD. | |
| """ |
This script scans your Dropbox (or any given folder) for folders stored in the ignore array and excludes them from syncing. Makes use of the official Dropbox CLI
I'm a beginner at bash, so all improvements are welcome!
#!/bin/bash
set -e
# SETTINGS
| # Steps to build and install tmux from source. | |
| # Takes < 25 seconds on EC2 env [even on a low-end config instance]. | |
| VERSION=2.7 | |
| sudo yum -y remove tmux | |
| sudo yum -y install wget tar libevent-devel ncurses-devel | |
| wget https://github.com/tmux/tmux/releases/download/${VERSION}/tmux-${VERSION}.tar.gz | |
| tar xzf tmux-${VERSION}.tar.gz | |
| rm -f tmux-${VERSION}.tar.gz | |
| cd tmux-${VERSION} |
| """ | |
| Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy) | |
| BSD License | |
| """ | |
| import numpy as np | |
| # data I/O | |
| data = open('input.txt', 'r').read() # should be simple plain text file | |
| chars = list(set(data)) | |
| data_size, vocab_size = len(data), len(chars) |
| #!/usr/bin/python3 | |
| import datetime | |
| import sys | |
| import math | |
| import numpy as np | |
| from argparse import ArgumentParser | |
| from collections import defaultdict | |
| from chainer import FunctionSet, Variable, functions, optimizers |
| # Note – this is not a bash script (some of the steps require reboot) | |
| # I named it .sh just so Github does correct syntax highlighting. | |
| # | |
| # This is also available as an AMI in us-east-1 (virginia): ami-cf5028a5 | |
| # | |
| # The CUDA part is mostly based on this excellent blog post: | |
| # http://tleyden.github.io/blog/2014/10/25/cuda-6-dot-5-on-aws-gpu-instance-running-ubuntu-14-dot-04/ | |
| # Install various packages | |
| sudo apt-get update |
With NLTK version 3.1 and Stanford NER tool 2015-12-09, it is possible to hack the StanfordNERTagger._stanford_jar to include other .jar files that are necessary for the new tagger.
First set up the environment variables as per instructed at https://github.com/nltk/nltk/wiki/Installing-Third-Party-Software
| # use the following snippet in your ipython notebook shell | |
| import argparse | |
| import tensorflow as tf | |
| tf.app.flags.FLAGS = tf.python.platform.flags._FlagValues() | |
| tf.app.flags._global_parser = argparse.ArgumentParser() |
With the availability of huge amount of data for research and powerfull machines to run your code on, Machine Learning and Neural Networks is gaining their foot again and impacting us more than ever in our everyday lives. With huge players like Google opensourcing part of their Machine Learning systems like the TensorFlow software library for numerical computation, there are many options for someone interested in starting off with Machine Learning/Neural Nets to choose from. Caffe, a deep learning framework developed by the Berkeley Vision and Learning Center (BVLC) and its contributors, comes to the play with a fresh cup of coffee.
The following section is divided in to two parts. Caffe's documentation suggest
| # Working example for my blog post at: | |
| # https://danijar.github.io/structuring-your-tensorflow-models | |
| import functools | |
| import tensorflow as tf | |
| from tensorflow.examples.tutorials.mnist import input_data | |
| def doublewrap(function): | |
| """ | |
| A decorator decorator, allowing to use the decorator to be used without |
