1. Artificial Neural
Networks

2. Learning Paradigms
Supervised learning
Unsupervised learning
Reinforcement learning

3. Supervised Learning vs Unsupervised Learning vs
Reinforcement Learning
 Supervised Learning: In Supervised Learning input is
provided as a labelled dataset, a model can learn from it
to provide the result of the problem easily. The dataset is
split into training and test dataset for training and then
validating the model. The supervised learned model is
then used to generate predictions on previously unseen
unlabeled data that belongs to the category of data the
model was trained on.
 Types of Problems
 Supervised Learning deals with two types of problem-
classification problems and regression problems.

4. Classification problems
 This algorithm helps to predict a discrete value. It can be
thought, the input data as a member of a particular class
or group. For instance, taking up the photos of the fruit
dataset, each photo has been labelled as a mango, an
apple, etc. Here, the algorithm has to classify the new
images into any of these categories. Examples:
 Naive Bayes Classifier
 Support Vector Machines
 Logistic Regression

5. Regression problems
 These problems are used for continuous data. For
example, predicting the price land in a city, given
the area, location, number of rooms, etc. And then
the input is sent to the machine for calculating the
price of the land according to previous examples.
Examples-
 Linear Regression
 Nonlinear Regression
 Bayesian Linear Regression

6. Unsupervised Learning
 This learning algorithm is completely opposite to
Supervised Learning. In short, there is no complete
and clean labelled dataset in unsupervised learning.
Unsupervised learning is self-organized learning. Its
main aim is to explore the underlying patterns and
predicts the output. Here we basically provide the
machine with data and ask to look for hidden features
and cluster the data in a way that makes sense.
Example
 K – Means clustering
 Principal Component Analysis

7. Reinforcement Learning
 It is neither based on supervised learning nor
unsupervised learning. Moreover, here the algorithms
learn to react to an environment on their own. It is rapidly
growing and moreover producing a variety of learning
algorithms. These algorithms are useful in the field of
Robotics, Gaming etc.

8. Artificial Neural Network
 An artificial neural network consists of a pool of simple
processing units which communicate by sending signals to
each other over a large number of weighted connections.

9. How do our brains work?
 A processing element
Dendrites: Input
Cell body: Processor
Synaptic: Link
Axon: Output

10. Artificial Neural Network
 A set of major aspects of a parallel distributed model include:
 a set of processing units (cells).
 a state of activation for every unit, which equivalent to the output of the
unit.
 connections between the units. Generally each connection is defined by a
weight.
 a propagation rule, which determines the effective input of a unit from its
external inputs.
 an activation function, which determines the new level of activation based
on the effective input and the current activation.
 an external input for each unit.
 a method for information gathering (the learning rule).
 an environment within which the system must operate, providing input
signals and _ if necessary _ error signals.

11. Why Artificial Neural Networks?
There are two basic reasons why we are interested in
building artificial neural networks (ANNs):
• Technical viewpoint: Some problems such as
character recognition or the prediction of future
states of a system require massively parallel and
adaptive processing.
• Biological viewpoint: ANNs can be used to
replicate and simulate components of the human
(or animal) brain, thereby giving us insight into
natural information processing.

12. Artificial Neural Networks
• The “building blocks” of neural networks are the
neurons.
• In technical systems, we also refer to them as units or nodes.
• Basically, each neuron
 receives input from many other neurons.
 changes its internal state (activation) based on the current
input.
 sends one output signal to many other neurons, possibly
including its input neurons (recurrent network).

13. Artificial Neural Networks
• Information is transmitted as a series of electric
impulses, so-called spikes.
• The frequency and phase of these spikes encodes the
information.
• In biological systems, one neuron can be connected to as
many as 10,000 other neurons.
• Usually, a neuron receives its information from other
neurons in a confined area, its so-called receptive field.

14. How do ANNs work?
 An artificial neural network (ANN) is either a hardware
implementation or a computer program which strives to
simulate the information processing capabilities of its biological
exemplar. ANNs are typically composed of a great number of
interconnected artificial neurons. The artificial neurons are
simplified models of their biological counterparts.
 ANN is a technique for solving problems by constructing software
that works like our brains.

15. How do our brains work?
 The Brain is A massively parallel information processing system.
 Our brains are a huge network of processing elements. A typical
brain contains a network of 10 billion neurons.

16. How do ANNs work?
An artificial neuron is an imitation of a human neuron

17. How do ANNs work?
• Now, let us have a look at the model of an artificial neuron.

18. How do ANNs work?
Output
x1
x2
xm
∑
y
Processing
Input
∑= X1+X2 + ….+Xm =y
. . . . . . . . . . .
.

19. How do ANNs work?
Not all inputs are equal
Output
x1
x2
xm
∑
y
Processing
Input
∑= X1w1+X2w2 + ….+Xmwm
=y
w1
w2
w
m
weights
. . . . . . . . . . .
.
. . . .
.

20. How do ANNs work?
The signal is not passed down to the
next neuron verbatim
Transfer Function
(Activation Function)
Output
x1
x2
xm
∑
y
Processing
Input
w1
w2
wm
weights
. . . . . . . . . . .
.
f(vk)
. . . .
.

21. The output is a function of the input, that is
affected by the weights, and the transfer
functions

22. Multi-layer neural network
 A fully connected multi-layer neural network is
called a Multilayer Perceptron (MLP). It has 3
layers including one hidden layer. If it has more
than 1 hidden layer, it is called a deep ANN. An
MLP is a typical example of a feedforward
artificial neural network

23. Multi-layer neural network

24. The steps required to build an
ANN are as follows:
 1. Gather data. Divide into training data and test data. The
training data
needs to be further divided into training data and
validation data.
 2. Select the network architecture, such as Feedforward
network.
 3. Select the algorithm, such as Multi-layer Perception.
 4. Set network parameters.
 5. Train the ANN with training data.
 6. Validate the model with validation data.
 7. Freeze the weights and other parameters.
 8. Test the trained network with test data.

25. Backpropagation in neural
network
 Backpropagation is a process involved in training
a neural network. It involves taking the error rate
of a forward propagation and feeding this loss
backward through the neural network layers to
fine-tune the weights. Backpropagation is the
essence of neural net training

26. Transfer Functions
 Linear: The output is proportional to the total
weighted input.
 Threshold: The output is set at one of two values,
depending on whether the total weighted input is
greater than or less than some threshold value.
 Non‐linear: The output varies continuously but not
linearly as the input changes.

27. Error Estimation
 The root mean square error (RMSE) is a frequently-
used measure of the differences between values
predicted by a model or an estimator and the values
actually observed from the thing being modeled or
estimated

28. Activation Functions
 Used to calculate the output response of a
neuron.
 Sum of the weighted input signal is applied
with an activation to obtain the response.
 Activation functions can be linear or non
linear

29. Weights
• Each neuron is connected to every other
neuron by means of directed links
• Links are associated with weights
• Weights contain information about the
input signal and is represented as a matrix
• Weight matrix also called connection
matrix

30. Bias
 Bias is like another weight. Its included by adding
a component x0=1 to the input vector X.
 X=(1,X1,X2…Xi,…Xn)
 Bias is of two types
 Positive bias: increase the net input
 Negative bias: decrease the net input

31. Why Bias is required?
 The relationship between input and output given by
the equation of straight line y=mx+c
X Y
Input
C(bias)
y=mx+C

32. Activation function
 The neuron is basically is a weighted average of
input, then this sum is passed through an
activation function to get an output.
 Y = ∑ (weights*input + bias)
 Here Y can be anything for a neuron between
range -infinity to +infinity. So, we have to bound
our output to get the desired prediction or
generalized results.
 Y = Activation function(∑ (weights*input + bias))

33. Types of Activation Functions
 We have divided all the essential neural networks in
three major parts:
 A. Binary step function
 B. Linear function
 C. Non linear activation function

34. A. Binary Step Neural Network
Activation Function
 1. Binary Step Function
 . It is basically a threshold base classifier, in this, we
decide some threshold value to decide output that
neuron should be activated or deactivated.
 f(x) = 1 if x > 0 else 0 if x < 0

35. Binary Step Function

36. B. Linear Neural Network
Activation Function
 It is a simple straight line activation function where
our function is directly proportional to the weighted
sum of neurons or input. Linear activation functions
are better in giving a wide range of activations and a
line of a positive slope may increase the firing rate as
the input rate increases.
 In binary, either a neuron is firing or not. If you know
gradient descent in deep learning then you would
notice that in this function derivative is constant.
Y = mx+c

37. C. Non Linear Neural Network
Activation Function
 3. ReLU( Rectified Linear unit) Activation function
 Rectified linear unit or ReLU is most widely used
activation function right now which ranges from 0 to
infinity, All the negative values are converted into
zero, and this conversion rate is so fast that neither it
can map nor fit into data properly which creates a
problem.
 It doesn't allow for the activation of all of the neurons
at the same time. i.e., if any input is negative, ReLU
converts it to zero and doesn't allow the neuron to get
activated. This means that only a few neurons are
activated, making the network easy for computation

39. Sigmoid Activation Function
 The sigmoid activation function is used mostly as
it does its task with great efficiency, it basically is
a probabilistic approach towards decision making
and ranges in between 0 to 1, so when we have to
make a decision or to predict an output we use
this activation function because of the range is the
minimum, therefore, prediction would be more
accurate.
 The equation for the binary sigmoid function is
f(x) = 1/(1+e(-x) )
 The equation for the bipolar sigmoid function is
f(x) = 2/(1+e(-x) )-1

41. Proposed ANN Model
41
Sentiment
score
Input layer Hidden layers Output layer
Predicted
demand
No. of review
views
bias
bias
bias
X1
X2
H1
Output
H2
H3
w1
w6

42. Neural network diagram shows final weights
and bias
42