A perceptron is:
a) a single layer feed-forward neural network with pre-processing
b) an auto-associative neural network
c) a double layer auto-associative neural network
d) a neural network that contains feedback
. Why are linearly separable problems of interest of neural network researchers? a) Because they are the only class of problem that network can solve successfully b) Because they are the only class of problem that Perceptron can solve successfully c) Because they are the only mathematical functions that are continue d) Because they are the only mathematical functions you can draw
What can you do with an nn and what not?
How neural networks became a universal function approximators?
What are the population, sample, training set, design set, validation set, and test set?
Which is true for neural networks? a) It has set of nodes and connections b) Each node computes it’s weighted input c) Node could be in excited state or non-excited state d) All of the mentioned
The network that involves backward links from output to the input and hidden layers is called as ____. a) Self organizing maps b) Perceptrons c) Recurrent neural network d) Multi layered perceptron
What is the role of activation functions in a Neural Network?
How human brain works?
What are batch, incremental, on-line, off-line, deterministic, stochastic, adaptive, instantaneous, pattern, constructive, and sequential learning?
What is a Neural Network?
Explain Generative Adversarial Network.
What learning rate should be used for backprop?
AI Algorithms (74)
AI Natural Language Processing (96)
AI Knowledge Representation Reasoning (12)
AI Robotics (183)
AI Computer Vision (13)
AI Neural Networks (66)
AI Fuzzy Logic (31)
AI Games (8)
AI Languages (141)
AI Tools (11)
AI Machine Learning (659)
Data Science (671)
Data Mining (120)
AI Deep Learning (111)
Generative AI (153)
AI Frameworks Libraries (197)
AI Ethics Safety (100)
AI Applications (427)
AI General (197)
AI AllOther (6)