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Artificial Intelligence Interview Questions and Answers

by GangBoard Admin, November 26, 2018

In case you’re searching for Artificial Intelligence Interview Questions and answers for Experienced or Freshers, you are at the correct place. There is parcel of chances from many presumed organizations on the planet. The Artificial Intelligence advertise is relied upon to develop to more than $5 billion by 2020, from just $180 million, as per Artificial Intelligence industry gauges. In this way, despite everything you have the chance to push forward in your vocation in Artificial Intelligence Development. Gangboard offers Advanced Artificial Intelligence Interview Questions and answers that assist you in splitting your Artificial Intelligence interview and procure dream vocation as Artificial Intelligence Developer.

Best Artificial Intelligence Interview Questions and Answers

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Q1). Describe about Classical AI & Weak AI?

Answer:

Classical AI:

It has more concerned with deductive thought as provided with set of constraints, deduce a conclusion

Weak AI:

It simply predicts that some features that are resembling to the human machine intelligence can be incorporated to computer make it more useful tools in it.

Q2). What is a production rule consist of and the which searhc method take a less memory?

Answer:

Production Rule:

It is a rule which comprises of a set of rule and a sequence of steps in it.

Search Method:

Depth First Search is the method that would takes the less memory for any of the process to follow.

Q3). How A* algorithm is works with search method?

Answer:

A* Algorithm Search Methodology:

A* algo’s is fully based on first search method and it provides an idea of optimization and quick choose of the path and along-with all the characterisitics lies on it the same.

Q4). What is Heruistic Function & How Neural Networks working in AI?

Answer:

Heuristic Function:

It is an alternatives function for ranks in search algorithms and at each branching step based on the available information to decide which branch that needs to be follow.

Neural Networks In AI:

In general it is an biological term, but in artificial intelligence it is an emulation of a biological neural system,

which receives the data, process the data and gives output based on the algorithm and empirical data.

Q5). How the Strong AI & Statistical AI works?

Answer:

Strong AI:

It claims that the computer can be made to think on a level equal to humans. This is what the strong ai performs.

Statistical AI:

It has more concerned with inductive thought as provided with set of patterns, induce with the trend.

Q6). How to resolve a problem with the Game Playing Problem Methodology?

Answer:

Game Playing Problem Methodology:

Heuristic Approach is the best way to proceed further for game playing problem, though it will use the technique based on intelligent guesswork. Let us say an example like chess game – Chess between human and computer as it will proceed with brute force computation and looking at hundreds of thousands of positions.

Q7). Simple Explanation about Alternate, Artificial, Compound and Natural Key?

Answer:

Natural Key:

It is one of the data element that is stored within a construct, and it is optimized as the primary key.

Compound & Artifical Key:

  • If there is no single data element that uniquely defines the occurrences within a construct, then integrating multiple elements to create a unique identifier for the construct and it is called as compound key.
  • If there is no obvious key either stands alone or compound is available, then the last report is to simply create a key by assigning a number to each record or occurrence and it is called a artificial key.

Q8). What is an Agent and How Partial Order or Planning Involve?

Answer:

Agent:

Like, anything that preceives its environment by the sensors, and act upon an environment by effectors are called as

Agent. (e.g. Robots, Programs, Humans, HCI, HMI etc.)

Partial Order or Planning:

Instead of searching over possible situation that involves searching over the space of possbile plans. Then the idea can be construct as a plan piece by piece.

Q9). On How many ways we can construct a plan and which property is considered as not a desirable property of a logical rule-based system?

Answer:

Ways to Construct a Plan:

Action, nothing but add an operator Secondly, add an ordering constraints between the operators

Property not desirable:

Attachment is the one which not considered as a logical-rule based system in artificial intelligence.

Q10). How the Generality and Top-Down Parser works?

Answer:

Generality:

It is the ease measure with which the method can be adapted to different domains of application

Top-Down Parser:

It begins by hypothesizing a sentence and successively predicting lower level constituents until that the individual pre-terminal symbols are written.

Q11). How FOPL works in AI?

Answer:

FOLP:

It’s nothing but a first order predicate logic we called as shortly FOPL.

Working Methodology:

  • It needs a language to express assertions about certain world
  • It needs an inteference system to deductive apparatus whereby we may draw conclusions from such assertions
  • It needs a semantic based on set theory

Q12). How AI perform against Frames and Scripts?

Answer:

Frames:

  • They are variant of semantic networks which is one of the popular ways of presenting non-procedural knowledge in an expert system.
  • A frame which is an artificial data structure is used to divide the knowledge into substructure by representing in stereotyped-situations.

Scripts:

  • It is similar with respect to frames but except the values that fill the slots must be ordered.
  • Though, scripts used in natural language understanding systems to organize a knowledge base in terms of the situation that the system should understand.

Q13).Describe the Building Mehtodology of Bayesian Network and Bayes Model with respect to AI?

Answer:

Build Methodology of Bayesian Network:

  • The Bayesian Network creation, falls under the consequence between a node and its predecessors.
  • And also the node can be conditionally independent of its predecessors.

Build Methodology of Bayes Model:

  • There are three terminology that required to build a Bayes Model.
  • And they should be one conditional probability based and the other two unconditional probability based.

Q14). Breif the Literal that help for top-down inductive learning methods?

Answer:

The below are the few literal that currently used for inductive learning methodology:

  • Predicates
  • Equality & In-Equality
  • Arithmetic Literals

Q15). What is Hidden Markov Model & Signal Flow In Speech Recoginition?

Answer:

HMM:

  • It is ubiquitous tool for modelling time series data or to model sequence behaviour of the system.
  • It can be used in almost all the current speech based systems.
  • It can be stated in single discrete random variable.

Signal Flow Used:

Acoustic signal flow is the one which can be used in the speech to identify the sequence of words.

Q16).  Which is the similar operation performed by the drop-out in neural network?

Answer:

Bagging; Dropout can be seen as bagging, it each training step it creates a different network which is trained with backpropagation. It is same as ensemble of many networks

trained with a single sample.

Q17). How a neural networks became a universal function approximators?

Answer: Because of the Activation Functions; Yes, the activation functions are helpful in making the functions piecewise linear which in turn helps in representing the any complex function.

Q18). What are the possible reasons for slow learning rate in the Neural networks?

Answer:

  • Low learning rate,
  • High regularization,
  • Stuck at local Minima

Low learning rate : Because of this the minima is very slow by gradient descent algorithm

High regularization : This will bound the parameter values to the very low values and complexity is very much decreased

Stuck at local Minima : When it is stuck at local minima it requires more iterations or change in learning rate to get out of it.

Q19). Comment on batch Normalization?

Answer: To make the data standardized before sending it to the another layer. It reduces the impacts of previous layers by keeping the mean and variance constant, makes the layers independent of each other. The convergence becomes faster.

Q20). What are the different NLP tasks deep learning can be applied?

Answer:

Machine translation, Sentiment Analysis, Question and Answer system

Machine translation : Sequence to sequence models are used for this.

Sentiment Analysis : Classification techniques on text using neural networks

Question and Answer system : This is again a Seq to seq model

Q21). What are the disadvantages of representing the words with one hot vectors?

Answer:

  • No similarities are captures,
  • Very high no. of dimensions to compute One hot vectors are very sparse vectors which are orthogonal to each other and each vector is represented by the same number of total number of different words in corpus. So it is high dimensional as well as no similarities are captured.

Q22). Simple explanation of one hot representation to lower dimension conversion?

Answer: Trained Neural Network with one hidden layer gives the lookup table. First of all train a model NN model with one hidden layer to predict the context words, after the training the actual weight matrix that is learnt by hidden layer is user for representing the words.

Q23). How dropout is different from regularization related?

Answer: When dropout rate is very high, regularization will be very low. It constrains adapting network to the data to avoid over fitting.

Q24). How BPTT varies from vanilla backpropagation?

Answer: In BPTT, in each time step, for each weight the gradients are summed together. Errors will be calculated for each time step. The weights are updated after the network is rolled back.

Q25). What is the way of dealing with “Exploding Gradient” problem in RNNs?

Answer: Gradient clipping, gradient is set to the threshold. Gradient clipping will chop the gradients or restricts them to a Threshold value to prevent the gradients from getting too large.

Q26). What is advantage of pooling layer in convolutional neural networks?

Answer: Statistical Average of the Output of the convolution layer, which is easy to compute on the further steps. This reduces the spatial size of the representation to reduce the amount of parameters and computation in the network. Pooling layer operates on each feature map independently.

Q27). Can RNN be unfolded into full CNN with infinite length?

Answer: TRUE, RNN’s neuron can be thought of as a neuron sequence of infinite length of time steps.

Q28). Which of the following data augmentation technique would you prefer for an object recognition problem?

Answer: Horizontal flipping, Rescaling, Zooming; Deep learning model actually require so much of data to train the models. It is very data hungry. And to take advantage of training the models with various angles of objects we go with this data augmentation technique.

Q29). Size of Convolution kernel would necessarily increase the performance of CNN?

Answer: FALSE, it is hyperparameter so changing it we can increase or decrease performance. We initially randomly initialize the weights for these kernels and they learn the correct weight by back propagation. So it make more computation time and occupy resources.

Q30). Why do we prefer LSTM over RNN?

Answer: Due to vanishing gradient, Vanishing gradient problem depends on the choice of the activation function. Activation functions (e.g sigmoid or tanh) usually ‘squash’ input into a very small number range in a very non-linear fashion.

Q31). What are techniques can be used for the keyword normalization?

Stemming and Lemmatization.

Answer: Stemming usually is the process that cuts off the ends of words in the hope of deriving the root word most of the time. So in simple word it just removes the affixes.

Lemmatization uses vocabulary and morphological analysis of words, and most of the time root it to the correct root words, ex: good for best. The root words are called as lemma.

Q32). Features used for accuracy improvement of in NLP classification model?

Answer: Frequency counts, Vector Notation, POS, Dependency grammar

Q33). What is the major difference between CRF (Conditional Random Field) and HMM (Hidden

Markov Model)?

Answer: HMMs are generative models , models the joint distribution P(y,x). Therefore, model the distribution of the data P(x). These computations might take longer time compared to directly computing the conditional probability.

CRFs are discriminative models which model conditional probability P(y|x). As such, they do not require P(x) to be modelled. This results in faster performance, as they need fewer parameters to be learned.

Q34). What is generally the sequence followed when building a neural network architecture for semantic segmentation for image?

Answer:

  • Encoder network on input and Decoder network on output.
  • The encoder is a classification network which is pre trained and just like VGG/ResNet and followed by a decoder network.
  • The decoder is to project the lower resolution features learnt by encoder onto higher resolution space to get the dense classification.

Q35). Can Normal Neural Network has long term dependencies on the Sentences?

Answer: NO, RNNs and LSTMs can have them. This is because the hidden state information is also passed to the consecutive layers in RNN and LSTM.

Q36). What is main difference between AI and ML?

Answer: Just think of artificial intelligence as a broader umbrella under which machine learning and deep learning come. From the below diagram we can see that even deep learning is a subset of Machine Leaning. So you can see that all three AI, machine learning and deep learning are the subsets of each other.

Q37). What is the main focus area of artificial intelligence?

a) To solve real-world problems
b) To solve artificial problems
c) To explain various sorts of intelligence
d) To extract scientific causes

Answer: c

Q38). The most primary method that people use to sense their surrounding is:

a) reading
b) writing
c) speaking
d) seeing

Answer: d

Q39). Where Natural language understanding applications can be seen:

a) natural language interfaces
b) natural language front ends
c) text understanding systems
d) all of the mentioned

Answer: d

Q40). Answer True or False: Zero sum games are the one in which there are two agents whose actions is most alternate and in which the utility values at the end of the game are always the same.

a) True
b) False

Answer: b
Explanation: Utility values are always same and opposite.

Q41). How Branches are located in AI?

Answer:

Branches:

  • Expert System
  • Pattern Recognition
  • Swarm Intelligence
  • Data Mining
  • Genetic Algorithm
  • Neural Networks
  • Statistical AI
  • Fuzzy Logic

Q42).Describe Game Theory with AI Relation?

Answer:

Game Theory:

An AI System will use the game theory for the purpose of the requirement that enhance as the more than a participant. So, the relation between the game have two parts like,

  • Participant Design
  • Mechanism Design

Q43). How an AI can find Conference Information?

Answer:

Conference Information:

A webpage that let you search for upcoming or previous conference in AI disciplinary way which can maintained by a Georg Thimm. So, that we called as AI based Conference Information.

Q44). How Relational Knowledge used in AI?

Answer:

Relational Knowledge:

A knowledge representation scheme in which facts are represented as a set of relations. Let’s say e.g. Knowledge about a player can be represented using a relation called as player which consist of three fields,

  • Player Name
  • Height
  • Weight

Q45). Describe the methodology of Inheritable Knowledge in AI?

Answer:

Inheritable Knowledge:

  • A knowledge repesentation scheme which can be represented in the form of objects, their attributes and the corresponding values of the attributes.
  • The relation between the object defined using a isa property in it.
  • Let’s say an e.g. In a game two entities like Amature Male & Person are presented as objects than the relation between the two is that Amature  Male isa Person.

Q46). How NLP works against AI?

Answer:

NLP:

  • Natural Language Processing shortly called as NLP.
  • It’s nothing but an processing and prehaps based understanding.
  • Like, process an computational linguistics with the help of read the scenario by natural human recognizable language.

Q47). Describe about Supervised  and Unsupervised Learning?

Answer:

Supervised Learning:

  • It is one of the machine learning process.
  • It process against the output that fed back from computer for software to learn from for more accurate result in the next time.
  • It can receive initial training to start from machine.

Unsupervised Learning:

  • Different Methodology of machine learning.
  • Though, in contrast with unsupervised machine learning mean a computer will learn without training to base its learning on.

Q48). In Which Scenario AI Accomplished?

Answer:

  • In generic computing machinery & intelligence, computers would able to pass the tuning test at a reasonably sophiscated level, in a particular level.
  • The average interrogator would not be able to identify the computer correctly more than 70 per cent of the time after a minute of  conversation.

Q49). How the Semantic Analysis used in AI?

Answer:

Semantic Analysis:

Semantic will helps to extract the meaning from the group of sentence. So, that the semantic would be helpful in AI.

Q50). What is supervised machine learning?

Answer: It requires training using labelled data. Example: in order into do classification, which was a supervised learning task, you’ll first need into label the data you’ll use into train the model into classify data into your labelled groups.

Q51). What’s the difference between a generative & discriminative model?

Answer: A generative model will learn categories have data while a discriminative model will simply learn the distinction between different categories have data.

Q52). What are the algorithms used in ai ?

Answer:

  • neural networks
  • natural language processing
  • support vector machine

Q53). What is variance?

Answer: This, on the other hand, was error due into way too much complexity in your learning algorithm. Mainly due into this complexity, the algorithm was highly sensitive into high degrees have variation, which can lead your model into over fit the data. Additionally, you will be carrying too much noise from your training data for your model into be useful.

Q54). How KNN was different from k-means clustering?

Answer: The difference between both is, K-Nearest Neighbor was a supervised classification algorithm, whereas k-means was a unsupervised clustering algorithm. The procedure may seem similar at first, what it really means was the in order into K-Nearest Neighbors into work, you need labelled data which you want into classify a unlabeled point into it.

Q55). What was Bayes Theorem?

Answer: Bayes Theorem gives you the posterior probability which have a event given what was known as prior knowledge. Again, it was the basis behind Naive Bayes classifier.

Q56). What was the difference between L1 & L2 regularization?

Answer: First, regularization was the technique which helps into solve over fitting problem in Machine Learning. Regularization inclines into spread error among all the terms, while L1 was more binary, with most variables either being assigned a 1 or 0 in weighting. This corresponds into setting a Laplacian prior on the terms, while L2 corresponds into a Gaussian prior.

Q57). What is Type I error?

Answer: Type one error was false positive, while Type II was false negative. Type one error was claiming something has happened when it hasn’t.

Q58). Explain the difference with Type II with examples.

Answer: Instances are telling a man he was pregnant. Mainly on the other hand, Type II error means you claim nothing was happened but in fact something is. For example, you tell a pregnant lady she isn’t carrying baby.

Q59). What was the difference between Probability & Likelihood?

Answer: The Probability quantifies prediction have outcome whereas likelihood quantifies trust in model. For instances someone challenges us into a profitable gambling game. Then probabilities will serve us into compute like the expected profile have your gains & losses. In contrast, likelihood will serve into quantify whether we trust those probabilities in the first place and whether we smell a rat.

Q60). What Deep Learning was exactly?

Answer: Most people didn’t know this, but Machine Learning & Deep Learning was not two different things, but Deep learning was a subset have Machine learning.

Q61). What DL deals with?

Answer: It mostly deals with neural networks: how into use back propagation & other certain principles from neuroscience into more accurately model large sets have unlabeled data.

Q62). What’s the difference between a generative & discriminative model?

Answer: A discriminative model will understand the distinction between different categories have data, while A generative model will learn categories have data. Models will generously outperform generative models on classification tasks.

Q63). What was Time Series Analysis/Forecasting?

Answer: A Machine Learning data-set which has a collection have observations. For example,

Observation 1

Observation 2

Observation 3

But, a Time series data-set was different. It adds a explicit order dependence between observations: a time dimension.

Q64). What are the additional dimensions?

Answer: The additional dimension was both a constraint & a structure the provides a source has additional information.

Time 1, Observation

Time 2, Observation

Time 3, Observation

Q65). How would you handle a imbalanced data-set?

Answer: It is, for example, you have 90% have the data in one class & 10% in other. Leads into problems such as, no predictive power on the other category have data.

Q66). List few techniques here.

Answer:

Here were few techniques into get over it,

Obviously collect more data into balance

Try different algorithm (Not going into work effectively)

Correct the imbalance in data-set

Q67). Explain Pruning in Decision trees.

Answer: Pruning has you remove branches they have weak predictive power in order into reduce the complexity have the model & in addition increase the predictive accuracy have a decision tree model.

Q68). What are the several flavors?

Answer: There were several flavors which includes, bottom-up & top-down pruning, with approaches such as reduced error pruning & cost complexity pruning.

Q69). Difference between Model accuracy or Model Performance?

Answer: Your Machine Learning model performance & often look towards details. There were models with higher accuracy it can perform worse in predictive power as well.

Well it has everything into do with how model accuracy was only a subset have model performance & at that, sometimes misleading one.

Q70). Do you have research experience in machine learning?

Answer: Machine Learning was emerging &  no one wants novice players in their teams. Most employers hiring for ML position will look for experience in field. Research papers supervised by leaders in field, can set you apart from herd. Make sure you were ready with all the summary &  justification have the work you have done in the past years.

Q71). What were the last Machine Learning papers you read? Why you think the was important?

Answer: As this field was emerging day by day, it was crucial into keep up with the latest scientific literatures into show the you were really into Machine Learning &  not here just because it was the latest buzzword. Some good books into start with includes Deep Learning by Ian Good fellow.

Q72). How would you approach the “Netflix Prize” competition?

Answer: The Netflix Prize was a famed competition where Netflix offered $1,000,000 for a better collaborative filtering algorithm. The team the won called BellKor had a 10% improvement &  used a ensemble have different methods into win. Some familiarity with the case &  its solution will help demonstrate you’ve paid attention into machine learning for a while.

Q73). What’s your favorite algorithm, & can you explain it into me in less than a minute?

Answer: This type have question mainly tests your ability have communicating complex &  technical nuances with poise &  the ability into summarize quickly &  efficiently. Make sure you have a choice have algorithm which you can explain easily. Try into explain different algorithms so simply &  effectively the a five-year-old could grasp the basics.

Q74). Where do you usually source data-sets?

Answer: This type have questions were the real tie-breakers. If someone was going for a interview, he/she must know the drill have some related question. It was questions like this which purely illustrates your interest in Machine Learning. See my post for detailed answer on where into find machine learning data-sets.

Q75). How do you think Google was training data for self-driving cars?

Answer: Questions like this check your understanding have current affairs in the industry &  how things at certain level works. Google was currently using recaptcha into source labelled data on storefronts &  traffic signs. They were also building on training data collected by Sebastian Thrun at GoogleX.

Q76). How would you implement a recommendation system for our company’s users?

Answer: There will be a lot have questions like this which will involve implementation have machine learning models into their company’s problems. You should definitely study company’s profile &  its products before going in. In addition, factors such as, financials have the company, in which the company operates, what were their users will help you get a clearer picture.

Q77). How will you handle missing data?

Answer: One can find missing data in a data-set &  either drop those rows or columns, or decide into replace them with another value. In python library Pandas there were two useful functions which will be helpful, isnull() &  dropna().

Q78). Describe a hash table.

Answer: A hash table was a data structure the produces a associative array. A key was mapped into certain values through the use have a hash function. They were often used for tasks such as database indexing.

Q79). Which data visualization libraries do you use & why they were useful?

Answer: What’s important here was into define your views on how into properly visualize data &  your personal preferences when it comes into tools. Popular tools include R’s ggplot, Python’s seaborn &  matplotlib, &  tools such as Plot.ly &  Tableau.

Q80). Considering the long list have machine learning algorithm, given a data set, how do you decide which one into use?

Answer: This was one more tricky question. Given what type have data there is, discrete, time series, continuous, you should give your answers.

Q81). What was the difference between supervised & unsupervised machine learning?

Answer: Supervised learning requires training labeled data. For example, in order into do classification (a supervised learning task), you’ll need into first label the data you’ll use into train the model into classify data into your labeled groups. Unsupervised learning, in contrast, do not require labeling data explicitly.

Q82). How was KNN different from k-means clustering?

Answer: K-Nearest Neighbors was a supervised classification algorithm, while k-means clustering was a unsupervised clustering algorithm. While the mechanisms may seem similar at first, what this really means was the in order for K-Nearest Neighbors into work, you need labeled data you want into classify a unlabeled point into (thus the nearest neighbor part). K-means clustering requires only a set have unlabeled points &  a threshold: the algorithm will take unlabeled points &  gradually learn how into cluster them into groups by computing the mean have the distance between different points.

The critical difference here was the KNN needs labeled points &  was thus supervised learning, while k-means doesn’t — &  was thus unsupervised learning.

Q83). Define precision & Recall

Answer: Recall was also known as the true positive rate: the amount have positives your model claims compared into the actual number have positives there were throughout the data. Precision was also known as the positive predictive value, &  it was a measure have the amount have accurate positives your model claims compared into the number have positives it actually claims. It can be easier into think have recall &  precision in the context have a case where you’ve predicted the there were 10 apples &  5 oranges in a case have 10 apples. You’d have perfect recall (there were actually 10 apples, &  you predicted there would be 10) but 66.7% precision because out have the 15 events you predicted, only 10 (the apples) were correct.

Q84). What was Bayes’ Theorem? How was it useful in a machine learning context?

Answer: Bayes’ Theorem gives you the posterior probability have a event given what was known as prior knowledge. Mathematically, it’s expressed as the true positive rate have a condition sample divided by the sum have the false positive rate have the population &  the true positive rate have a condition. Say you had a 60% chance have actually having the flu after a flu test, but out have people who had the flu, the test will be false 50% have the time, &  the overall population only has a 5% chance have having the flu. Would you actually have a 60% chance have having the flu after having a positive test?

Bayes’ Theorem says no. It says the you have a (.6 * 0.05) (True Positive Rate have a Condition Sample) / (.6*0.05)(True Positive Rate have a Condition Sample) + (.5*0.95) (False Positive Rate have a Population)  = 0.0594 or 5.94% chance have getting a flu.

Bayes’ Theorem was the basis behind a branch have machine learning the most notably includes the Naive Bayes classifier. That’s something important into consider when you’re faced with machine learning interview questions.

Q85). Why was “Naive” Bayes naive?

Answer: Despite its practical applications, especially in text mining, Naive Bayes was considered “Naive” because it makes a assumption the was virtually impossible into see in real-life data: the conditional probability was calculated as the pure product have the individual probabilities have components. This implies the absolute independence have features — a condition probably never met in real life.

Q86). What’s your favorite algorithm, & can you explain it into me in less than a minute?

Answer: This type have question tests your understanding have how into communicate complex &  technical nuances with poise &  the ability into summarize quickly &  efficiently. Make sure you have a choice &  make sure you can explain different algorithms so simply &  effectively the a five-year-old could grasp the basics! A clever way into think about this was into think have Type I error as telling a man he was pregnant, while Type II error means you tell a pregnant woman she isn’t carrying a baby.

Q87). What was deep learning, & how do it contrast with other machine learning algorithms?

Answer: Deep learning was a subset have machine learning the was concerned with neural networks: how into use backpropagation &  certain principles from neuroscience into more accurately model large sets have unlabelled or semi-structured data. In the sense, deep learning represents a unsupervised learning algorithm the learns representations have data through the use have neural nets.

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