This document discusses neural networks and single layer perceptrons. It provides an overview of biological neurons versus artificial neurons in neural networks. It then explains the computational steps for training a single layer perceptron, including calculating the activation, using an activation function, and updating weights with gradient descent. The document demonstrates implementing a perceptron model in Python to classify data from the Sonar dataset. It evaluates the model using metrics like a confusion matrix and accuracy.

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Basic Concepts in Neural Networks: Single Layer Perceptron in Python
Presentation · June 2018
DOI: 10.13140/RG.2.2.13053.87522
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Tahmina Zebin
The University of Manchester
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2. Neural Networks
Basic Concepts and Implementation
Presented by:
Tahmina Zebin
Tahmina.zebin@Manchester.ac.uk
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3. WHAT ARE NEURAL NETWORKS?
 A family of Machine Learning techniques inspired by the construction of the human
brain.
 Neural Network approaches are useful for extracting patterns from images, video,
speech and time series data.
Some day to day application of Neural Networks you probably have encountered:
Recurrent Neural Networks(Automatic Text suggestion)
Convolutional Neural networks (Image labeler/Handwritten digit recognizer)
 These models can deal with large amounts of data.
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4. TODAY’S FOCUS
 Biological neuron vs Artificial neuron
 A single layer perceptron
 Computational steps for training a Perceptron
 Implementation of a perceptron model in Python
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5. From Wikipedia
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BIOLOGICAL NEURON VS THE
ARTIFICIAL NEURON
SINGLE LAYER PERCEPTRON
BIOLOGICAL NEURON

6. PERCEPTRON ALGORITHM
The Perceptron receives input signals from examples of training data
that we weight and combine in a linear equation called the activation:
activation = sum(weight_i * x_i) + bias
The activation is then transformed into an output value or prediction
using a transfer function, such as the step transfer function.
prediction = 1.0 if activation >= 0.0 else 0.0
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Activation Functions
ReLU

7. TRAINING A PERCEPTRON WITH THE DATA
Gradient Descent is the
process of minimizing a
function by following the
gradients of the cost function.
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We can estimate the weight values for our training data using stochastic
gradient descent.
W(t+1) = w(t) + learning_rate * (predicted - actual) * x(t)
Stochastic gradient descent requires
two parameters:
Learning Rate
Epochs
Cost function/loss function

8. COMPUTATIONAL STEPS
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9. LET’S MAKE PREDICTIONS FOR A BINARY
CLASSIFICATION PROBLEM.
• Modeling the Sonar Dataset.
• A data set with 207 observations and 61 variable columns.
• The first 60 column of the dataset represent the energy within a particular
frequency band, integrated over a certain period of time.
• The last column contains the class labels.
• There are two classes 0 if the object is a rock, and 1 if the object is a mine (metal
cylinder).
These steps will give us the foundation to implement and
apply the Perceptron algorithm to your own classification
predictive modeling problems.
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10. IT’S DEMO TIME..
Spyder IDE (Python 3.6)
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11. PRESENTING RESULTS FROM A NEURAL
NETWORK
Confusion Matrix
Accuracy
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12. REVIEW
In this tutorial, we discovered how to implement the Perceptron algorithm using stochastic gradient
descent with Python keras and sklearn library.
We Learned:
How to make predictions for a binary classification problem.
How to apply the technique to a real classification predictive modeling problem.
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13. QUIZ
How many divisions on the dataset we have created in the example?
Training set(80%), Test Set(20%)
What was the encoding technique we used to convert the categorical
output labels?
LabelEncoder()
Can any of you recall some of the python libraries we used in our code?
Numpy- Numerical toolkits
Pandas – Data Analysis toolkits
Sklearn – Machine learning toolkit in python
Keras- A neural network API with tensorfow/theano backend
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14. THANKS FOR YOUR TIME 
QUESTIONS?
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