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        <title>courses:2017:cs551</title>
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        <description>CS551: Introduction to Deep Learning

This course will provide basic understanding of deep learning and how to solve classification problems having large amount of data. In this course several open source tools will be demonstrated to build deep learning network.</description>
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        <description>CS551: Final Projects

	*  Question answering system by Chanchal Suman (CSE) &amp; Himani Srivastava (CSE)
		*  [ Report], [ Presentation]

	*   Sentiment analysis of movie reviews  by Nikhil Cheke (CSE) &amp; Harsimran Bedi (CSE)
		*  [ Report], [ Presentation]

	*    House number recognition from street view</description>
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        <description>General information for projects

	*  Need to take up a problem and apply DL techniques to solve it.
	*  It will carry 40% weight for your grades.
	*  Project can be done in a group having no more that 2 students
	*  

Announcement

	*  Please submit your write-up by 18th February.</description>
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        <description>Installation guide for Keras


Step 1. An easy way to install most of the dependencies is to install Anaconda.
		- Download page https://www.continuum.io/downloads
			--install
					bash Anaconda2-4.2.0-Linux-x86_64.sh 
					
			--allow set up path at the end of installation
			

Step 2. Check PATH variable echo $PATH.
		- If not set, then set the path in .bashrc file.
				--export PATH=&quot;/home/niraj/anaconda2/bin:$PATH&quot;
		- Restart terminal and check again.


Step 3. Installing theano using pip …</description>
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        <description>Sample code


#basic MLP network
# nice documentation is available at https://keras.io/

# Step 1
# import classes and functions 
import numpy							#package for scientific computing, support for large array etc.
from keras.datasets import mnist	 	#dataset
from keras.models import Sequential		#model
from keras.layers import Dense			#layer
from keras.layers import Dropout		#layer
from keras.utils import np_utils		#for transforming data 
import matplotlib.pyplot as plt			#to plot images

# Step 2:…</description>
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