Neural Networks and Fuzzy Systems

1. M. Raihan
Email: rianku11@gmail.com

2. Perceptron

3. Limitations of the Perceptron
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4. Perceptron Limitations
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Multi-layer perceptron can solve this problem
More than one layer of perceptrons (with a hard-
limiting activation function) can learn any Boolean
function
However, a learning algorithm for multi-layer
perceptrons has not been developed until much later
Back propagation algorithm (replacing the hard-limiter with a sigmoid activation
function)
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5. Perceptron Limitations
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6. Decision Boundaries
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7. Decision Boundaries
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8. Decision Boundaries
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 In general, a pattern classifier carves up (partitions) the feature
space into volumes called decision regions.
All feature vectors in a decision region are assigned to the same
category.
The decision regions are often simply connected, but they can be
multiply connected as well, consisting of two or more non-touching
regions.
In pattern recognition and machine learning, a feature vector is an n-
dimensional vector of numerical features that represent some object.
Many algorithms in machine learning require a numerical
representation of objects, since such representations facilitate
processing and statistical analysis. 8

9. The Multi-Layer Perceptron
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10. The Multi-Layer Perceptron
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A multilayer perceptron (MLP) is a feed-forward artificial
neural network that generates a set of outputs from a set of
inputs.
An MLP is characterized by several layers of input nodes
connected as a directed graph between the input and
output layers.
MLP uses backpropogation for training the network.
MLP is a deep learning method.
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11. MLP Decision Boundary – Nonlinear
Problems, Solved!
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12. Machine Learning
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What is Learning?
Learning takes place as a result of interaction between an
agent and the world.
The idea behind learning is that
Percepts received by an agent should be used not only for
acting, but also for improving the agent’s ability to behave
optimally in the future to achieve the goal.
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13. Why “Learn” ?
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Learning is used when:
Human expertise does not exist (navigating on Mars)
Humans are unable to explain their expertise(speech
recognition)
Solution changes in time (routing on a computer network)
Solution needs to be adapted to particular cases (user
biometrics)
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14. Things You Might Be Interested In
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15. Things You Might Be Interested In
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16. Learning from Data
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The world is driven by data.
Germany’s climate research centre generates 10 petabytes per year
Google processes 24 petabytes per day
The Large Hadron Collider produces 60 gigabytes per minute (~12 DVDs)
There are over 50m credit card transactions a day in the US alone.
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17. If Data Had mass, The Earth Would Be
A Black Hole
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Around the world, computers capture and store
terabytes of data everyday.
Science has also taken advantage of the ability of
computers to store massive amount of data.
The size and complexity of these data sets means that
humans are unable to extract useful information from
them.
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18. Thank You
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