This lecture is about NEURAL NETWORKS WITH “R”. Neural networks form the basis of DL, and applications are enormous for DL, ranging from voice recognition to cancer detection. Moreover, the discussion is relating Machine learning is about training a model or an algorithm with data and then using the model to predict any new data.

1. NEURAL NETWORKS WITH “R”
Lecture 14
Dr. S.M. Ali Tirmizi
Chapter No. 4 - 6
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BENEFITS OF DL USING NN
 Neural networks form the basis of DL, and
applications are enormous for DL, ranging from
voice recognition to cancer detection.
 The pros (benefits) outweigh the cons and give
neural networks as the preferred modeling
technique for data science, machine learning,
and predictions.

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BENEFITS OF DL USING NN
The following are some of the advantages of neural networks:
1. Neural networks are flexible and can be used for both
regression and classification problems.
2. Any data which can be made numeric can be used in the
model, as neural network is a mathematical model with
approximation functions.
3. Neural networks are good to model with nonlinear data with
large number of inputs; for example, images.
4. It is reliable in an approach of tasks involving many features.

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BENEFITS OF DL USING NN
5. It works by splitting the problem of
classification into a layered network of simpler
elements.
6. Once trained, the predictions are pretty fast.
7. Neural networks can be trained with any
number of inputs and layers.
8. Neural networks work best with more data
points.

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WHAT IS MACHINE LEARNING
 Machine learning is about training a model or an
algorithm with data and then using the model to
predict any new data.
 Machines too can be taught like toddlers to do a
task based on training.

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WHAT IS MACHINE LEARNING
 First, we feed enough data to tell the machine
what needs to be done on what circumstances.
 After the training, the machine can perform
automatically and can also learn to fine-tune
itself.
 This type of training the machine is called
machine learning.

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WHAT IS MACHINE LEARNING

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WHAT IS MACHINE LEARNING

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WHAT IS MACHINE LEARNING

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WHAT IS MACHINE LEARNING

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TRAINING AND TESTING THE MODEL
 Training and testing the model forms the basis for
further usage of the model for prediction in
predictive analytics.
 Given a dataset of 100 rows of data, which includes
the predictor and response variables, we split the
dataset into a convenient ratio (say 70:30) and
allocate 70 rows for training and 30 rows for testing.
 The rows are selected in random to reduce bias.

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TRAINING AND TESTING THE MODEL
 Once the training data is available, the data is fed to the
neural network to get the massive universal function in place.
 The training data determines the weights, biases, and
activation functions to be used to get to output from input.
 After training and testing, the model is said to be deployed,
where actual data is passed through the model to get the
prediction.
 For example, the use case may be determining a fraud
transaction or a home loan eligibility check based on various
input parameters.

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DEEP NEUTRAL NETWORK (DNNs)
 Deep learning is a class of machine learning
wherein learning happens on multiple levels of
neuron networks.
 The standard diagram depicting a DNN is shown
in the following figure:

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PERCEPTRON NN MODELING
 A perceptron is a simple classification function that
directly makes its prediction.
 The core of the functionality lives in the weights
and how we update the weights to the best possible
prediction of y.
 There is another type of perceptron called the multi-
class perceptron, which can classify many possible
labels for an animal, such as dog, cat, or bird.

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RECURRENT NEURAL NETWORK (RNN)
 RNN is where the connections between neurons
can form a cycle.
 Unlike feed-forward networks, RNNs can use
internal memory for their processing.
 RNNs are a class of ANNs that feature
connections between hidden layers that are
propagated through time in order to learn
sequences.

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CONVOLUTIONAL NEURAL NETWORK
(CNN)
 CNN are designed specifically for image
recognition and classification.
 CNNs have multiple layers of neural networks
that extract information from images and
determine the class they fall into.
 For example, a CNN can detect whether the
image is a cat or not if it is trained with a set of
images of cats.

17. END OF LECTURE 14
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