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AmlanChakrabarti3

This lecture gives a good introduction to Deep Learning Concepts

Data & Analytics
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Prof. Amlan Chakrabarti IEEE Computer Soc. Dist. Vist. & ACM Dist. Speaker Vice President, Society for Data Science (S4DS) Director, A.K.Choudhury School of Information Technology University of Calcutta Webinar Organized by Dept. of Computer Science, Surendranath College, Kolkata 29th June 2020
INTRODUCTORY CONCEPTS
Machine Learning is Everywhere
Formal Definition • “Machine learning is the field of study which gives the computers the ability to learn without being explicitly programmed”- Arther Samuels 1959 • “A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.”- Tom Mitchells 1997 • Machine learning focuses on the development of computer programs that can access data and use it learn for themselves. • Example: To predict, traffic patterns at a busy intersection (task T) • We can run it through a machine learning algorithm with data about past traffic patterns (experience E) • If it has successfully “learned”, it will then do better at predicting future traffic patterns (performance measure P).
Defining The Learning Task
What is Machine Learning?
The Essence of Machine Learning • Example: Predicting how a viewer will rate a movie – 10% Improvement = 1 Million Dollar Prize • When does Machine Learning help? – A pattern exists – We cannot pin it down mathematically – We have data
MACHINE LEARNING STRATEGIES
Supervised Learning: Regression  There are a few concepts to unpack here: • Dependent Variable • Independent Variable(s) • Slope & Intercept • Error Function
Supervised Learning: Classification Binary Classification Multi-label Classification Multiclass Classification
Unsupervised Learning: Clustering • Finding groups of objects such that objects in a group are similar (or related) to one another and different from (or unrelated to) the objects in other groups Partitional Clustering Hierarchical Clustering
Applications of Clustering
ML in Practice
PERCEPTRON : STRUCTURAL BUILDING BLOCK OF DEEP LEARNING
Perceptron: Role of Weights & Bias Adjusting Weights Adjusting Bias
 CNN is a special type of feedforward neural network originally employed in the field of computer vision.  Its design is inspired by the human visual cortex.  The visual cortex contains a lot of cells that are responsible for detecting light in small and overlapping sub-regions of the visual fields, which are called receptive fields.  These cells act as local filters over the input space.  CNN consists of multiple convolutional layers, each of which performs the function that is processed by the cells in the visual cortex.
A toy ConvNet: X’s and O’s X or O CNN A two-dimensional array of pixels
For example CNN X CNN O
Trickier cases CNN X CNN O
Deciding is hard ?
-1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 What computers see ?
-1 -1 -1 -1 -1 -1 -1 -1 -1 -1 X -1 -1 -1 -1 X X -1 -1 X X -1 -1 X X -1 -1 -1 -1 X 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 X -1 -1 -1 -1 X X -1 -1 X X -1 -1 X X -1 -1 -1 -1 X -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 What computers see
-1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 Computers are literal x
ConvNets match pieces of the image = = =
1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 1 -1 1 -1 1 -1 -1 1 -1 1 -1 1 -1 1 -1 1 Features match pieces of the image
1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 1 -1 1 -1 1 -1 -1 1 -1 1 -1 1 -1 1 -1 1
1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 1 -1 1 -1 1 -1 -1 1 -1 1 -1 1 -1 1 -1 1
1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 1 -1 1 -1 1 -1 -1 1 -1 1 -1 1 -1 1 -1 1
1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 1 -1 1 -1 1 -1 -1 1 -1 1 -1 1 -1 1 -1 1
1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 1 -1 1 -1 1 -1 -1 1 -1 1 -1 1 -1 1 -1 1
1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 Filtering: The math behind the match
Filtering: The math behind the match
1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 Filtering: The math behind the match
1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 Filtering: The math behind the match
1 1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 Filtering: The math behind the match
1 1 1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 Filtering: The math behind the match
1 1 1 1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 Filtering: The math behind the match
1 1 1 1 1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 Filtering: The math behind the match
1 1 1 1 1 1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 Filtering: The math behind the match
1 1 1 1 1 1 1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 Filtering: The math behind the match
1 1 1 1 1 1 1 1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 Filtering: The math behind the match
1 1 1 1 1 1 1 1 1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 Filtering: The math behind the match
1 1 1 1 1 1 1 1 1 1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 Filtering: The math behind the match
1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 Filtering: The math behind the match
1 1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 Filtering: The math behind the match
1 1 -1 1 1 1 -1 1 1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 Filtering: The math behind the match
1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 1 -1 1 1 1 -1 1 1 Filtering: The math behind the match 55 1 1 -1 1 1 1 -1 1 1
1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 Convolution: Trying every possible match 0.77 -0.11 0.11 0.33 0.55 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11 0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55 0.33 0.33 -0.33 0.55 -0.33 0.33 0.33 0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11 -0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.55 0.33 0.11 -0.11 0.77
1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 Convolution: Trying every possible match = 0.77 -0.11 0.11 0.33 0.55 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11 0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55 0.33 0.33 -0.33 0.55 -0.33 0.33 0.33 0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11 -0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.55 0.33 0.11 -0.11 0.77 The size of the convoluted matrix is given by C=((I-F+2P)/S)+1, where C is the size of the Convoluted matrix, I is the size of the input matrix, F the size of the filter matrix and P the padding applied to the input matrix
1 -1 -1 -1 1 -1 -1 -1 1 0.33 -0.11 0.55 0.33 0.11 -0.11 0.77 -0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11 0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11 0.33 0.33 -0.33 0.55 -0.33 0.33 0.33 0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55 -0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11 0.77 -0.11 0.11 0.33 0.55 -0.11 0.33 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 = 0.77 -0.11 0.11 0.33 0.55 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11 0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55 0.33 0.33 -0.33 0.55 -0.33 0.33 0.33 0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11 -0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.55 0.33 0.11 -0.11 0.77 -1 -1 1 -1 1 -1 1 -1 -1 1 -1 1 -1 1 -1 1 -1 1 0.33 -0.55 0.11 -0.11 0.11 -0.55 0.33 -0.55 0.55 -0.55 0.33 -0.55 0.55 -0.55 0.11 -0.55 0.55 -0.77 0.55 -0.55 0.11 -0.11 0.33 -0.77 1.00 -0.77 0.33 -0.11 0.11 -0.55 0.55 -0.77 0.55 -0.55 0.11 -0.55 0.55 -0.55 0.33 -0.55 0.55 -0.55 0.33 -0.55 0.11 -0.11 0.11 -0.55 0.33 = = -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1
Convolution layer 0.33 -0.11 0.55 0.33 0.11 -0.11 0.77 -0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11 0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11 0.33 0.33 -0.33 0.55 -0.33 0.33 0.33 0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55 -0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11 0.77 -0.11 0.11 0.33 0.55 -0.11 0.33 1 -1 -1 -1 1 -1 -1 -1 1 0.77 -0.11 0.11 0.33 0.55 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11 0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55 0.33 0.33 -0.33 0.55 -0.33 0.33 0.33 0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11 -0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.55 0.33 0.11 -0.11 0.77 -1 -1 1 -1 1 -1 1 -1 -1 1 -1 1 -1 1 -1 1 -1 1 0.33 -0.55 0.11 -0.11 0.11 -0.55 0.33 -0.55 0.55 -0.55 0.33 -0.55 0.55 -0.55 0.11 -0.55 0.55 -0.77 0.55 -0.55 0.11 -0.11 0.33 -0.77 1.00 -0.77 0.33 -0.11 0.11 -0.55 0.55 -0.77 0.55 -0.55 0.11 -0.55 0.55 -0.55 0.33 -0.55 0.55 -0.55 0.33 -0.55 0.11 -0.11 0.11 -0.55 0.33 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1
Convolution layer 0.33 -0.11 0.55 0.33 0.11 -0.11 0.77 -0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11 0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11 0.33 0.33 -0.33 0.55 -0.33 0.33 0.33 0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55 -0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11 0.77 -0.11 0.11 0.33 0.55 -0.11 0.33 0.77 -0.11 0.11 0.33 0.55 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11 0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55 0.33 0.33 -0.33 0.55 -0.33 0.33 0.33 0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11 -0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.55 0.33 0.11 -0.11 0.77 0.33 -0.55 0.11 -0.11 0.11 -0.55 0.33 -0.55 0.55 -0.55 0.33 -0.55 0.55 -0.55 0.11 -0.55 0.55 -0.77 0.55 -0.55 0.11 -0.11 0.33 -0.77 1.00 -0.77 0.33 -0.11 0.11 -0.55 0.55 -0.77 0.55 -0.55 0.11 -0.55 0.55 -0.55 0.33 -0.55 0.55 -0.55 0.33 -0.55 0.11 -0.11 0.11 -0.55 0.33 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1
Pooling: Shrinking the image stack
1.00 Pooling 0.77 -0.11 0.11 0.33 0.55 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11 0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55 0.33 0.33 -0.33 0.55 -0.33 0.33 0.33 0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11 -0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.55 0.33 0.11 -0.11 0.77
1.00 0.33 Pooling 0.77 -0.11 0.11 0.33 0.55 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11 0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55 0.33 0.33 -0.33 0.55 -0.33 0.33 0.33 0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11 -0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.55 0.33 0.11 -0.11 0.77
1.00 0.33 0.55 Pooling 0.77 -0.11 0.11 0.33 0.55 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11 0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55 0.33 0.33 -0.33 0.55 -0.33 0.33 0.33 0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11 -0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.55 0.33 0.11 -0.11 0.77
1.00 0.33 0.55 0.33 Pooling 0.77 -0.11 0.11 0.33 0.55 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11 0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55 0.33 0.33 -0.33 0.55 -0.33 0.33 0.33 0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11 -0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.55 0.33 0.11 -0.11 0.77
1.00 0.33 0.55 0.33 0.33 Pooling 0.77 -0.11 0.11 0.33 0.55 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11 0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55 0.33 0.33 -0.33 0.55 -0.33 0.33 0.33 0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11 -0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.55 0.33 0.11 -0.11 0.77
1.00 0.33 0.55 0.33 0.33 1.00 0.33 0.55 0.55 0.33 1.00 0.11 0.33 0.55 0.11 0.77 Pooling 0.77 -0.11 0.11 0.33 0.55 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11 0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55 0.33 0.33 -0.33 0.55 -0.33 0.33 0.33 0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11 -0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.55 0.33 0.11 -0.11 0.77
1.00 0.33 0.55 0.33 0.33 1.00 0.33 0.55 0.55 0.33 1.00 0.11 0.33 0.55 0.11 0.77 0.33 -0.11 0.55 0.33 0.11 -0.11 0.77 -0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11 0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11 0.33 0.33 -0.33 0.55 -0.33 0.33 0.33 0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55 -0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11 0.77 -0.11 0.11 0.33 0.55 -0.11 0.33 0.77 -0.11 0.11 0.33 0.55 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11 0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55 0.33 0.33 -0.33 0.55 -0.33 0.33 0.33 0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11 -0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.55 0.33 0.11 -0.11 0.77 0.33 -0.55 0.11 -0.11 0.11 -0.55 0.33 -0.55 0.55 -0.55 0.33 -0.55 0.55 -0.55 0.11 -0.55 0.55 -0.77 0.55 -0.55 0.11 -0.11 0.33 -0.77 1.00 -0.77 0.33 -0.11 0.11 -0.55 0.55 -0.77 0.55 -0.55 0.11 -0.55 0.55 -0.55 0.33 -0.55 0.55 -0.55 0.33 -0.55 0.11 -0.11 0.11 -0.55 0.33 0.33 0.55 1.00 0.77 0.55 0.55 1.00 0.33 1.00 1.00 0.11 0.55 0.77 0.33 0.55 0.33 0.55 0.33 0.55 0.33 0.33 1.00 0.55 0.11 0.55 0.55 0.55 0.11 0.33 0.11 0.11 0.33
Pooling layer 1.00 0.33 0.55 0.33 0.33 1.00 0.33 0.55 0.55 0.33 1.00 0.11 0.33 0.55 0.11 0.77 0.33 -0.11 0.55 0.33 0.11 -0.11 0.77 -0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11 0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11 0.33 0.33 -0.33 0.55 -0.33 0.33 0.33 0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55 -0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11 0.77 -0.11 0.11 0.33 0.55 -0.11 0.33 0.77 -0.11 0.11 0.33 0.55 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11 0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55 0.33 0.33 -0.33 0.55 -0.33 0.33 0.33 0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11 -0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.55 0.33 0.11 -0.11 0.77 0.33 -0.55 0.11 -0.11 0.11 -0.55 0.33 -0.55 0.55 -0.55 0.33 -0.55 0.55 -0.55 0.11 -0.55 0.55 -0.77 0.55 -0.55 0.11 -0.11 0.33 -0.77 1.00 -0.77 0.33 -0.11 0.11 -0.55 0.55 -0.77 0.55 -0.55 0.11 -0.55 0.55 -0.55 0.33 -0.55 0.55 -0.55 0.33 -0.55 0.11 -0.11 0.11 -0.55 0.33 0.33 0.55 1.00 0.77 0.55 0.55 1.00 0.33 1.00 1.00 0.11 0.55 0.77 0.33 0.55 0.33 0.55 0.33 0.55 0.33 0.33 1.00 0.55 0.11 0.55 0.55 0.55 0.11 0.33 0.11 0.11 0.33
Normalization
Rectified Linear Units (ReLUs) 0.77 -0.11 0.11 0.33 0.55 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11 0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55 0.33 0.33 -0.33 0.55 -0.33 0.33 0.33 0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11 -0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.55 0.33 0.11 -0.11 0.77 0.77
0.77 0 Rectified Linear Units (ReLUs) 0.77 -0.11 0.11 0.33 0.55 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11 0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55 0.33 0.33 -0.33 0.55 -0.33 0.33 0.33 0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11 -0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.55 0.33 0.11 -0.11 0.77
0.77 0 0.11 0.33 0.55 0 0.33 Rectified Linear Units (ReLUs) 0.77 -0.11 0.11 0.33 0.55 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11 0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55 0.33 0.33 -0.33 0.55 -0.33 0.33 0.33 0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11 -0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.55 0.33 0.11 -0.11 0.77
0.77 0 0.11 0.33 0.55 0 0.33 0 1.00 0 0.33 0 0.11 0 0.11 0 1.00 0 0.11 0 0.55 0.33 0.33 0 0.55 0 0.33 0.33 0.55 0 0.11 0 1.00 0 0.11 0 0.11 0 0.33 0 1.00 0 0.33 0 0.55 0.33 0.11 0 0.77 Rectified Linear Units (ReLUs) 0.77 -0.11 0.11 0.33 0.55 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11 0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55 0.33 0.33 -0.33 0.55 -0.33 0.33 0.33 0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11 -0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.55 0.33 0.11 -0.11 0.77
ReLU layer 0.77 0 0.11 0.33 0.55 0 0.33 0 1.00 0 0.33 0 0.11 0 0.11 0 1.00 0 0.11 0 0.55 0.33 0.33 0 0.55 0 0.33 0.33 0.55 0 0.11 0 1.00 0 0.11 0 0.11 0 0.33 0 1.00 0 0.33 0 0.55 0.33 0.11 0 0.77 0.33 0 0.11 0 0.11 0 0.33 0 0.55 0 0.33 0 0.55 0 0.11 0 0.55 0 0.55 0 0.11 0 0.33 0 1.00 0 0.33 0 0.11 0 0.55 0 0.55 0 0.11 0 0.55 0 0.33 0 0.55 0 0.33 0 0.11 0 0.11 0 0.33 0.33 0 0.55 0.33 0.11 0 0.77 0 0.11 0 0.33 0 1.00 0 0.55 0 0.11 0 1.00 0 0.11 0.33 0.33 0 0.55 0 0.33 0.33 0.11 0 1.00 0 0.11 0 0.55 0 1.00 0 0.33 0 0.11 0 0.77 0 0.11 0.33 0.55 0 0.33 0.33 -0.11 0.55 0.33 0.11 -0.11 0.77 -0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11 0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11 0.33 0.33 -0.33 0.55 -0.33 0.33 0.33 0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55 -0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11 0.77 -0.11 0.11 0.33 0.55 -0.11 0.33 0.77 -0.11 0.11 0.33 0.55 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11 0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55 0.33 0.33 -0.33 0.55 -0.33 0.33 0.33 0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11 -0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.55 0.33 0.11 -0.11 0.77 0.33 -0.55 0.11 -0.11 0.11 -0.55 0.33 -0.55 0.55 -0.55 0.33 -0.55 0.55 -0.55 0.11 -0.55 0.55 -0.77 0.55 -0.55 0.11 -0.11 0.33 -0.77 1.00 -0.77 0.33 -0.11 0.11 -0.55 0.55 -0.77 0.55 -0.55 0.11 -0.55 0.55 -0.55 0.33 -0.55 0.55 -0.55 0.33 -0.55 0.11 -0.11 0.11 -0.55 0.33
Layers get stacked -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1.00 0.33 0.55 0.33 0.33 1.00 0.33 0.55 0.55 0.33 1.00 0.11 0.33 0.55 0.11 0.77 0.33 0.55 1.00 0.77 0.55 0.55 1.00 0.33 1.00 1.00 0.11 0.55 0.77 0.33 0.55 0.33 0.55 0.33 0.55 0.33 0.33 1.00 0.55 0.11 0.55 0.55 0.55 0.11 0.33 0.11 0.11 0.33
Deep stacking -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1.00 0.55 0.55 1.00 0.55 1.00 1.00 0.55 1.00 0.55 0.55 0.55
Fully connected layer 1.00 0.55 0.55 1.00 0.55 1.00 1.00 0.55 1.00 0.55 0.55 0.55 1.00 0.55 0.55 1.00 1.00 0.55 0.55 0.55 0.55 1.00 1.00 0.55
Fully connected layer X O 1.00 0.55 0.55 1.00 1.00 0.55 0.55 0.55 0.55 1.00 1.00 0.55
Fully connected layer X O 0.55 1.00 1.00 0.55 0.55 0.55 0.55 0.55 1.00 0.55 0.55 1.00
Fully connected layer X O 0.9 0.65 0.45 0.87 0.96 0.73 0.23 0.63 0.44 0.89 0.94 0.53
Fully connected layer X O 0.9 0.65 0.45 0.87 0.96 0.73 0.23 0.63 0.44 0.89 0.94 0.53
Fully connected layer X O 0.9 0.65 0.45 0.87 0.96 0.73 0.23 0.63 0.44 0.89 0.94 0.53
Fully connected layer X O 0.9 0.65 0.45 0.87 0.96 0.73 0.23 0.63 0.44 0.89 0.94 0.53
Fully connected layer X O 0.9 0.65 0.45 0.87 0.96 0.73 0.23 0.63 0.44 0.89 0.94 0.53
Fully connected layer X O 0.9 0.65 0.45 0.87 0.96 0.73 0.23 0.63 0.44 0.89 0.94 0.53
Fully connected layer X O 0.9 0.65 0.45 0.87 0.96 0.73 0.23 0.63 0.44 0.89 0.94 0.53
Fully connected layer X O 0.9 0.65 0.45 0.87 0.96 0.73 0.23 0.63 0.44 0.89 0.94 0.53
Putting It All Together
Hyperparameters (knobs)
Various forms
Deep Learning for CAD • Winner of various image segmentation tasks • Shows stable performance even with small annotated images 92 U-net O. Roneneberger et al. 2015
COVID-Net 93 Neural Network Design for Detection of COVID-19 Cases from Chest X-Ray Images COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-Ray Images Linda Wang, Zhong Qiu Lin, and Alexander Wong 13,975 CXR images across 13,870 patient cases https://github.com/lindawangg/COVID-Net
Not just images
Images
Sound
Text
ML and DL Research @ AKCSIT, CU  Computer Vision  Computer Aided Disease Detection and Diagnosis (UGC/UPE-II, TEQIP-III, Collaborators: Peerless Hospital, Park Clinic Hospital Kolkata, M.R. Bangur Institute of Neurosciences, SSKM Kolkata )  Video Analytics  Renewable Energy  Prediction and Forecasting of Power Generation (funded by USAID in collaboration with University of Colorado Boulder )  Text Analytics  Sentiment Analysis from Social cites, News etc.  Cybersecurity  Prevention of web phishing  Detection of fake news  Classification of toxic words in social media  Quantum Machine Learning (DST Indo-Japan Project, Iwate Prefecture University, Japan)  Quantum Inspired Machine Learning
DL_Lecture_29_06_2020.pptx
DL_Lecture_29_06_2020.pptx

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DL_Lecture_29_06_2020.pptx

  • 1. Prof. Amlan Chakrabarti IEEE Computer Soc. Dist. Vist. & ACM Dist. Speaker Vice President, Society for Data Science (S4DS) Director, A.K.Choudhury School of Information Technology University of Calcutta Webinar Organized by Dept. of Computer Science, Surendranath College, Kolkata 29th June 2020
  • 3. Machine Learning is Everywhere
  • 4. Formal Definition • “Machine learning is the field of study which gives the computers the ability to learn without being explicitly programmed”- Arther Samuels 1959 • “A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.”- Tom Mitchells 1997 • Machine learning focuses on the development of computer programs that can access data and use it learn for themselves. • Example: To predict, traffic patterns at a busy intersection (task T) • We can run it through a machine learning algorithm with data about past traffic patterns (experience E) • If it has successfully “learned”, it will then do better at predicting future traffic patterns (performance measure P).
  • 6. What is Machine Learning?
  • 7. The Essence of Machine Learning • Example: Predicting how a viewer will rate a movie – 10% Improvement = 1 Million Dollar Prize • When does Machine Learning help? – A pattern exists – We cannot pin it down mathematically – We have data
  • 9. Supervised Learning: Regression  There are a few concepts to unpack here: • Dependent Variable • Independent Variable(s) • Slope & Intercept • Error Function
  • 10. Supervised Learning: Classification Binary Classification Multi-label Classification Multiclass Classification
  • 11. Unsupervised Learning: Clustering • Finding groups of objects such that objects in a group are similar (or related) to one another and different from (or unrelated to) the objects in other groups Partitional Clustering Hierarchical Clustering
  • 14.
  • 15.
  • 16.
  • 17. PERCEPTRON : STRUCTURAL BUILDING BLOCK OF DEEP LEARNING
  • 18.
  • 19.
  • 20.
  • 21.
  • 22.
  • 23. Perceptron: Role of Weights & Bias Adjusting Weights Adjusting Bias
  • 24.  CNN is a special type of feedforward neural network originally employed in the field of computer vision.  Its design is inspired by the human visual cortex.  The visual cortex contains a lot of cells that are responsible for detecting light in small and overlapping sub-regions of the visual fields, which are called receptive fields.  These cells act as local filters over the input space.  CNN consists of multiple convolutional layers, each of which performs the function that is processed by the cells in the visual cortex.
  • 25. A toy ConvNet: X’s and O’s X or O CNN A two-dimensional array of pixels
  • 29. -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 What computers see ?
  • 30. -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 X -1 -1 -1 -1 X X -1 -1 X X -1 -1 X X -1 -1 -1 -1 X 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 X -1 -1 -1 -1 X X -1 -1 X X -1 -1 X X -1 -1 -1 -1 X -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 What computers see
  • 31. -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 Computers are literal x
  • 32. ConvNets match pieces of the image = = =
  • 33. 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 1 -1 1 -1 1 -1 -1 1 -1 1 -1 1 -1 1 -1 1 Features match pieces of the image
  • 34. 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 1 -1 1 -1 1 -1 -1 1 -1 1 -1 1 -1 1 -1 1
  • 35. 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 1 -1 1 -1 1 -1 -1 1 -1 1 -1 1 -1 1 -1 1
  • 36. 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 1 -1 1 -1 1 -1 -1 1 -1 1 -1 1 -1 1 -1 1
  • 37. 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 1 -1 1 -1 1 -1 -1 1 -1 1 -1 1 -1 1 -1 1
  • 38. 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 1 -1 1 -1 1 -1 -1 1 -1 1 -1 1 -1 1 -1 1
  • 39. 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 Filtering: The math behind the match
  • 40. Filtering: The math behind the match
  • 41. 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 Filtering: The math behind the match
  • 42. 1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 Filtering: The math behind the match
  • 43. 1 1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 Filtering: The math behind the match
  • 44. 1 1 1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 Filtering: The math behind the match
  • 45. 1 1 1 1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 Filtering: The math behind the match
  • 46. 1 1 1 1 1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 Filtering: The math behind the match
  • 47. 1 1 1 1 1 1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 Filtering: The math behind the match
  • 48. 1 1 1 1 1 1 1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 Filtering: The math behind the match
  • 49. 1 1 1 1 1 1 1 1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 Filtering: The math behind the match
  • 50. 1 1 1 1 1 1 1 1 1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 Filtering: The math behind the match
  • 51. 1 1 1 1 1 1 1 1 1 1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 Filtering: The math behind the match
  • 52. 1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 Filtering: The math behind the match
  • 53. 1 1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 Filtering: The math behind the match
  • 54. 1 1 -1 1 1 1 -1 1 1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 Filtering: The math behind the match
  • 55. 1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 1 -1 1 1 1 -1 1 1 Filtering: The math behind the match 55 1 1 -1 1 1 1 -1 1 1
  • 56. 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 Convolution: Trying every possible match 0.77 -0.11 0.11 0.33 0.55 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11 0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55 0.33 0.33 -0.33 0.55 -0.33 0.33 0.33 0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11 -0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.55 0.33 0.11 -0.11 0.77
  • 57. 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 Convolution: Trying every possible match = 0.77 -0.11 0.11 0.33 0.55 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11 0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55 0.33 0.33 -0.33 0.55 -0.33 0.33 0.33 0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11 -0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.55 0.33 0.11 -0.11 0.77 The size of the convoluted matrix is given by C=((I-F+2P)/S)+1, where C is the size of the Convoluted matrix, I is the size of the input matrix, F the size of the filter matrix and P the padding applied to the input matrix
  • 58. 1 -1 -1 -1 1 -1 -1 -1 1 0.33 -0.11 0.55 0.33 0.11 -0.11 0.77 -0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11 0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11 0.33 0.33 -0.33 0.55 -0.33 0.33 0.33 0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55 -0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11 0.77 -0.11 0.11 0.33 0.55 -0.11 0.33 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 = 0.77 -0.11 0.11 0.33 0.55 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11 0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55 0.33 0.33 -0.33 0.55 -0.33 0.33 0.33 0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11 -0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.55 0.33 0.11 -0.11 0.77 -1 -1 1 -1 1 -1 1 -1 -1 1 -1 1 -1 1 -1 1 -1 1 0.33 -0.55 0.11 -0.11 0.11 -0.55 0.33 -0.55 0.55 -0.55 0.33 -0.55 0.55 -0.55 0.11 -0.55 0.55 -0.77 0.55 -0.55 0.11 -0.11 0.33 -0.77 1.00 -0.77 0.33 -0.11 0.11 -0.55 0.55 -0.77 0.55 -0.55 0.11 -0.55 0.55 -0.55 0.33 -0.55 0.55 -0.55 0.33 -0.55 0.11 -0.11 0.11 -0.55 0.33 = = -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1
  • 59. Convolution layer 0.33 -0.11 0.55 0.33 0.11 -0.11 0.77 -0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11 0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11 0.33 0.33 -0.33 0.55 -0.33 0.33 0.33 0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55 -0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11 0.77 -0.11 0.11 0.33 0.55 -0.11 0.33 1 -1 -1 -1 1 -1 -1 -1 1 0.77 -0.11 0.11 0.33 0.55 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11 0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55 0.33 0.33 -0.33 0.55 -0.33 0.33 0.33 0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11 -0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.55 0.33 0.11 -0.11 0.77 -1 -1 1 -1 1 -1 1 -1 -1 1 -1 1 -1 1 -1 1 -1 1 0.33 -0.55 0.11 -0.11 0.11 -0.55 0.33 -0.55 0.55 -0.55 0.33 -0.55 0.55 -0.55 0.11 -0.55 0.55 -0.77 0.55 -0.55 0.11 -0.11 0.33 -0.77 1.00 -0.77 0.33 -0.11 0.11 -0.55 0.55 -0.77 0.55 -0.55 0.11 -0.55 0.55 -0.55 0.33 -0.55 0.55 -0.55 0.33 -0.55 0.11 -0.11 0.11 -0.55 0.33 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1
  • 60. Convolution layer 0.33 -0.11 0.55 0.33 0.11 -0.11 0.77 -0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11 0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11 0.33 0.33 -0.33 0.55 -0.33 0.33 0.33 0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55 -0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11 0.77 -0.11 0.11 0.33 0.55 -0.11 0.33 0.77 -0.11 0.11 0.33 0.55 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11 0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55 0.33 0.33 -0.33 0.55 -0.33 0.33 0.33 0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11 -0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.55 0.33 0.11 -0.11 0.77 0.33 -0.55 0.11 -0.11 0.11 -0.55 0.33 -0.55 0.55 -0.55 0.33 -0.55 0.55 -0.55 0.11 -0.55 0.55 -0.77 0.55 -0.55 0.11 -0.11 0.33 -0.77 1.00 -0.77 0.33 -0.11 0.11 -0.55 0.55 -0.77 0.55 -0.55 0.11 -0.55 0.55 -0.55 0.33 -0.55 0.55 -0.55 0.33 -0.55 0.11 -0.11 0.11 -0.55 0.33 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1
  • 61. Pooling: Shrinking the image stack
  • 62. 1.00 Pooling 0.77 -0.11 0.11 0.33 0.55 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11 0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55 0.33 0.33 -0.33 0.55 -0.33 0.33 0.33 0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11 -0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.55 0.33 0.11 -0.11 0.77
  • 63. 1.00 0.33 Pooling 0.77 -0.11 0.11 0.33 0.55 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11 0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55 0.33 0.33 -0.33 0.55 -0.33 0.33 0.33 0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11 -0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.55 0.33 0.11 -0.11 0.77
  • 64. 1.00 0.33 0.55 Pooling 0.77 -0.11 0.11 0.33 0.55 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11 0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55 0.33 0.33 -0.33 0.55 -0.33 0.33 0.33 0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11 -0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.55 0.33 0.11 -0.11 0.77
  • 65. 1.00 0.33 0.55 0.33 Pooling 0.77 -0.11 0.11 0.33 0.55 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11 0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55 0.33 0.33 -0.33 0.55 -0.33 0.33 0.33 0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11 -0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.55 0.33 0.11 -0.11 0.77
  • 66. 1.00 0.33 0.55 0.33 0.33 Pooling 0.77 -0.11 0.11 0.33 0.55 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11 0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55 0.33 0.33 -0.33 0.55 -0.33 0.33 0.33 0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11 -0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.55 0.33 0.11 -0.11 0.77
  • 67. 1.00 0.33 0.55 0.33 0.33 1.00 0.33 0.55 0.55 0.33 1.00 0.11 0.33 0.55 0.11 0.77 Pooling 0.77 -0.11 0.11 0.33 0.55 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11 0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55 0.33 0.33 -0.33 0.55 -0.33 0.33 0.33 0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11 -0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.55 0.33 0.11 -0.11 0.77
  • 68. 1.00 0.33 0.55 0.33 0.33 1.00 0.33 0.55 0.55 0.33 1.00 0.11 0.33 0.55 0.11 0.77 0.33 -0.11 0.55 0.33 0.11 -0.11 0.77 -0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11 0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11 0.33 0.33 -0.33 0.55 -0.33 0.33 0.33 0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55 -0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11 0.77 -0.11 0.11 0.33 0.55 -0.11 0.33 0.77 -0.11 0.11 0.33 0.55 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11 0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55 0.33 0.33 -0.33 0.55 -0.33 0.33 0.33 0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11 -0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.55 0.33 0.11 -0.11 0.77 0.33 -0.55 0.11 -0.11 0.11 -0.55 0.33 -0.55 0.55 -0.55 0.33 -0.55 0.55 -0.55 0.11 -0.55 0.55 -0.77 0.55 -0.55 0.11 -0.11 0.33 -0.77 1.00 -0.77 0.33 -0.11 0.11 -0.55 0.55 -0.77 0.55 -0.55 0.11 -0.55 0.55 -0.55 0.33 -0.55 0.55 -0.55 0.33 -0.55 0.11 -0.11 0.11 -0.55 0.33 0.33 0.55 1.00 0.77 0.55 0.55 1.00 0.33 1.00 1.00 0.11 0.55 0.77 0.33 0.55 0.33 0.55 0.33 0.55 0.33 0.33 1.00 0.55 0.11 0.55 0.55 0.55 0.11 0.33 0.11 0.11 0.33
  • 69. Pooling layer 1.00 0.33 0.55 0.33 0.33 1.00 0.33 0.55 0.55 0.33 1.00 0.11 0.33 0.55 0.11 0.77 0.33 -0.11 0.55 0.33 0.11 -0.11 0.77 -0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11 0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11 0.33 0.33 -0.33 0.55 -0.33 0.33 0.33 0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55 -0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11 0.77 -0.11 0.11 0.33 0.55 -0.11 0.33 0.77 -0.11 0.11 0.33 0.55 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11 0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55 0.33 0.33 -0.33 0.55 -0.33 0.33 0.33 0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11 -0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.55 0.33 0.11 -0.11 0.77 0.33 -0.55 0.11 -0.11 0.11 -0.55 0.33 -0.55 0.55 -0.55 0.33 -0.55 0.55 -0.55 0.11 -0.55 0.55 -0.77 0.55 -0.55 0.11 -0.11 0.33 -0.77 1.00 -0.77 0.33 -0.11 0.11 -0.55 0.55 -0.77 0.55 -0.55 0.11 -0.55 0.55 -0.55 0.33 -0.55 0.55 -0.55 0.33 -0.55 0.11 -0.11 0.11 -0.55 0.33 0.33 0.55 1.00 0.77 0.55 0.55 1.00 0.33 1.00 1.00 0.11 0.55 0.77 0.33 0.55 0.33 0.55 0.33 0.55 0.33 0.33 1.00 0.55 0.11 0.55 0.55 0.55 0.11 0.33 0.11 0.11 0.33
  • 71. Rectified Linear Units (ReLUs) 0.77 -0.11 0.11 0.33 0.55 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11 0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55 0.33 0.33 -0.33 0.55 -0.33 0.33 0.33 0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11 -0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.55 0.33 0.11 -0.11 0.77 0.77
  • 72. 0.77 0 Rectified Linear Units (ReLUs) 0.77 -0.11 0.11 0.33 0.55 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11 0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55 0.33 0.33 -0.33 0.55 -0.33 0.33 0.33 0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11 -0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.55 0.33 0.11 -0.11 0.77
  • 73. 0.77 0 0.11 0.33 0.55 0 0.33 Rectified Linear Units (ReLUs) 0.77 -0.11 0.11 0.33 0.55 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11 0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55 0.33 0.33 -0.33 0.55 -0.33 0.33 0.33 0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11 -0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.55 0.33 0.11 -0.11 0.77
  • 74. 0.77 0 0.11 0.33 0.55 0 0.33 0 1.00 0 0.33 0 0.11 0 0.11 0 1.00 0 0.11 0 0.55 0.33 0.33 0 0.55 0 0.33 0.33 0.55 0 0.11 0 1.00 0 0.11 0 0.11 0 0.33 0 1.00 0 0.33 0 0.55 0.33 0.11 0 0.77 Rectified Linear Units (ReLUs) 0.77 -0.11 0.11 0.33 0.55 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11 0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55 0.33 0.33 -0.33 0.55 -0.33 0.33 0.33 0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11 -0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.55 0.33 0.11 -0.11 0.77
  • 75. ReLU layer 0.77 0 0.11 0.33 0.55 0 0.33 0 1.00 0 0.33 0 0.11 0 0.11 0 1.00 0 0.11 0 0.55 0.33 0.33 0 0.55 0 0.33 0.33 0.55 0 0.11 0 1.00 0 0.11 0 0.11 0 0.33 0 1.00 0 0.33 0 0.55 0.33 0.11 0 0.77 0.33 0 0.11 0 0.11 0 0.33 0 0.55 0 0.33 0 0.55 0 0.11 0 0.55 0 0.55 0 0.11 0 0.33 0 1.00 0 0.33 0 0.11 0 0.55 0 0.55 0 0.11 0 0.55 0 0.33 0 0.55 0 0.33 0 0.11 0 0.11 0 0.33 0.33 0 0.55 0.33 0.11 0 0.77 0 0.11 0 0.33 0 1.00 0 0.55 0 0.11 0 1.00 0 0.11 0.33 0.33 0 0.55 0 0.33 0.33 0.11 0 1.00 0 0.11 0 0.55 0 1.00 0 0.33 0 0.11 0 0.77 0 0.11 0.33 0.55 0 0.33 0.33 -0.11 0.55 0.33 0.11 -0.11 0.77 -0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11 0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11 0.33 0.33 -0.33 0.55 -0.33 0.33 0.33 0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55 -0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11 0.77 -0.11 0.11 0.33 0.55 -0.11 0.33 0.77 -0.11 0.11 0.33 0.55 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11 0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55 0.33 0.33 -0.33 0.55 -0.33 0.33 0.33 0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11 -0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.55 0.33 0.11 -0.11 0.77 0.33 -0.55 0.11 -0.11 0.11 -0.55 0.33 -0.55 0.55 -0.55 0.33 -0.55 0.55 -0.55 0.11 -0.55 0.55 -0.77 0.55 -0.55 0.11 -0.11 0.33 -0.77 1.00 -0.77 0.33 -0.11 0.11 -0.55 0.55 -0.77 0.55 -0.55 0.11 -0.55 0.55 -0.55 0.33 -0.55 0.55 -0.55 0.33 -0.55 0.11 -0.11 0.11 -0.55 0.33
  • 76. Layers get stacked -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1.00 0.33 0.55 0.33 0.33 1.00 0.33 0.55 0.55 0.33 1.00 0.11 0.33 0.55 0.11 0.77 0.33 0.55 1.00 0.77 0.55 0.55 1.00 0.33 1.00 1.00 0.11 0.55 0.77 0.33 0.55 0.33 0.55 0.33 0.55 0.33 0.33 1.00 0.55 0.11 0.55 0.55 0.55 0.11 0.33 0.11 0.11 0.33
  • 77. Deep stacking -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1.00 0.55 0.55 1.00 0.55 1.00 1.00 0.55 1.00 0.55 0.55 0.55
  • 78. Fully connected layer 1.00 0.55 0.55 1.00 0.55 1.00 1.00 0.55 1.00 0.55 0.55 0.55 1.00 0.55 0.55 1.00 1.00 0.55 0.55 0.55 0.55 1.00 1.00 0.55
  • 89. Putting It All Together
  • 92. Deep Learning for CAD • Winner of various image segmentation tasks • Shows stable performance even with small annotated images 92 U-net O. Roneneberger et al. 2015
  • 93. COVID-Net 93 Neural Network Design for Detection of COVID-19 Cases from Chest X-Ray Images COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-Ray Images Linda Wang, Zhong Qiu Lin, and Alexander Wong 13,975 CXR images across 13,870 patient cases https://github.com/lindawangg/COVID-Net
  • 96. Sound
  • 97. Text
  • 98. ML and DL Research @ AKCSIT, CU  Computer Vision  Computer Aided Disease Detection and Diagnosis (UGC/UPE-II, TEQIP-III, Collaborators: Peerless Hospital, Park Clinic Hospital Kolkata, M.R. Bangur Institute of Neurosciences, SSKM Kolkata )  Video Analytics  Renewable Energy  Prediction and Forecasting of Power Generation (funded by USAID in collaboration with University of Colorado Boulder )  Text Analytics  Sentiment Analysis from Social cites, News etc.  Cybersecurity  Prevention of web phishing  Detection of fake news  Classification of toxic words in social media  Quantum Machine Learning (DST Indo-Japan Project, Iwate Prefecture University, Japan)  Quantum Inspired Machine Learning