AI-Mock-Interviewer/PDF_Extracted_Data · Datasets at Hugging Face

Questions stringlengths 5 360 ⌀	Answers stringlengths 6 2.23k ⌀
what is the central limit theorem and why is it important?	Suppose that we are interested in estimating the average height among all people. Collecting data for every person in the world is impossible. While we cant obtain a height measurement from everyone in the population, we can still sample some people. The question now becomes, what can we say about the average height of the entire population given a single sample. The Central Limit Theorem addresses this question exactly. Read more here .
what is sampling how many sampling methods do you know?	Data sampling is a statistical analysis technique used to select, manipulate and analyze a representative subset of data points to identify patterns and trends in the l arger data set being examined. Read the full answer here .
what is the difference between type ivs type ii error?	null
what is linear regression what do the terms p value coefficient and r squared	null
value mean what is the significance of each of these components?	null
what are the assumptions required for linear regression?	There are four major assumptions: 1. There is a linear relationship between the dependent variables and the regres sors, meaning the model you are creating actually fits the data, 2. The errors or residuals of the data are normally distributed and independent from each other, 3. There is minimal multicollinearity between explanatory variables, and 4. Homoscedasticity. This means the variance around the regression line is the same for all values of the predictor variable .
what is a statistical interaction?	Basically, an interaction is when the effect of one factor (input variable) on the dependent variable (output varia ble) differs among levels of another factor. Read more here .
what is selection bias?	null
what is an example of a dataset with a non gaussian distribution?	The Gaussian distribution is part of the Exponential family of distributions, but there are a lot more of them, with the same sort of ease of use, in many cases, and if the person doing th e machine learning has a solid grounding in statistics, they can be utilized where appropriate. Read more here .
what is the binomial probability formula?	The binomial distribution consists of the probabilities of each of the possible numbers of successes on N trials for independent events that each have a pro bability of (the Greek letter pi) of occurring. Read more Data Science :
what is data science list the differences between supervised and unsupervised	learning. Data Science is a blend of various tools, algorithms, and machine learning principles wi th the goal to discover
hidden patterns from the raw data how is this different from what statisticians have been doing for years?	null
what is bias variance tradeoff?	null
what is a confusion matrix?	null
what is the difference between long and wide format data?	null
what do you understand by the term normal distribution?	Data is usually distributed in different ways with a bias to the left or to the right or it can all be jumbled up. However, there are chances that data is distributed around a central value without any bias to the left or right and reaches normal distribution in the form of a bell -shaped curve. Figure: Normal distribution in a bell curve The random variables are distributed in the form of a symmetrical, bell -shaped curve. Properties of Normal Distribution are as follows; 1. Unimodal -one mode 2. Symmetrical -left and right halves are mirror images 3. Bell-shaped -maximum height (mode) at the mean 4. Mean, Mode, and Median are all located in the cen ter 5. Asymptotic
what is correlation and covariance in statistics?	null
what is the difference between point estimates and confidence interval?	Point Estimation gives us a particular value as an estimate of a populatio n parameter. Method of Moments and Maximum Likelihood estimator methods are used to derive Point Estimators for population parameters. A confidence interval gives us a r ange of values which is likely to contain the population parameter. The confidence inte rval is generally preferred, as it tells us how likely this interval is to contain the population parameter. This likeliness or probability is called Confidence Level or Confidence coefficient and represented by 1 alpha, where alpha is the level of signi ficance.
what is the goal of ab testing?	null
what is p value?	null
what is the probability that you see at least one shooting star in the period of an hour?	Probability of not seeing any shooting star in 15 minutes is = 1 P( Seeing one shooting s tar ) = 1 0.2 = 0.8 Probability of not seeing any shooting star in the period of one hour = (0.8) ^ 4 = 0.4096 Probability of see ing at least one shooting star in the one hour = 1 P( Not seeing any star ) = 1 0.4096 = 0.5904
how can you generate a random number between 17 with only a die?	null
probability that they have two girls?	null
is also ahead?	There are two ways of choosing the coin. One is to pick a fair coin and the other is to pick the one with two heads. Probability of sele cting fair coin = 999/1000 = 0.999 Probability of selecting unfair coin = 1/1000 = 0.001 Selecting 10 heads in a row = Selecting fair coin * Getting 10 heads + Selecting an unfair coin P (A) = 0.999 * (1/ 2)^5 = 0.999 * (1/1024) = 0.000976 P (B) = 0.001 * 1 = 0.001 P( A / A + B ) = 0.000976 / (0.000976 + 0.001) = 0.4939 P( B / A + B ) = 0.001 / 0.001976 = 0.5061 Probability of selecting another head = P(A/A+B) * 0.5 + P(B/A+B) * 1 = 0.4939 * 0.5 + 0.5061 = 0.7531
what do you understand by statistical power of sensitivity and how do you calculate it?	Sensitivity is commonly used to validate the accuracy of a classifier (Logistic, SVM, Random Forest etc.). Sensitivity is nothing but Pred icted True events/ Total events. True events he re are the events which were true and model also predicted them as true. Calculation of seasonality is pretty straightforward. Seasonality = ( True Positives ) / ( Positives in Actual Dependent Variable )
why is resampling done?	Resampling is done in any of these cases: Estimating the accuracy of sample statistics by using subsets of accessible data or drawing randomly with replacement from a set of data points Substituting labels on data points when per forming significance tests Validating models by using random subsets (bootstrapping, cross -validation)
what are the differences between overfitting and under fitting?	null
how to combat overfitting and under fitting?	To combat overfi tting and underfitting, you can resample the data to estimate the model accuracy (k -fold cross -validation) and by having a validation dataset to evaluate the model.
what is regularisation why is it useful?	Data Scientist Masters Program Explore Curriculum Regularisation is the process of adding tuning parameter to a model to induce smoothness in order to prevent overfitting. This is mo st often done by adding a constant multiple to an existing weight vector. This constant is often the L1(Lasso) or L2(ridge). The model pre dictions should then minimize the loss function calculated on the regularized training set.
what is the law of large numbers?	null
what are confounding variables?	null
what are the types of biases that can occur during sampling?	Selection bias Under coverage bias Survivorship bias
what is survivorship bias?	null
what is selection bias?	Selection bias occurs when the sample obtained is not representative of the population intended to be analysed.
explain how a roc curve works?	null
what is tf idf vectorization?	null
why we generally use soft max nonlinearity function as last operation in network?	null
python orr which one would you prefer for text analytics?	We will prefer Python because of the following reasons: Python would be the best option because it has Pandas library that provides easy to use data structures and high -performance data analysis tools. R is more suitable for machine le arning than j ust text analysis. Python performs faster for all types of text analytics.
how does data cleaning plays a vital role in the analysis?	null
what is cluster sampling?	null
what is systematic sampling?	null
what are eigenvectors and eigenvalues?	null
can you cite some examples where a false positive is important than a false negative?	null
actually purchased anything but are marked as having made 10000 worth of purchase follow steven our if or more ai and data science posts httpslnkdingzu463xq36 can you cite some examples where a false negative important than a false positive?	Examp le 1: Assume there is an airport A which has received high -security threats and based on certain characteristics they identify whether a particular passenger can be a threat or not. Due to a shortage of staff, they decide to scan passe ngers being predict ed as risk positives by their predictive model. What will happen
if a true threat customer is being flagged as non threat by airport model?	null
example 2whatifjuryor judge decides to make a criminal go free?	Example 3 : What if you rejec ted to marry a very good person based on your predictive model and you
happen to meet him her after a few years and realize that you had a false negative?	Q37. Can you cite some examples where both false positive and false negatives are equally
important?	null
can you explain the difference between a validation set and a test set?	null
what is machine learning?	null
what is supervised learning?	null
what is unsupervised learning?	null
what are the various classification algorithms?	The diagram lists the most important classification algorithms .
what is naive in a naive bayes?	null
what are the support vectors in svm?	null
what are the different kernels in svm?	null
what are entropy and information gain in decision tree algorithm?	null
what is pruning in decision tree?	Pruning is a technique in machine learning and search algorithms that reduces the size of decision trees by removing sections of the tree that provide little power to classify instances. S o, when we remove sub-node s of a decision node, this process is called pruning or opposite process of splitting.
what is logistic regression state an example when you have used logistic	null
what is linear regression?	null
what are the drawbacks of the linear model?	null
what is the difference between regression and classification ml techniques?	null
what are recommender systems?	null
what is collaborative filtering?	null
how can outlier values be treated?	null
what are the various steps involved in an analytics project?	The following are the various steps involved in an analytics project : 1. Understand the Business problem 2. Explore the data and become familiar with it. 3. Prepare the data for modelling by detecting outliers, trea ting missing values, transforming variables, etc. 4. After data preparation, start running the model, analyze the result and tweak the approach. This is an iterative step until the best possible outcome is achieved. 5. Validate the model using a ne w data set. 6. Start implementing the model and track the result to analyze the performance of the model over the period of time.
during analysis how do you treat missing values?	null
follow steven our if or more ai and data science posts httpslnkdingzu463xq60how will you define the number of clusters in a clustering algorithm?	null
what is ensemble learning?	null
describe in brief any type of ensemble learning?	null
what is a random forest how does it work?	null
how do you work towards a random forest?	The underlying principle of this technique is that several weak learners combined to provide a keen learner. The steps involved are Build several decision trees on bootstrapped training samples of data On each tree, each time a split is considered, a random sample of mm predictors is chosen as split candidates, out of all pp predictors Rule of thumb: At eac h split m=pm=p Predictions: At the majority rule
what cross validation technique would you use on a time series data set?	null
what is a box cox transformation?	null
how regularly must an algorithm be updated?	null
gb dataset how would you go about this problem have you ever faced this kind of	null
problem in your machine learning data science experience so far?	null
what do you mean by deep learning?	Deep Learning is nothing but a paradigm of mach ine learning which has shown incred ible promise in recent years. This is because of the fact that Deep Learning shows a great analogy with the functioning of the human brain.
what is the difference between machine learning and deep learning?	Machine l earning is a field of computer science that gives computers the ability to learn without being explicitly programmed. Machine learning can be categorised in the following three categories. 1. Supervised machine learning, 2. Unsupervised machine learning, 3. Reinfor cement learning Deep Learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks. Q71. What, in your opinion, is the reason for the popularity of Deep Learning in recent
times?	null
what is reinforcement learning?	Reinforcement Learning is learning what to do and how to map situations to actions. The end result is to maximise the numerical reward signal. The learner is not told which acti on to take but in stead must discover which action will yield the maximum reward. Reinforcement learning is inspired by the learning of human beings, it is based on the reward/penalty mechanism.
what are artificial neural networks?	Artificial Neural ne tworks are a spec ific set of algorithms that have revolutionized machine learning. They are inspired by biological neural networks. Neural Networks can adapt to changing the i nput so the netwo rk generates the best possible result without needing to redesign the output criteria.
describe the structure of artificial neural networks?	null
how are weights initialized in a network?	null
what is the cost function?	null
what are hyperparameters?	null
what will happen if the learning rate is set in accurately too low or too high?	null
what is the difference between epoch batch and iteration in deep learning?	null
what are the different layers on cnn?	null
pooling on cnn and how does it work?	null
what are recurrent neural networks r nns?	null
how does an lst m network work?	null
what is a multilayer perceptron mlp?	null
exploding gradients?	null
what is vanishing gradients?	null
what are the variants of backpropagation?	null
what are the different deep learning frameworks?	null
what is the role of the activation function?	The Activation function is used to introduce non-linearity into the neural netwo rk helping it to learn more complex function. Without which the neural network would be only able to learn linear function which is a linear combination of its input data. An activation function is a function in an artifi cial neuron that delivers an output based on inputs. Q93. Name a few Machine Learning libraries for various purposes . Purpose Libraries Scientific Computation Numpy Tabular Data Pandas Data Modelling & Pr eprocessing Scikit Learn Time -Series Analysis Statsmodels Text processing Regular Expressions, NLTK Deep Learning Tensorflow, Pytorch
what is an auto encoder?	Auto -enco ders are simple learning networks that aim to transform inputs into outputs with the minimum possible error. This means that we want the output to be as close to input as possible. We add a couple of layers between the input and the output, and the sizes of these layers are smaller than the input layer. The auto-encoder receives unlabelled input which is then encoded to reconstruct the input.
what is a boltzmann machine?	Boltzmann machines have a simple learning algorithm that allows them to discove r interesting features that represent complex regularities in the training data. The Boltzmann machine is basically used to optimise the weights and the quantity for the g iven problem. The learning algorithm is very slow in networks with many layers of fea ture detectors. Restricted Boltzmann Machines algorithm has a single layer of feature detectors which makes it faster than the rest.

what is the central limit theorem and why is it important?

Suppose that we are interested in estimating the average height among all people. Collecting data for every person in the world is impossible. While we cant obtain a height measurement from everyone in the population, we can still sample some people. The question now becomes, what can we say about the average height of the entire population given a single sample. The Central Limit Theorem addresses this question exactly. Read more here .

what is sampling how many sampling methods do you know?

Data sampling is a statistical analysis technique used to select, manipulate and analyze a representative subset of data points to identify patterns and trends in the l arger data set being examined. Read the full answer here .

what is the difference between type ivs type ii error?

null

what is linear regression what do the terms p value coefficient and r squared

null

value mean what is the significance of each of these components?

null

what are the assumptions required for linear regression?

There are four major assumptions: 1. There is a linear relationship between the dependent variables and the regres sors, meaning the model you are creating actually fits the data, 2. The errors or residuals of the data are normally distributed and independent from each other, 3. There is minimal multicollinearity between explanatory variables, and 4. Homoscedasticity. This means the variance around the regression line is the same for all values of the predictor variable .

what is a statistical interaction?

Basically, an interaction is when the effect of one factor (input variable) on the dependent variable (output varia ble) differs among levels of another factor. Read more here .

what is selection bias?

null

what is an example of a dataset with a non gaussian distribution?

The Gaussian distribution is part of the Exponential family of distributions, but there are a lot more of them, with the same sort of ease of use, in many cases, and if the person doing th e machine learning has a solid grounding in statistics, they can be utilized where appropriate. Read more here .

what is the binomial probability formula?

The binomial distribution consists of the probabilities of each of the possible numbers of successes on N trials for independent events that each have a pro bability of (the Greek letter pi) of occurring. Read more Data Science :

what is data science list the differences between supervised and unsupervised

learning. Data Science is a blend of various tools, algorithms, and machine learning principles wi th the goal to discover

hidden patterns from the raw data how is this different from what statisticians have been doing for years?

null

what is bias variance tradeoff?

null

what is a confusion matrix?

null

what is the difference between long and wide format data?

null

what do you understand by the term normal distribution?

Data is usually distributed in different ways with a bias to the left or to the right or it can all be jumbled up. However, there are chances that data is distributed around a central value without any bias to the left or right and reaches normal distribution in the form of a bell -shaped curve. Figure: Normal distribution in a bell curve The random variables are distributed in the form of a symmetrical, bell -shaped curve. Properties of Normal Distribution are as follows; 1. Unimodal -one mode 2. Symmetrical -left and right halves are mirror images 3. Bell-shaped -maximum height (mode) at the mean 4. Mean, Mode, and Median are all located in the cen ter 5. Asymptotic

what is correlation and covariance in statistics?

null

what is the difference between point estimates and confidence interval?

Point Estimation gives us a particular value as an estimate of a populatio n parameter. Method of Moments and Maximum Likelihood estimator methods are used to derive Point Estimators for population parameters. A confidence interval gives us a r ange of values which is likely to contain the population parameter. The confidence inte rval is generally preferred, as it tells us how likely this interval is to contain the population parameter. This likeliness or probability is called Confidence Level or Confidence coefficient and represented by 1 alpha, where alpha is the level of signi ficance.

what is the goal of ab testing?

null

what is p value?

null

what is the probability that you see at least one shooting star in the period of an hour?

Probability of not seeing any shooting star in 15 minutes is = 1 P( Seeing one shooting s tar ) = 1 0.2 = 0.8 Probability of not seeing any shooting star in the period of one hour = (0.8) ^ 4 = 0.4096 Probability of see ing at least one shooting star in the one hour = 1 P( Not seeing any star ) = 1 0.4096 = 0.5904

how can you generate a random number between 17 with only a die?

null

probability that they have two girls?

null

is also ahead?

There are two ways of choosing the coin. One is to pick a fair coin and the other is to pick the one with two heads. Probability of sele cting fair coin = 999/1000 = 0.999 Probability of selecting unfair coin = 1/1000 = 0.001 Selecting 10 heads in a row = Selecting fair coin * Getting 10 heads + Selecting an unfair coin P (A) = 0.999 * (1/ 2)^5 = 0.999 * (1/1024) = 0.000976 P (B) = 0.001 * 1 = 0.001 P( A / A + B ) = 0.000976 / (0.000976 + 0.001) = 0.4939 P( B / A + B ) = 0.001 / 0.001976 = 0.5061 Probability of selecting another head = P(A/A+B) * 0.5 + P(B/A+B) * 1 = 0.4939 * 0.5 + 0.5061 = 0.7531

what do you understand by statistical power of sensitivity and how do you calculate it?

Sensitivity is commonly used to validate the accuracy of a classifier (Logistic, SVM, Random Forest etc.). Sensitivity is nothing but Pred icted True events/ Total events. True events he re are the events which were true and model also predicted them as true. Calculation of seasonality is pretty straightforward. Seasonality = ( True Positives ) / ( Positives in Actual Dependent Variable )

why is resampling done?

Resampling is done in any of these cases: Estimating the accuracy of sample statistics by using subsets of accessible data or drawing randomly with replacement from a set of data points Substituting labels on data points when per forming significance tests Validating models by using random subsets (bootstrapping, cross -validation)

what are the differences between overfitting and under fitting?

null

how to combat overfitting and under fitting?

To combat overfi tting and underfitting, you can resample the data to estimate the model accuracy (k -fold cross -validation) and by having a validation dataset to evaluate the model.

what is regularisation why is it useful?

Data Scientist Masters Program Explore Curriculum Regularisation is the process of adding tuning parameter to a model to induce smoothness in order to prevent overfitting. This is mo st often done by adding a constant multiple to an existing weight vector. This constant is often the L1(Lasso) or L2(ridge). The model pre dictions should then minimize the loss function calculated on the regularized training set.

what is the law of large numbers?

null

what are confounding variables?

null

what are the types of biases that can occur during sampling?

Selection bias Under coverage bias Survivorship bias

what is survivorship bias?

null

what is selection bias?

Selection bias occurs when the sample obtained is not representative of the population intended to be analysed.

explain how a roc curve works?

null

what is tf idf vectorization?

null

why we generally use soft max nonlinearity function as last operation in network?

null

python orr which one would you prefer for text analytics?

We will prefer Python because of the following reasons: Python would be the best option because it has Pandas library that provides easy to use data structures and high -performance data analysis tools. R is more suitable for machine le arning than j ust text analysis. Python performs faster for all types of text analytics.

how does data cleaning plays a vital role in the analysis?

null

what is cluster sampling?

null

what is systematic sampling?

null

what are eigenvectors and eigenvalues?

null

can you cite some examples where a false positive is important than a false negative?

null

actually purchased anything but are marked as having made 10000 worth of purchase follow steven our if or more ai and data science posts httpslnkdingzu463xq36 can you cite some examples where a false negative important than a false positive?

Examp le 1: Assume there is an airport A which has received high -security threats and based on certain characteristics they identify whether a particular passenger can be a threat or not. Due to a shortage of staff, they decide to scan passe ngers being predict ed as risk positives by their predictive model. What will happen

if a true threat customer is being flagged as non threat by airport model?

null

example 2whatifjuryor judge decides to make a criminal go free?

Example 3 : What if you rejec ted to marry a very good person based on your predictive model and you

happen to meet him her after a few years and realize that you had a false negative?

Q37. Can you cite some examples where both false positive and false negatives are equally

important?

null

can you explain the difference between a validation set and a test set?

null

what is machine learning?

null

what is supervised learning?

null

what is unsupervised learning?

null

what are the various classification algorithms?

The diagram lists the most important classification algorithms .

what is naive in a naive bayes?

null

what are the support vectors in svm?

null

what are the different kernels in svm?

null

what are entropy and information gain in decision tree algorithm?

null

what is pruning in decision tree?

Pruning is a technique in machine learning and search algorithms that reduces the size of decision trees by removing sections of the tree that provide little power to classify instances. S o, when we remove sub-node s of a decision node, this process is called pruning or opposite process of splitting.

what is logistic regression state an example when you have used logistic

null

what is linear regression?

null

what are the drawbacks of the linear model?

null

what is the difference between regression and classification ml techniques?

null

what are recommender systems?

null

what is collaborative filtering?

null

how can outlier values be treated?

null

what are the various steps involved in an analytics project?

The following are the various steps involved in an analytics project : 1. Understand the Business problem 2. Explore the data and become familiar with it. 3. Prepare the data for modelling by detecting outliers, trea ting missing values, transforming variables, etc. 4. After data preparation, start running the model, analyze the result and tweak the approach. This is an iterative step until the best possible outcome is achieved. 5. Validate the model using a ne w data set. 6. Start implementing the model and track the result to analyze the performance of the model over the period of time.

during analysis how do you treat missing values?

null

follow steven our if or more ai and data science posts httpslnkdingzu463xq60how will you define the number of clusters in a clustering algorithm?

null

what is ensemble learning?

null

describe in brief any type of ensemble learning?

null

what is a random forest how does it work?

null

how do you work towards a random forest?

The underlying principle of this technique is that several weak learners combined to provide a keen learner. The steps involved are Build several decision trees on bootstrapped training samples of data On each tree, each time a split is considered, a random sample of mm predictors is chosen as split candidates, out of all pp predictors Rule of thumb: At eac h split m=pm=p Predictions: At the majority rule

what cross validation technique would you use on a time series data set?

null

what is a box cox transformation?

null

how regularly must an algorithm be updated?

null

gb dataset how would you go about this problem have you ever faced this kind of

null

problem in your machine learning data science experience so far?

null

what do you mean by deep learning?

Deep Learning is nothing but a paradigm of mach ine learning which has shown incred ible promise in recent years. This is because of the fact that Deep Learning shows a great analogy with the functioning of the human brain.

what is the difference between machine learning and deep learning?

Machine l earning is a field of computer science that gives computers the ability to learn without being explicitly programmed. Machine learning can be categorised in the following three categories. 1. Supervised machine learning, 2. Unsupervised machine learning, 3. Reinfor cement learning Deep Learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks. Q71. What, in your opinion, is the reason for the popularity of Deep Learning in recent

times?

null

what is reinforcement learning?

Reinforcement Learning is learning what to do and how to map situations to actions. The end result is to maximise the numerical reward signal. The learner is not told which acti on to take but in stead must discover which action will yield the maximum reward. Reinforcement learning is inspired by the learning of human beings, it is based on the reward/penalty mechanism.

what are artificial neural networks?

Artificial Neural ne tworks are a spec ific set of algorithms that have revolutionized machine learning. They are inspired by biological neural networks. Neural Networks can adapt to changing the i nput so the netwo rk generates the best possible result without needing to redesign the output criteria.

describe the structure of artificial neural networks?

null

how are weights initialized in a network?

null

what is the cost function?

null

what are hyperparameters?

null

what will happen if the learning rate is set in accurately too low or too high?

null

what is the difference between epoch batch and iteration in deep learning?

null

what are the different layers on cnn?

null

pooling on cnn and how does it work?

null

what are recurrent neural networks r nns?

null

how does an lst m network work?

null

what is a multilayer perceptron mlp?

null

exploding gradients?

null

what is vanishing gradients?

null

what are the variants of backpropagation?

null

what are the different deep learning frameworks?

null

what is the role of the activation function?

The Activation function is used to introduce non-linearity into the neural netwo rk helping it to learn more complex function. Without which the neural network would be only able to learn linear function which is a linear combination of its input data. An activation function is a function in an artifi cial neuron that delivers an output based on inputs. Q93. Name a few Machine Learning libraries for various purposes . Purpose Libraries Scientific Computation Numpy Tabular Data Pandas Data Modelling & Pr eprocessing Scikit Learn Time -Series Analysis Statsmodels Text processing Regular Expressions, NLTK Deep Learning Tensorflow, Pytorch

what is an auto encoder?

Auto -enco ders are simple learning networks that aim to transform inputs into outputs with the minimum possible error. This means that we want the output to be as close to input as possible. We add a couple of layers between the input and the output, and the sizes of these layers are smaller than the input layer. The auto-encoder receives unlabelled input which is then encoded to reconstruct the input.

what is a boltzmann machine?

Boltzmann machines have a simple learning algorithm that allows them to discove r interesting features that represent complex regularities in the training data. The Boltzmann machine is basically used to optimise the weights and the quantity for the g iven problem. The learning algorithm is very slow in networks with many layers of fea ture detectors. Restricted Boltzmann Machines algorithm has a single layer of feature detectors which makes it faster than the rest.