WorksheetsNeural Network Basics
Total questions: 30
Worksheet time: 15mins
_________ type of model, the algorithm learns from a dataset which is labelled, and the algorithm uses the answer keys to evaluate its accuracy on the training data.
Supervised learning
UnSupervised learning
Reinforcement learning
•In this type of model, the algorithm learns and makes sense by extracting features/patterns from the unlabelled dataset provided (The system will evaluate by itself)
Unsupervised learning
supervised learning
Reinforcement learning
Information flows from
Input layer -> Hidden Layer-> Output layer
Feedforward Network
Backpropagation
Every Artificial Neural must have at least ______
two layers.
three layers.
four layers.
The neurons in the human brain regularly change their threshold.
True
False
The current Artificial Neural Networks are part of _____.
Weak Al systems.
Strong Al systems.
Future Al systems.
How many output layers are required for constructing an Artificial Neural Network?
1
3
2
Conventional Computing is deterministic in nature while Neural Computing is Probabilistic in nature.
True
False
Backpropagation calculates the ____ and propagates it back to earlier layers.
value
error
data
The neurons in the human brain regularly change their threshold.
True
False
Neural network is inspired by .......
Human heart
Human lungs
Human brain
human eyes
Neural Network is type of ........... which is type of machine learning
natural language processing
deep learning
speech
robotics
What is this?
artificial intelligence
algorithm
neural network
How many output layers have the following Neural network?
1
2
3
4
How many hidden layers have the following Neural network
4
5
6
7
What is ANN
Artificial Neural Node
Artificial Neuro Nest
Artificial Neural Network
Artificial Neuron Naive
The input data travels over the network (propagates) until it reaches the output layer. This is called........
Back propagation
Forward propagation
In the neural network shown,
each neuron is connected to a random selection of other neurons in the network
every neuron is connected to every other neuron in the network
every neuron is conected to every neuron in the next layer
each neuron is connected to a random selection of neurons in the next layer
Which of the following is the feature of a neural network?
Extract information without any programming
All of these
Learn itself and produced the trained data
Provide the filtered information without input
The neurons in the human brain regularly change their threshold.
True
False
It is a subset of Artificial Intelligence which enables machines to improve at tasks with experience (data).
Machine Learning
AI
Deep Learning
computational model inspired by the structure and functions of the biological neural network
artificial neural network (ANN)
neuron
weight
bias
Why do we need biological neural networks?
to solve tasks like machine vision & natural language processing
to apply heuristic search methods to find solutions of problem
to make smart human interactive & user friendly system
all of the mentioned
What are dendrites?
fibers of nerves
nuclear projections
other name for nucleus
none of the mentioned
What is purpose of Axon?
receptors
transmitter
transmission
none of the mentioned
Each layer in NN is connected to the next layer through:
bias
neuron
weight
activation function
What are the minimum number of layers we can have in a NN
Four
Five
Three
Two
In Single input Single output network the number of neurons are:
3
4
2
5
