3. Supervised Learning vs Unsupervised Learning vs
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
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.
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.
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
Cell body: Processor
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
connections between the units. Generally each connection is defined by a
a propagation rule, which determines the effective input of a unit from its
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
• 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
• 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
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
• 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?
∑= X1+X2 + ….+Xm =y
. . . . . . . . . . .
19. How do ANNs work?
Not all inputs are equal
∑= X1w1+X2w2 + ….+Xmwm
. . . . . . . . . . .
. . . .
20. How do ANNs work?
The signal is not passed down to the
next neuron verbatim
. . . . . . . . . . .
. . . .
21. The output is a function of the input, that is
affected by the weights, and the transfer
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
24. The steps required to build an
ANN are as follows:
1. Gather data. Divide into training data and test data. The
needs to be further divided into training data and
2. Select the network architecture, such as Feedforward
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
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
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
28. Activation Functions
Used to calculate the output response of a
Sum of the weighted input signal is applied
with an activation to obtain the response.
Activation functions can be linear or non
• 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
Bias is like another weight. Its included by adding
a component x0=1 to the input vector X.
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
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
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
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
36. B. Linear Neural Network
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
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
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
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