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Each time an agent receives its consumption good, it consumes it and immediately after, produces a new unit of its production good (each agent owns a single storage unit).

Therefore, in each \(ij\)-market, the probability of successfully exchanging a good \(i\) against a good \(j\) depends on the respective number of i-sellers – j-buyers and j-sellers– i-buyers (e.g., if there is in the \(ij\)-market at time \(t\), 4 \(i\)-sellers – \(j\)-buyers and 8 \(j\)-sellers – \(i\) buyers, 4 \(ij\)-exchanges will take place and the probability of success for a \(i\)-seller – \(j\) buyers is 0.5 while a \(j\)-seller – \(i\)-buyer will proceed to the desired exchange with certainty).

At time step \(t\), when an agent attempts to exchange \(i\) against \(j\), it updates the success rate estimation associated to the exchange of type \((i,j)\), noted \({e}_{ij}\) according to: with \(\alpha \in [0,1]\), a free parameter and \(s\), a binary variable such as \(s=1\) if the agent succeeded in his exchange, \(0\) otherwise.

Let \(v(ij)\) be the value associated to the choice \(ij\) (i.e., exchange \(i\) against \(j\)) and \({\Delta }_{ij}\) the estimation by the agent of the time that will be spent before consumption if he chooses \(ij\): with \(\beta \ >\ 0\), a free parameter.

If a type-\(ik\) agent (with \(k\,\ne\, j\)), the value of \({\Delta }_{ij}\) depends on the action policy chosen by the agent, as \({\Delta }_{ij}\) would be equal in this case to the sum of the \(\delta\)-values for each intermediary exchange planned by the agent.

However, Apesteguia and Ballester (2018) show that the combination of a softmax rule and either a risk-sensitivity or a temporal discounting model can be problematic, as the parameter describing the risk-sensitivity discounting effect can have a non-monotonic effect on the variable of interest.

With these values, the value associated with exchanging his production good against his consumption good was indeed higher than the value of any other exchange for all agents, implying that all agents were preferring the direct exchange strategy at the first time-step.

We ran \(4\) separate simulations before the experiment using the same distribution of agents as for experiments (\(2\) matching the conditions of Experiment I and \(2\) matching the conditions of Experiment II).

Financial compensation of \(10\) euros was offered to each participant, with a bonus proportional to their score (a subject earned a point when he succeeded to obtain its consumption good and each point corresponded to \(0.20\) euros).

subject plays the role of a producer of a good \(i\) and a consumer of a good \(j\), in an economy comprising either \(30\) (uniform condition) or \(36\) (non-uniform condition) subjects.

During \(50\) time steps, he has to choose which type of exchange he wants to try, among two options (e.g., with good \(1\) in hand, he has to choose between trying to exchange good \(1\) against good \(2\), or good \(1\) against good \(3\)).

For statistical analysis of the human experiment as well the experiment-like simulations, we averaged this measure overtime for the last third of the trials, to assert learning curves were stable.

As we did not expect a normal distribution of data due to clustering effects at the boundaries of our scale, assessment of statistic relevance of our observations has been made with Mann–Whitney’s U ranking test (Mann and Whitney, 1947), applying Bonferroni’s corrections for multiple comparisons.

Also, as a consequence of having 4 goods in circulation, subjects were having 3 alternatives each time, instead of 2 (for instance, with the good \(1\) in hand, they had a choice between trying to exchange it against the good \(2\), \(3\) or \(4\)).

Hence, the distribution was either uniform (U), either non-uniform promoting the use of a medium of exchange (NUPM): With four goods in circulation, two agent types can use the good \(1\) as a medium of exchange: Agents that produce good \(2\) and consume good \(3\) and agents that produce good \(3\) and consume good \(4\).

As we did not expect a normal distribution of data due to clustering effects at the boundaries of our scale, assessment of statistic relevance of our observations has been made with Mann-Whitney’s U ranking test (Mann and Whitney, 1947), applying Bonferroni’s corrections for multiple comparisons.

100+ Data Science Interview Questions You Must Prepare for 2020

In this Data Science Interview Questions blog, I will introduce you to the most frequently asked questions on Data Science, Analytics and Machine Learning interviews.

The following are the topics covered in our interview questions: Before moving ahead, you may go through the recording of Data Science Interview Questions where our instructor has shared his experience and expertise that will help you to crack any Data Science.

Data Science is a blend of various tools, algorithms, and machine learning principles with the goal to discover hidden patterns from the raw data.

The types of selection bias include: Bias: Bias is an error introduced in your model due to oversimplification of the machine learning algorithm.

When you train your model at that time model makes simplified assumptions to make the target function easier to understand.

Linear Regression, Logistic Regression Variance: Variance is error introduced in your model due to complex machine learning algorithm, your model learns noise also from the training data set and performs badly on test data set.

As you continue to make your model more complex, you end up over-fitting your model and hence your model will start suffering from high variance.

Bias-Variance trade-off:The goal of any supervised machine learning algorithm is to have low bias and low variance to achieve good prediction performance.

In the wide-format, a subject’s repeated responses will be in a single row, and each response is in a separate column.In thelong-format, each row is a one-time point per subject.You can recognize data in wide format by the fact that columns generally represent groups.

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.

it explains the systematic relation between a pair of random variables, wherein changes in one variable reciprocal by a corresponding change in another variable.

It can be used to test everything from website copy to sales emails to search ads An example of this could be identifying the click-through rate for a banner ad.

Probability of selecting 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

=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 Q15.

In statistics and machine learning, one of the most common tasks is to fit amodelto a set of training data, so as to be able to make reliable predictions on general untrained data.

To combat overfitting 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.

For example, if you are researching whether a lack of exercise leads to weight gain, lack of exercise = independent variable weight gain = dependent variable.

TF–IDF is short for term frequency-inverse document frequency, is a numerical statistic that is intended to reflect how important a word is to a document in a collection or corpus.

The TF–IDF value increases proportionally to the number of times a word appears in the document but is offset by the frequency of the word in the corpus, which helps to adjust for the fact that some words appear more frequently in general.

Thebivariateanalysisattempts to understand the difference between two variables at a time as in a scatterplot.For example, analyzing the volume of sale and spending can be considered as an example of bivariate analysis.

Cluster samplingis a technique used when it becomes difficult to study the target population spread across a wide area and simple random sampling cannot be applied.Cluster Sample is a probability sample where each sampling unit is a collection or cluster of elements.

Eigenvectorsare used for understanding linear transformations.In data analysis, we usually calculate the eigenvectors for a correlation or covariance matrix.Eigenvectors are the directions along which a particular linear transformation acts by flipping, compressing or stretching.

In the absence of cancerous cell, chemotherapy will do certain damage to his normal healthy cells and might lead to severe diseases, even cancer.

Example 2:Let’s say an e-commerce company decided to give $1000 Gift voucher to the customers whom they assume to purchase at least $10,000 worth of items.They send free voucher mail directly to 100 customers without any minimum purchase condition because they assume to make at least 20% profit on sold items above $10,000.

Now the issue is if we send the $1000 gift vouchers to customers who have not actually purchased anything but are marked as having made $10,000 worth of purchase.

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 ashortage of staff, they decide to scan passengers being predicted as risk positives by their predictive model.

Example 3:What if you rejected 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?

In theBankingindustry giving loans is the primary source of making money but at the same time if your repayment rate is not good you will not make any profit, rather you will risk huge losses.

Cross-validationis a model validation technique for evaluating how the outcomes of statistical analysis willgeneralizeto anindependent dataset.Mainly used in backgrounds where the objective is forecast and one wants to estimate how accurately a model will accomplish in practice.

validation data set) in order to limit problems like overfitting and get an insight on how the model will generalize to an independentdata set.

Machine Learning explores the study and construction of algorithms that can learn from and make predictions on data.Closely related to computational statistics.Used to devise complex models and algorithms that lend themselves to a prediction which in commercial use is known as predictive analytics.

If you built a fruit classifier, the labels will be “this is an orange, this is an apple and this is a banana”, based on showing the classifier examples of apples, oranges and bananas.

Unsupervised learning is a type of machine learning algorithm used to draw inferences from datasets consisting of input data without labelled responses.

If you have n features in your training data set, SVM tries to plot it in n-dimensional space with the value of each feature being the value of a particular coordinate.

In the diagram, we see that the thinner lines mark the distance from the classifier to the closest data points called the support vectors (darkened data points).

decision tree is built top-down from a root node and involve partitioning of data into homogenious subsets.ID3uses enteropy to check the homogeneity of a sample.

Pruningis a technique in machine learning and search algorithms that reduces the size ofdecision treesby removing sections of thetreethat provide little power to classify instances.

For example, if you want to predict whether a particular political leader will win the election or not.In this case, the outcome of prediction is binary i.e.

The predictor variables here would be the amount of money spent for election campaigning of a particular candidate, the amount of time spent in campaigning, etc.

In Supervised machine learning algorithm, we have to train the model using labelled data set, While training we have to explicitly provide the correct labels and algorithm tries to learn the pattern from input to output.

Recommender Systemsare a subclass of information filtering systems that are meant to predict the preferences or ratings that a user would give to a product.Recommender systems are widely used in movies, news, research articles, products, social tags, music, etc.

The process of filtering used by most of the recommender systems to find patterns or information by collaborating viewpoints, various data sources and multiple agents.

Outlier values can be identified by using univariate or any other graphical analysis method.If the number of outlier values is few then they can be assessed individually but for a large number of outliers, the values can be substituted with either the 99th or the 1st percentile values.

All extreme values are not outlier values.The most common ways to treat outlier values The following are the various steps involved in an analytics project: The extent of the missing values is identified after identifying the variables with missing values.If any patterns are identified the analyst has to concentrate on them as it could lead to interesting and meaningful business insights.

If there are no patterns identified, then the missing values can be substituted with mean or median values (imputation) or they can simply be ignored.Assigning a default value which can be mean, minimum or maximum value.

defines the number of clusters.The objective of clustering is to group similar entities in a way that the entities within a group are similar to each other but the groups are different from each other.

Boosting Boosting is an iterative technique which adjusts the weight of an observation based on the last classification.If an observation was classified incorrectly, it tries to increase the weight of this observation and vice versa.

Deep Learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks.

Now although Deep Learning has been around for many years, the major breakthroughs from these techniques came just in recent years.This is because of two main reasons: GPUs are multiple times faster and they help us build bigger and deeper deep learning models in comparatively less time than we required previously.

They are inspired by biological neural networks.Neural Networkscan adapt to changing the input so the network generates the best possible result without needing to redesign the output criteria.

If the learning rate is set too high, this causes undesirable divergent behaviour to the loss function due to drastic updates in weights.

It performs down-sampling operations to reduce the dimensionality and creates a pooled feature map by sliding a filter matrix over the input matrix.

RNNs are a type of artificial neural networks designed to recognise the pattern from the sequence of data such as Time series, stock market and government agencies etc.

Both these networks RNN and feed-forward named after the way they channel information through a series of mathematical orations performed at the nodes of the network.

The decision a recurrent neural network reached at time t-1 affects the decision that it will reach one moment later at time t.

So recurrent networks have two sources of input, the present and the recent past, which combine to determine how they respond to new data, much as we do in life.

Long-Short-Term Memory (LSTM) is a special kind of recurrent neural network capable of learning long-term dependencies, remembering information for long periods as its default behaviour.

A single layer perceptron can classify only linear separable classes with binary output (0,1), but MLP can classify nonlinear classes.

This means the input layers, the data coming in, and the activation function is based upon all nodes and weights being added together, producing the output.

While training an RNN, if you seeexponentially growing (very large) error gradients which accumulate and result in very large updates to neural network model weights during training, they’re known as exploding gradients.

Boltzmann machines have a simple learning algorithm that allows them to discover interesting features that represent complex regularities in the training data.

Dropout is a technique of dropping out hidden and visible units of a network randomly to prevent overfitting of data (typically dropping 20 per cent of the nodes).

Batch normalization is the technique to improve the performance and stability of neural networks by normalizing the inputs in every layer so that they have mean output activation of zero and standard deviation of one.

The forger will try different techniques to sell fake wine and make sure specific techniques go past the shop owner’s check.

The forger’s goal is to create wines that are indistinguishable from the authentic ones while the shop owner intends to tell if the wine is real or not accurately Let us understand this example with the help of an image.

So, there are two primary components of Generative Adversarial Network (GAN) named: The generator is a CNN that keeps keys producing images and is closer in appearance to the real images while the discriminator tries to determine the difference between real and fake images The ultimate aim is to make the discriminator learn to identify real and fake images.

This is as good a place as any to ask this I suppose. Google results being as m... | Hacker News

>"hit something of a brick wall decades ago"

Sure, ML has failures - but those failures are in applications and fields where old school symbolic AI can't even reasonably be applied to.

A lot of these papers have been solely focused on understandable/explainable machine learning which is an overarching topic that covers all your questions.

But I'm pretty sure there are plenty of researchers out there who are far more of an expert than you are in this field would wholly disagree with you.

Most Frequently Asked Artificial Intelligence Interview Questions

Ever since we realized how Artificial Intelligence is positively impacting the market, nearly every large business is on the lookout for AI professionals to help them make their vision a reality.

In case you have attended any Artificial Intelligence interview in the recent past, do paste those interview questions in the comments section and we’ll answer them at the earliest.

It is the science of getting computers to act by feeding them data and letting them learn a few tricks on their own, without being explicitly programmed to do so.

Edureka In the above state diagram, the Agent(a0) was in State (s0) and on performing an Action (a0), which resulted in receiving a Reward (r1) and thus being updated to State (s1).

On the occurrence of an event, Bayesian Networks can be used to predict the likelihood that any one of several possible known causes was the contributing factor.

In artificial intelligence (AI), a Turing Test is a method of inquiry for determining whether or not a computer is capable of thinking like a human being.

Edureka To briefly sum it up, the agent must take an action (A) to transition from the start state to the end state (S).

The series of actions taken by the agent, define the policy (π) and the rewards collected define the value (V).

Initially, only the next possible node is visible to you, thus you randomly start off and then learn as you traverse through the network.The main goal is to choose the path with the lowest cost.

Edureka Grid Search Grid search trains the network for every combination by using the two set of hyperparameters, learning rate and the number of layers.

For example, instead of checking all 10,000 samples, randomly selected 100 parameters can be checked.

Cross-validation: The idea behind cross-validation is to split the training data in order to generate multiple mini train-test splits.

More training data: Feeding more data to the machine learning model can help in better analysis and classification.

Remove features: Many times, the data set contains irrelevant features or predictor variables that are not needed for analysis.

Early stopping:A machine learning model is trained iteratively, this allows us to check how well each iteration of the model performs.

For example, pruning is performed on decision trees, the dropout technique is used on neural networks and parameter tuning can also be applied to solve overfitting issues.

Use Ensemble models: Ensemble learning is a technique that is used to create multiple Machine Learning models, which are then combined to produce more accurate results.

A value too low will result in a minimal effect and a value too high results in under-learning by the network.

Edureka Natural Language Understanding includes: Natural Language Generation includes: Stemming algorithms work by cutting off the end or the beginning of the word, taking into account a list of common prefixes and suffixes that can be found in an inflected word.

Image Smoothing is one of the best methods used for reducing noise by forcing pixels to be more like their neighbors, this reduces any distortions caused by contrasts.

Edureka “In the context of artificial intelligence(AI) and deep learning systems, game theory is essential to enable some of the key capabilities required in multi-agent environments in which different AI programs need to interact or compete in order to accomplish a goal.”

Edureka Minimax is a recursive algorithm used to select an optimal move for a player assuming that the other player is also playing optimally.

game can be defined as a search problem with the following components: There are two players involved in a game: The following approach is taken for a Tic-Tac-Toe game using the Minimax algorithm: Step 1: First, generate the entire game tree starting with the current position of the game all the way up to the terminal states.

To summarize, Minimax Decision = MAX{MIN{3,5,10},MIN{2,2}} = MAX{3,2} = 3 Alpha-beta Pruning If we apply alpha-beta pruning to a standard minimax algorithm, it returns the same move as the standard one, but it removes all the nodes that are possibly not affecting the final decision.

Edureka In this case, Minimax Decision = MAX{MIN{3,5,10}, MIN{2,a,b}, MIN{2,7,3}} = MAX{3,c,2} = 3 Hint: (MIN{2,a,b} would certainly be less than or equal to 2, i.e., c<=2 and hence MAX{3,c,2} has to be 3.) Facebook uses DeepFace for face verification.

It works on the face verification algorithm, structured by Artificial Intelligence (AI) techniques using neural network models.

Process: In modern face recognition, the process completes in 4 raw steps: Output: Final result is a face representation, which is derived from a 9-layer deep neural net Training Data: More than 4 million facial images of more than 4000 people Result: Facebook can detect whether the two images represent the same person or not Target Marketing involves breaking a market into segments &

concentrating it on a few key segments consisting of the customers whose needs and desires most closely match your product.

The beauty of target marketing is that by aiming your marketing efforts at specific groups of consumers it makes the promotion, pricing, and distribution of your products and/or services easier and more cost-effective.

Edureka The following approach is followed for detecting fraudulent activities: Data Extraction: At this stage data is either collected through a survey or web scraping is performed.

Any inconsistencies or missing values may lead to wrongful predictions, therefore such inconsistencies must be dealt with at this step.

For example, if a person has spent an unusual sum of money on a particular day, the chances of a fraudulent occurrence are very high.

Nearest Neighbour is a Supervised Learning algorithm that classifies a new data point into the target class, depending on the features of its neighboring data points.

Edureka The following steps can be carried out to predict whether a loan must be approved or not: Data Extraction: At this stage data is either collected through a survey or web scraping is performed.

Building a Machine Learning model: There are n number of machine learning algorithms that can be used for predicting whether an applicant loan request is approved or not.

Edureka Here, Therefore, by using the Linear Regression model, wherein Y-axis represents the sales and X-axis denotes the time period, we can easily predict the sales for the upcoming months.

Collaborative filtering is the process of comparing users with similar shopping behaviors in order to recommend products to a new user with similar shopping behavior.

He does not buy the coke, but Amazon recommends a bottle of coke to user B since his shopping behaviors and his lifestyle is quite similar to user A.

By understanding such correlations between items, companies can grow their businesses by giving relevant offers and discount codes on such items.

Edureka In the above figure: This problem can be solved by using the Q-Learning algorithm, which is a reinforcement learning algorithm used to solve reward based problems.

Suppose, the Agent traverses from room 2 to room5, then the following path is taken: Next, we can put the state diagram and the instant reward values into a reward table or a matrix R, like so:

The formula to calculate the Q matrix: Q(state, action) = R(state, action) + Gamma * Max [Q(next state, all actions)] Here, Q(state, action) and R(state, action) represent the state and action in the Reward matrix R and the Memory matrix Q.

Edureka This sounds complex, let me break it down into steps: Image Acquisition: The sample images are collected and stored as an input database.

Image Pre-processing: Image pre-processing includes the following: Image Segmentation: It is the process of partitioning a digital image into multiple segments so that image analysis becomes easier.

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