tarus gontthias

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

	'''This script goes along the blog post
	"Building powerful image classification models using very little data"
	from blog.keras.io.
	It uses data that can be downloaded at:
	https://www.kaggle.com/c/dogs-vs-cats/data
	In our setup, we:
	- created a data/ folder
	- created train/ and validation/ subfolders inside data/
	- created cats/ and dogs/ subfolders inside train/ and validation/
	- put the cat pictures index 0-999 in data/train/cats

	#!/bin/sh

	# you can replace $HOME with any dir
	sed -i 's@\$TEXLIVEHOME@'"$HOME"'@' texlive.profile
	wget http://mirror.ctan.org/systems/texlive/tlnet/install-tl-unx.tar.gz
	tar zxf install-tl-unx.tar.gz
	./install-tl*/install-tl -profile texlive.profile

	# texlive.tar.gz is a portable and full TeXLive package
	tar zcf texlive.tar.gz -C $HOME texlive

	from keras.models import Sequential
	from keras.layers.core import Dense, Dropout, Activation, Flatten
	from keras.layers.convolutional import Convolution2D, MaxPooling2D
	from keras.layers.normalization import BatchNormalization

	#AlexNet with batch normalization in Keras
	#input image is 224x224

	model = Sequential()
	model.add(Convolution2D(64, 3, 11, 11, border_mode='full'))