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7. What is a Training Dataset?
Ans. A collection of data provided to a machine learning model to help it analyse and learn patterns is called training data.
8. Name two types of learning-based approaches.
Ans. The two types of learning based approaches are: Machine Learning and Deep Learning.
B. Long answer type questions.
1. Differentiate between Machine learning and Deep learning.
Ans. The difference between ML and DL are as follows:
Parameters Machine Learning Deep Learning
Machine Learning algorithm can easily work with When the size of the data is small, a Deep
Data smaller data set. Learning algorithm does not perform well as a
Dependency deep learning algorithm needs large amounts
of data to understand perfectly.
Machine Learning algorithms can work on low
Hardware Deep Learning algorithms are heavily dependent
end machines as well.
Dependency on high-end machines.
When we are solving a problem using a Deep Learning algorithm solves the problem
traditional machine learning algorithm it is end to end.
Problem generally recommended that we first break down
Solving the problem into different sub parts and solve
Approach them individually and then finally combine them
to get the desired result.
Machine Learning algorithms take much less Usually, Deep Learning algorithms take a
time to train. long time to train because there are many
Execution Time parameters making the training time longer
than usual.
2. How does Neural Networks work?
Ans. Neural Networks are made up of layers of neurons, just like the human brain that consists of millions of neurons. These
neurons are the core processing units of the network. A Neural Network is divided into multiple layers. Each layer in
Neural Network is further divided into several blocks called nodes. Each node has its own task to accomplish which is
then passed to the next layer. These layers with their working are as follows:
INPUT HIDDEN OUTPUT
LAYER LAYERS LAYER
a. Input Layer: The input layer is the first layer of a Neural Network. Its job is to receive raw data from the outside
world and pass it into the network. No processing happens at this layer; it simply acts as a gateway for the data to
enter the system.
Advanced Concepts of Modeling in AI 209

