CNNs can be used for image classification by using trainable convolutional and pooling layers to extract features from images, followed by dense layers for classification. CNNs were made practical by increased computational power and large datasets. Libraries like Keras make it easy to build and train CNNs. Example projects include sentiment analysis, customer conversion analysis, and inventory management using computer vision and natural language processing with CNNs.

1. image classification using cnn
[no math version]
@debarko
Practo

2. whoami
Debarko De
Practo
Talk : twitter/debarko
Code : github/debarko
Practo : dd@practo.comwhat to expect
Why use CNN and not regular image processing
How to easily build one for your tasks
How you can implement
This is NOT a tutorial for any of the libraries involved
Where to study more?

3. Agenda
Features
Problem statement & Impact
Trainable Feature Extractors
What is a CNN
Transfer Learning
Libraries
Projects
Fanciness
References

11. Problem Statement & Impact

12. Hand Crafted
Feature Extractor
Simple classifiers
Trainable Feature
Extractor
Trainable
Classifiers

13. CNN

14. if x input
then y output

15. x * W = y’

16. W is
kernel/filter

17. y != y’

18. E = f(Y,Y’)

19. CNN

20. Convolution Layer
(Dot Product)
1 0 1
0 1 0
1 0 1
Kernel

21. Pooling Layer
(Max, Mean, Avg)

22. Gradient
Descent

23. Why now?
2006 DeepNet paper [Link]
Computational power
Libraries
Lot of data → Imagenet
GPU Power

27. # Create first network with Keras
from keras.models import Sequential
from keras.layers import Dense
import numpy

28. # Create first network with Keras
from keras.models import Sequential
from keras.layers import Dense
import numpy
# fix random seed for reproducibility
seed = 7
numpy.random.seed(seed)

29. # Create first network with Keras
from keras.models import Sequential
from keras.layers import Dense
import numpy
# fix random seed for reproducibility
seed = 7
numpy.random.seed(seed)
# load pima indians dataset
dataset = numpy.loadtxt("pima-indians-diabetes.csv", delimiter=",")

30. # Create first network with Keras
from keras.models import Sequential
from keras.layers import Dense
import numpy
# fix random seed for reproducibility
seed = 7
numpy.random.seed(seed)
# load pima indians dataset
dataset = numpy.loadtxt("pima-indians-diabetes.csv", delimiter=",")
# split into input (X) and output (Y) variables
X = dataset[:,0:8]
Y = dataset[:,8]

31. # create model
model = Sequential()

32. # create model
model = Sequential()
model.add(Dense(12, input_dim=8, init='uniform', activation='relu'))

33. # create model
model = Sequential()
model.add(Dense(12, input_dim=8, init='uniform', activation='relu'))
model.add(Dense(8, init='uniform', activation='relu'))

34. # create model
model = Sequential()
model.add(Dense(12, input_dim=8, init='uniform', activation='relu'))
model.add(Dense(8, init='uniform', activation='relu'))
model.add(Dense(1, init='uniform', activation='sigmoid'))

35. # create model
model = Sequential()
model.add(Dense(12, input_dim=8, init='uniform', activation='relu'))
model.add(Dense(8, init='uniform', activation='relu'))
model.add(Dense(1, init='uniform', activation='sigmoid'))
# Compile model
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])

36. # create model
model = Sequential()
model.add(Dense(12, input_dim=8, init='uniform', activation='relu'))
model.add(Dense(8, init='uniform', activation='relu'))
model.add(Dense(1, init='uniform', activation='sigmoid'))
# Compile model
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
# Fit the model
model.fit(X, Y, nb_epoch=150, batch_size=10)

37. # create model
model = Sequential()
model.add(Dense(12, input_dim=8, init='uniform', activation='relu'))
model.add(Dense(8, init='uniform', activation='relu'))
model.add(Dense(1, init='uniform', activation='sigmoid'))
# Compile model
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
# Fit the model
model.fit(X, Y, nb_epoch=150, batch_size=10)
# evaluate the model
scores = model.evaluate(X, Y)

38. # create model
model = Sequential()
model.add(Dense(12, input_dim=8, init='uniform', activation='relu'))
model.add(Dense(8, init='uniform', activation='relu'))
model.add(Dense(1, init='uniform', activation='sigmoid'))
# Compile model
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
# Fit the model
model.fit(X, Y, nb_epoch=150, batch_size=10)
# evaluate the model
scores = model.evaluate(X, Y)
print("%s: %.2f%%" % (model.metrics_names[1], scores[1]*100))

39. Entire Code

40. # Create first network with Keras
from keras.models import Sequential
from keras.layers import Dense
import numpy
# fix random seed for reproducibility
seed = 7
numpy.random.seed(seed)
# load pima indians dataset
dataset = numpy.loadtxt("pima-indians-diabetes.csv", delimiter=",")
# split into input (X) and output (Y) variables
X = dataset[:,0:8]
Y = dataset[:,8]
# create model
model = Sequential()
model.add(Dense(12, input_dim=8, init='uniform', activation='relu'))
model.add(Dense(8, init='uniform', activation='relu'))
model.add(Dense(1, init='uniform', activation='sigmoid'))
# Compile model
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
# Fit the model
model.fit(X, Y, nb_epoch=150, batch_size=10)
# evaluate the model
scores = model.evaluate(X, Y)
print("%s: %.2f%%" % (model.metrics_names[1], scores[1]*100))

41. VGGNet

42. Layer 1
Layer 2
Layer 3
Layer 4
Layer 5
Visual Geometry Group
ImageNet

44. Few projects that you guys can easily work on
Review sentiment analysis on Practo / Zomato / Flipkart
Customer Conversion Analysis based on behavioural data
Inventory Stocking based on Search queries w.r.t location
Auto analysis and tagging of Support calls
Transcribe audio based on audio in phone calls and create tickets automatically
Any data understanding which can be spread out in a visual format or any time
series data

51. References
Basic Upto Speed Slideshow
Basic Lingo Catchup video for CNN
CS231n → Defacto & Best Online Course Work for CNNs
CS231n Assignments → http://cs231n.github.io/
Follow
@karpathy @drfeifei
Book
Fundamentals of Deep Learning: Designing Next-Generation Machine Intelligence Algorithms 1st Edition by Nikhil Buduma

52. धन्यवा
द
twitter.com/debarko