Please answer the question based on the picture below 17Paragraph●17Styles2- Answer the following questions based on your findings above.● What type of problem can a single neuron (0 hidden layers) learn to solve?When are hidden layers needed? → C A Not secure playground.tensorflow.org/#activation=tanh&batchSize=10&dataset=gauss®Dataset=reg-plane&learning Rate=0.03®ulari...3DATAWhich dataset doyou want to use?DARatio of training totest data: 50%Noise: 0Batch size: 10REGENERATE--▶IFEATURESWhich propertiesdo you want tofeed in?Epoch000,022X₂X₂X₁²X₂²X,X₂sin(X₁)Q Searchsin(X₂)Learning rate0.03Y+ --ActivationTanhYRegularizationNone0 HIDDEN LAYERS▾Regularization rate0OUTPUTTest loss 0.002Training loss 0.0015 4 3 2Y3 -2 -1Colors showsdata, neuron andweight values.-1Problem typeClassification*0532-0-1-2-60 1 2 3 4 5 6

A dataset is a collection of organized and related data. It can take many forms, such as a…

Answered: 2- Answer the following questions based…

2- Answer the following questions based on your findings above. ● What type of problem can a single neuron (0 hidden layers) learn to solve? When are hidden layers needed?

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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Question

100%

Please answer the question based on the picture below

17
Paragraph
●
17
Styles
2- Answer the following questions based on your findings above.
● What type of problem can a single neuron (0 hidden layers) learn to solve?
When are hidden layers needed?

→ C A Not secure playground.tensorflow.org/#activation=tanh&batchSize=10&dataset=gauss&regDataset=reg-plane&learning Rate=0.03&regulari...
3
DATA
Which dataset do
you want to use?
DA
Ratio of training to
test data: 50%
Noise: 0
Batch size: 10
REGENERATE
--
▶I
FEATURES
Which properties
do you want to
feed in?
Epoch
000,022
X₂
X₂
X₁²
X₂²
X,X₂
sin(X₁)
Q Search
sin(X₂)
Learning rate
0.03
Y
+ -
-
Activation
Tanh
Y
Regularization
None
0 HIDDEN LAYERS
▾
Regularization rate
0
OUTPUT
Test loss 0.002
Training loss 0.001
5 4 3 2
Y
3 -2 -1
Colors shows
data, neuron and
weight values.
-1
Problem type
Classification
*
0
5
3
2
-0
-1
-2
-6
0 1 2 3 4 5 6

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