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j-min /
Created Sep 24, 2019 — forked from arundasan91/
Caffe Installation Tutorial for beginners


Freshly brewed !

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.

Installation Instructions (Ubuntu 14 Trusty)

The following section is divided in to two parts. Caffe's documentation suggest

# -*- coding: utf-8 -*-
class Hangul:
BASE_CODE = 44032
# 초성 리스트. 00 ~ 18
'ㄱ', 'ㄲ', 'ㄴ', 'ㄷ', 'ㄸ', 'ㄹ', 'ㅁ', 'ㅂ', 'ㅃ',
import csv
import os
def get_csv_writer(filename, rows, delimiter):
with open(filename, 'w') as csvfile:
fieldnames = rows[0].keys()
writer = csv.DictWriter(csvfile, fieldnames=fieldnames, delimiter=delimiter)
for row in rows:

NLTK API to Stanford NLP Tools compiled on 2015-12-09

Stanford NER

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

j-min /
Last active Nov 15, 2016 — forked from erikbern/
TensorFlow Installation Log
# 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:
# Install various packages
sudo apt-get update
j-min /
Created Jul 13, 2016 — forked from karpathy/
Training a Neural Network ATARI Pong agent with Policy Gradients from raw pixels
""" Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """
import numpy as np
import cPickle as pickle
import gym
# hyperparameters
H = 200 # number of hidden layer neurons
batch_size = 10 # every how many episodes to do a param update?
learning_rate = 1e-4
gamma = 0.99 # discount factor for reward
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