Deep Learning and neural networks have been the most exciting breakthoughs happening in the field of Artificial Intelligence. Artificial neural networks are the fundamental blocks of any deep learning architecture and are so called as they are built to model human brains. Just like human brains, deep learning models can learn from real experience, adapt and apply. Neural networks are classified into several types, each with distinct personalities and learning models that tap into the workings of human brain to filter, remember, un-learn and re-learn.

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Introduction to
Neural Networks
Re-creating the powerful
human brain

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Instructors
Mousum Dutta
Chief Data Scientist,
Spotle.ai
Computer Science, IIT KGP
Kanishka Mandal
Deep Learning and ANN Specialist
Georgia Institute of Technology
Atlanta, USA

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The Human Brain Is Fascinating
The brain is the most complex
non-linear and parallel
computer we know:
‣ Consists of 100 billions of
nerve cells with trillions of
interconnections
‣ Consumes about 1/10th the
power of computers
‣ Is massively parallel while a
computer is largely serial
A pro tennis player can follow
the trajectory of a ball served
at a speed up to 160 mph.,
swing her arm and return the
ball in her opponent’s court
within micro-seconds. Thanks
to the parallel computing
power of the human nervous
system

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But the machines are CATCHING UP:
• The brain responds in 10-3 seconds while the CPU responds in 10-9 seconds.
• The human brain can process less than 1000 basic operations per second while a computer
can process 10 billion basic operations per second
• Precision – 1 in 4.2 billion for a 32 bit processor vs 1 in 100 for human brain
Ref: 1. von Neumann, J. The Computer and the Brain (Yale University Press, New Haven, CT, 2012), 3rd ed.
Ref: 2. Patterson, D.A. & Hennessy, J.L. Computer Organization and Design (Elsevier, Amsterdam, 2012), 4th ed.
But The Machines Are Catching Up

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Where The Machine Wins Where Machines Can Match In Near
Future
Where The Brain Wins
Information Processing
Memory Persistence
Routine Operations
Vision
Language Processing
Complex maneuvres (Driverless Cars)
Structured problem solving
Goal-setting
Creativity
Consciousness
Intuition – Unlearned responses
Empathy and emotions
The Brains Vs The Machine

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# Neural networks are models of the brain and
nervous system
# Highly parallel
‣ Process information more like the brain than a
serial computer
# Capable of learning – supervised and
# unsupervised
# Applications
‣ As powerful problem solvers
‣ In simulating complex human functions like
vision, language processing, complex
maneuvres
Artificial Neural Networks (ANN) -
The Machine Gets A Brain

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Confidential
Feed-forward Network
Convolutional Neural Network
Self-organising Neural Network
Recurrent Neural Networks or LSTM (Long Short Term
Memory)
Modular Neural Network
Radial Basis Neural Network
An Artificial Neural Network (ANN) is an information processing construct that is modelled on
the brain. It is made up of layers of inter-connected processing elements (neurones) that
solve specific problems. There are broad types of ANN listed below:
Artificial Neural Networks – Formal
Definition And Types

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• History traces back to the 50’s but became popular in the 80’s with work by
Rumelhart, Hinton, and Mclelland
‣ A General Framework for Parallel Distributed Processing in Parallel
Distributed Processing: Explorations in the Microstructure of Cognition
• Peaked in the 90’s. Today:
‣ Hundreds of variants
‣ Less a model of the actual brain than a useful tool, but still some debate
• Numerous applications:
‣ Handwriting, face, speech recognition
‣ Vehicles that drive themselves
‣ Models of reading, sentence production, dreaming
• Debate for philosophers and cognitive scientists
‣ Can human consciousness or cognitive abilities be explained by a
connectionist model or does it require the manipulation of symbols?
Neural Network History

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Biological Neural Networks And Art-
Loving Pigeons
● Pigeons as art experts (Watanabe et al. 1995)
● Experiment:
● Pigeon in Skinner box
● Present paintings of two different artists (e.g.
Chagall / Van Gogh)
● Reward for pecking when presented a
particular artist (e.g. Van Gogh)
● Pigeons were able to discriminate between Van
Gogh and Chagall with 95% accuracy (when
presented with pictures they had been trained on)
● Discrimination still 85% successful for unseen
paintings of the artists

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ANNs incorporate the two fundamental components of biological neural
networks:
1. Neurones (nodes)
2. Synapses (weights)
Artificial Neural Networks – The Basics

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Information flow is unidirectional
Data is presented to Input layer
Passed on to Hidden Layer
Passed on to Output layer
Information is distributed
Information processing is parallel
Feed-forward Network – The
Simplest Form Of Neural Network
Internal representation (interpretation) of data

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Certificate
Masterclass

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Certificate Masterclass In Deep Learning
And Neural Network Fundamentals
✓ Understand the concept and architecture of deep learning and neural
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✓ Build your own image recognition project using neural networks
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Neural Network applications

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15 Interactive Videos with
illustrative examples
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5 Hours Of Lecture
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using neural networks
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Learn Deep Learning and Neural Networks With Spotle: https://spotle.ai/learn/neural-networks

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