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Recent questions and answers in Artificial Intelligence
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GATE DS&AI 2024 | Question: 13
Let $h_{1}$ and $h_{2}$ be two admissible heuristics used in $A^{*}$ search.Which ONE of the following expressions is always an admissible heuristic?$h_{1}+h_{2}$h_{1} ... 1} / h_{2},\left(h_{2} \neq 0\right)$\left|h_{1}-h_{2}\right|$
NarutoUzumaki
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NarutoUzumaki
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Feb 16
Artificial Intelligence
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Memory Based GATE DA 2024 | Question: 32
Consider two admissible heuristic functions, \(h_1\) and \(h_2\). Determine which of the following combinations are admissible:\(\frac{h_1}{h_2}\) \(\left(h_2 > 0\right)\) \\\(h_1 ... {h}_2\) \\\(\left| h_1 - h_2 \right|\) \\\(h_1 + h_2\)
GO Classes
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GO Classes
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Feb 4
Artificial Intelligence
gate2024-da-memory-based
goclasses
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Memory Based GATE DA 2024 | Question: 50
You are provided with three images, each depicting a different face of a six-sided dice. Based on these images, determine the correct option.
GO Classes
203
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GO Classes
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Feb 4
Artificial Intelligence
gate2024-da-memory-based
goclasses
artificial-intelligence
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2
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UPENN | ML | Cross validation
Suppose you have picked the parameter \( \theta \) for a model using 10-fold cross-validation. The best way to pick a final model to use and estimate its error ... the \( \theta \) you found; use the average CV error as its error estimate
squirrel69
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squirrel69
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Jan 31
Artificial Intelligence
machine-learning
artificial-intelligence
statistics
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What resources can i use to study the Data Warehousing part for the GATE DA paper?
Ameya Kulkarni
172
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Ameya Kulkarni
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Jan 30
725
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2
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DRDO CSE 2022 Paper 2 | Question: 31
What is the State $\mathrm{X}$ called for the following machine learning model?
kaptaan_11
725
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kaptaan_11
answered
Jan 27
Artificial Intelligence
drdocse-2022-paper2
artificial-intelligence
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DA Practice | UPENN | ML | Naive Bais
Suppose you have a three-class problem where class label \( y \in \{0, 1, 2\} \), and each training example \( \mathbf{X} \) has 3 binary attributes \( X_1, ... an example using the Naive Bayes classifier?(a) 5b) 9(c) 11(d) 13(e) 23
ruchit816
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ruchit816
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Jan 23
Artificial Intelligence
machine-learning
artificial-intelligence
statistics
probability
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1
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UPENN | ML Questions for GATE DA
In fitting some data using radial basis functions with kernel width $σ$, we compute training error of $345$ and a testing error of $390$.(a) increasing ... error(C) not enough information is provided to determine how $σ$ should be changed
ruchit816
361
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ruchit816
answered
Jan 23
Artificial Intelligence
machine-learning
statistics
artificial-intelligence
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1
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UPENN | ML | DA Practice | Regularization
After applying a regularization penalty in linear regression, you find that some of the coefficients of $w$ are zeroed out. Which of the following penalties might have been used?(a) ... (c) L2 norm(d) either (A) or (B)(e) any of the above
ruchit816
364
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ruchit816
answered
Jan 21
Artificial Intelligence
machine-learning
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statistics
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469
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1
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AI Sample Question for DS-AI
Imagine you are guiding a robot through a grid-based maze using the A* algorithm. The robot is currently at node A (start) and wants to reach node B (goal). ... A* calculation? A) Node CB) Node DC) Node ED) Not enough information to decide
rajveer43
469
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rajveer43
answered
Jan 16
Artificial Intelligence
artificial-intelligence
machine-learning
probability
statistics
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324
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UPENN | DS-AI Sample | Decision Tree
When choosing one feature from \(X_1, \ldots, X_n\) while building a Decision Tree, which of the following criteria is the most appropriate to maximize? (Here, \(H()\) means entropy, and \(P( ... X_j)\)(d) \(H(Y | X_j)\)(e) \(H(Y) - P(Y)\)
rajveer43
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rajveer43
answered
Jan 16
Artificial Intelligence
artificial-intelligence
machine-learning
statistics
probability
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373
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UPENN | ML | DECISION TREE
Given the following table of observations, calculate the information gain $IG(Y |X)$ that would result from learning the value of $X$. XYRedTrueGreenFalseBrownFalseBrownFalse (a) 1/2(b) 1(c) 3/2(d) 2(e) none of the above
rajveer43
373
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rajveer43
answered
Jan 16
Artificial Intelligence
artificial-intelligence
statistics
machine-learning
binary-tree
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Decision Tree | Sample Question
$True$ or $False?$ If decision trees such as the ones we built in class are allowed to have decision nodes based on questions that can have many ... tend to add the multiple answer questions to the tree before adding the binary questions
prasantkr.singh
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prasantkr.singh
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Jan 15
Artificial Intelligence
algorithms
artificial-intelligence
machine-learning
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UPENN | ML | Cross Validation
P1: In the limit of infinite training and test data, consistent estimators always give at least as low a test error as biased estimators. P2: Leave-one out cross ... ?Only P1 is TrueOnly P2 is TrueP1 is True and P2 is FalseBoth are False
rajveer43
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rajveer43
answered
Jan 13
Artificial Intelligence
machine-learning
artificial-intelligence
statistics
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UPENN | ML | DA Practice
Using the same data as above \( \mathbf{X} = [-3, 5, 4] \) and \( \mathbf{Y} = [-10, 20, 20] \), assuming a ridge penalty \( \lambda = 50 \), what ratio versus the MLE ... \mathbf{w}}_{\text{ridge}} \) will be?(a)] 2b)] 1(c)] 0.666(d)] 0.5
rajveer43
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rajveer43
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Jan 13
Artificial Intelligence
artificial-intelligence
machine-learning
statistics
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UPENN | ML | DA Practice
Consider the statements:$P1:$ It is generally more important to use consistent estimators when one has smaller numbers of training examples.$P2:$ It is generally more important to ... C) Only $P2$ is True(D) Both $P1$ and $P2$ are False
rajveer43
217
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rajveer43
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Jan 13
Artificial Intelligence
machine-learning
artificial-intelligence
statistics
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DA Practice | UPENN | ML | Bias-Variance Trade Off | Regularization
Suppose we have a regularized linear regression model: \[ \text{argmin}_{\mathbf{w}} \left||\mathbf{Y} - \mathbf{Xw} \right||^2 + k \|\ ... bias, increases variance(d)] Decreases bias, decreases variance(e)] Not enough information to tell
rajveer43
266
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rajveer43
answered
Jan 13
Artificial Intelligence
machine-learning
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statistics
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UPENN | ML | DA Practice | Bias-Variance Trade-Off
Suppose we have a regularized linear regression model: \[ \text{argmin}_{\mathbf{w}} \left||\mathbf{Y} - \mathbf{Xw} \right||^2 + \lambda \ ... , increases variance(d)] Decreases bias, decreases variance(e)] Not enough information to tell
rajveer43
179
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rajveer43
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Jan 13
Artificial Intelligence
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machine-learning
statistics
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UPENN | Midterm | K Fold Validation | DA Practice |
Suppose we want to compute $10-Fold$ Cross-Validation error on $100$ training examples. We need to compute error $N1$ times, and the Cross-Validation error is the average of the errors. ... $N1 = 10, N2 = 100, N3 = 10$
rajveer43
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rajveer43
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Jan 13
Artificial Intelligence
machine-learning
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3
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ISRO2018-75
ln neural network, the network capacity is defined as:The traffic (tarry capacity of the networkThe total number of nodes in the networkThe number of patterns that can be stored and recalled in a networkNone of the above
rajveer43
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rajveer43
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Jan 3
Artificial Intelligence
isro2018
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UGC NET CSE | October 2020 | Part 2 | Question: 36
Which of the following is NOT true in problem solving in artificial intelligence?Implements heuristic search techniqueSolution steps are not explicitKnowledge is impreciseIt works on or implements repetition mechanism
rajveer43
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rajveer43
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Jan 3
Artificial Intelligence
ugcnetcse-oct2020-paper2
non-gate
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UGC NET CSE | June 2012 | Part 3 | Question: 21
$A^*$ algorithm uses $f'=g+h'$ to estimate the cost of getting from the initial state to the goal state, where $g$ is a measure of cost getting from initial state to ... $g=0$h'=0$h'=1$
rajveer43
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rajveer43
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Jan 3
Artificial Intelligence
ugcnetcse-june2012-paper3
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UGC NET CSE | June 2012 | Part 3 | Question: 2
In Delta Rule for error minimizationweights are adjusted w.r.to change in the outputweights are adjusted w.r.to difference between desired output and actual ... are adjusted w.r.to difference between output and outputnone of the above
rajveer43
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rajveer43
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Jan 3
Artificial Intelligence
ugcnetcse-june2012-paper3
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UGC NET CSE | December 2012 | Part 2 | Question: 46
Back propagation is a learning technique that adjusts weights in the neutral network by propagating weight changes.Forward from source to sinkBackward from sink to sourceForward from source to hidden nodesBackward from sink to hidden nodes
rajveer43
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rajveer43
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Jan 3
Artificial Intelligence
ugcnetcse-dec2012-paper2
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UGC NET CSE | December 2015 | Part 3 | Question: 8
Forward chaining systems are ____ where as backward chaining systems are ____Data driven, Data drivenGoal driven, Data drivenData driven, Goal drivenGoal driven, Goal driven
rajveer43
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rajveer43
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Jan 3
Artificial Intelligence
ugcnetcse-dec2015-paper3
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UGC NET CSE | December 2015 | Part 3 | Question: 45
Reasoning strategies used in expert systems includeForward chaining, backward chaining and problem reductionForward chaining, backward chaining and boundary ... and back propagationForward chaining, problem reduction and boundary mutation
rajveer43
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rajveer43
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Jan 3
Artificial Intelligence
ugcnetcse-dec2015-paper3
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UGC NET CSE | December 2015 | Part 3 | Question: 46
Language model used in LISP isFunctional programmingLogic programmingObject oriented programmingAll of the above
rajveer43
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rajveer43
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Jan 3
Artificial Intelligence
ugcnetcse-dec2015-paper3
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Artificial Intelligence Heuristic problem Confusion
rajveer43
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rajveer43
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Jan 3
Artificial Intelligence
artificial
intelligence
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2
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UGC NET CSE | July 2018 | Part 2 | Question: 73
In heuristic search algorithms in Artificial Intelligence (AI), if a collection of admissible heuristics $h_1 \dots h_m$ is available for a problem and none of them dominates any of the ... h_m(n)\}$h(n)=sum\{h_1(n), \dots , h_m(n)\}$
rajveer43
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rajveer43
answered
Jan 3
Artificial Intelligence
ugcnetcse-july2018-paper2
artificial-intelligence
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UGC NET CSE | July 2018 | Part 2 | Question: 78
Consider the following two sentences:The planning graph data structure can be used to give a better heuristic for a planning problemDropping negative effects from every ... but sentence b is falseSentence a is false but sentence b is true
rajveer43
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rajveer43
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Jan 3
Artificial Intelligence
ugcnetcse-july2018-paper2
planning
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UGC NET CSE | June 2019 | Part 2 | Question: 97
Consider the following:EvolutionSelectionReproductionMutationWhich of the following are found in genetic algorithms?b, c and d onlyb and d onlya, b, c and da, b and d only
rajveer43
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rajveer43
answered
Jan 3
Artificial Intelligence
ugcnetcse-june2019-paper2
artificial-intelligence
genetic-algorithms
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UGC NET CSE | June 2016 | Part 3 | Question: 66
A perceptron has input weights $W_1=-3.9$ and $W_2=1.1$ with threshold value $T=0.3.$ What output does it give for the input $x_1=1.3$ and $x_2=2.2?$-2.65$-2.30$0$1$
rajveer43
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rajveer43
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Jan 3
Artificial Intelligence
ugcnetcse-june2016-paper3
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UGC NET CSE | July 2018 | Part 2 | Question: 74
Consider following sentences regarding $A^*$, an informed search strategy in Artificial Intelligence (AI).$A^*$ expands all nodes with $f(n)<C^*$ ... statements b and statement c are trueAll the statements a, b and c are true
rajveer43
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rajveer43
answered
Jan 3
Artificial Intelligence
ugcnetcse-july2018-paper2
artificial-intelligence
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1
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Machine Learning
You are a designing a machine learning model for a binary classification problem. The model has three features: f1, f2, f3. Derive the objective and loss function for this problem.
rajveer43
466
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rajveer43
answered
Jan 3
Artificial Intelligence
machine-learning
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1
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Machine Learning Self-doubt
Please Solve this question with full explanation.
rajveer43
315
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rajveer43
answered
Jan 3
Artificial Intelligence
machine-learning
self-doubt
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439
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Ai Questions | DS-AI Paper | GATE 2024
Given a tree with a branching factor of 3 and a depth of 4, calculate the maximum number of nodes expanded during a breadth-first search.
C.Aravind REDDY
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C.Aravind REDDY
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Jan 2
Artificial Intelligence
discrete-mathematics
analytical-aptitude
quantitative-aptitude
artificial-intelligence
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650
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1
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DRDO CSE 2022 Paper 2 | Question: 28 (a)
Provide the correct answer for the following:________ is not the best evaluation metric for cancer prediction problem.
Tejas07
650
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Tejas07
answered
Dec 29, 2023
Artificial Intelligence
drdocse-2022-paper2
artificial-intelligence
2-marks
fill-in-the-blanks
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422
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0
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GATE DS-AI questions | ML
Consider the feature transform z = [L0(x) L1(x) L2(x)]T with Legendre polynomials and the linear model h(x) = w T .z. For the regularized hypothesis with w = [−1 ... 1] T , what is h(x) explicitly as a function of x? write solution for It.
rajveer43
422
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rajveer43
asked
Dec 11, 2023
Artificial Intelligence
artificial-intelligence
machine-learning
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553
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1
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DRDO CSE 2022 Paper 2 | Question: 28 (b)
Provide the correct answer for the following:The phenomena in which training error of the model decreases but test error increases is called___________.
Lakshay Kakkar
553
views
Lakshay Kakkar
answered
Dec 3, 2023
Artificial Intelligence
drdocse-2022-paper2
artificial-intelligence
2-marks
fill-in-the-blanks
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434
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1
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DRDO CSE 2022 Paper 2 | Question: 32
A perceptron consists of weights $\left[w_{1}, w_{2}, w_{3}, w_{4}\right]=[0.5,2,1,-3]$. The activation function is provided as $y=f(z)=1$ if $z \geq 2$ otherwise $0,$ ...
Kazuha
434
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Kazuha
answered
Oct 3, 2023
Artificial Intelligence
drdocse-2022-paper2
artificial-intelligence
activation-function
5-marks
descriptive
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