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18,600 | from the above outputwe can see that the first data instance is malignant tumor the radius of which is + step organizing data into sets in this stepwe will divide our data into two parts namely training set and test set splitting the data into these sets is very important because we have to test our model on the unseen... |
18,601 | the above series of and are the predicted values for the tumor classes malignant and benign nowby comparing the two arrays namely test_labels and predswe can find out the accuracy of our model we are going to use the accuracy_score(function to determine the accuracy consider the following command for thisfrom sklearn m... |
18,602 | by using the abovewe are going to build naive bayes machine learning model to use the tumor information to predict whether or not tumor is malignant or benign to begin withwe need to install the sklearn module it can be done with the help of the following commandimport sklearn nowwe need to import the dataset named bre... |
18,603 | - - - - - - + - + + - - - - - - + + + + - - - - - - from the above outputwe can see that the first data instance is malignant tumor the main radius of which is + for testing our model on unseen datawe need to split our data into training and testing data it can be done with the help of the following codefrom sklearn mo... |
18,604 | the above series of and are the predicted values for the tumor classes malignant and benign nowby comparing the two arrays namely test_labels and predswe can find out the accuracy of our model we are going to use the accuracy_score(function to determine the accuracy consider the following commandfrom sklearn metrics im... |
18,605 | dataset we will use the iris dataset which contains classes of instances eachwhere each class refers to type of iris plant each instance has the four features namely sepal lengthsepal widthpetal length and petal width the svm classifier to predict the class of the iris plant based on features is shown below kernel it i... |
18,606 | we need to create the svm classifier object svc_classifier svm_classifier svc(kernel='linear' =cdecision_function_shape='ovr'fit(xyz svc_classifier predict(x_plotz reshape(xx shapeplt figure(figsize=( )plt subplot( plt contourf(xxyyzcmap=plt cm tab alpha= plt scatter( [: ] [: ] =ycmap=plt cm set plt xlabel('sepal lengt... |
18,607 | keeper to predict the likelihood occurrencei buying play station or not the logistic function is the sigmoid curve that is used to build the function with various parameters prerequisites before building the classifier using logistic regressionwe need to install the tkinter package on our system it can be installed fro... |
18,608 | with the help of following codewe can run the classifier on the mesh gridoutput classifier predict(np c_[x_vals ravel()y_vals ravel()]output output reshape(x_vals shapeplt figure(plt pcolormesh(x_valsy_valsoutputcmap=plt cm grayplt scatter( [: ] [: ] =ys= edgecolors='black'linewidth= cmap=plt cm pairedthe following lin... |
18,609 | decision tree classifier decision tree is basically binary tree flowchart where each node splits group of observations according to some feature variable herewe are building decision tree classifier for predicting male or female we will take very small data set having samples these samples would consist of two features... |
18,610 | we can visualize the decision tree with the help of the following python codedot_data tree export_graphviz(clf,feature_names=data_feature_names,out_file=none,filledtrue,rounded=truegraph pydotplus graph_from_dot_data(dot_datacolors ('orange''yellow'edges collections defaultdict(listfor edge in graph get_edge_list():edg... |
18,611 | we can change the values of features in prediction to test it random forest classifier as we know that ensemble methods are the methods which combine machine learning models into more powerful machine learning model random foresta collection of decision treesis one of them it is better than single decision tree because... |
18,612 | n_features cancer data shape[ plt barh(range(n_features),forest feature_importances_align='center'plt yticks(np arange(n_features),cancer feature_namesplt xlabel('feature importance'plt ylabel('feature'plt show(performance of classifier after implementing machine learning algorithmwe need to find out how effective the ... |
18,613 | actual predicted true positives (tpfalse positives (fpfalse negatives (fntrue negatives (tnconfusion matrix in the confusion matrix above is for positive class and is for negative class following are the terms associated with confusion matrixtrue positivestps are the cases when the actual class of data point was and th... |
18,614 | specificity it may be defined as how many of the negatives do the model return it is exactly opposite to recall following is the formula for calculating the specificity of the modelspecificity tn tn fp class imbalance problem class imbalance is the scenario where the number of observations belonging to one class is sig... |
18,615 | in this casewe are taking samples without replacement from non-fraud instances and then combine them with the fraud instancesnon-fraudulent observations after random under sampling of total observations after combining them with fraudulent observations + hence nowthe event rate for new dataset after under sampling the ... |
18,616 | ai with python supervised learningregression regression is one of the most important statistical and machine learning tools we would not be wrong to say that the journey of machine learning starts from regression it may be defined as the parametric technique that allows us to make decisions based upon data or in other ... |
18,617 | nowwe need to create linear regressor object reg_linearlinear_model linearregression(train the object with the training samples reg_linear fit(x_trainy_trainwe need to do the prediction with the testing data y_test_pred reg_linear predict(x_testnow plot and visualize the data plt scatter(x_testy_testcolor='red'plt plot... |
18,618 | print("explain variance score ="round(sm explained_variance_score(y_testy_test_pred) )print(" score ="round(sm _score(y_testy_test_pred) )output performance of linear regressormean absolute error mean squared error median absolute error explain variance score - score - in the above codewe have used this small data if y... |
18,619 | x_trainy_train [:training_samples] [:training_samplesx_testy_test [training_samples:] [training_samples:nowwe need to create linear regressor object reg_linear_mullinear_model linearregression(train the object with the training samples reg_linear_mul fit(x_trainy_trainnowat last we need to do the prediction with the te... |
18,620 | datapoint [[ ]poly_datapoint polynomial fit_transform(datapointpoly_linear_model linear_model linearregression(poly_linear_model fit(x_train_transformedy_trainprint("\nlinear regression:\ "reg_linear_mul predict(datapoint)print("\npolynomial regression:\ "poly_linear_model predict(poly_datapoint)output linear regressio... |
18,621 | ai with python logic programming in this we will focus logic programming and how it helps in artificial intelligence we already know that logic is the study of principles of correct reasoning or in simple words it is the study of what comes after what for exampleif two statements are true then we can infer any third st... |
18,622 | installing useful packages for starting logic programming in pythonwe need to install the following two packageskanren it provides us way to simplify the way we made code for business logic it lets us express the logic in terms of rules and facts the following command will help you install kanrenpip install kanren symp... |
18,623 | it is compulsory to define variablesthis can be done as followsab var(' ')var(' 'we need to match the expression with the original pattern we have the following original patternwhich is basically ( + )*boriginal_pattern (mul(add )bwe have the following two expressions to match with the original patternexp (mul (add )ex... |
18,624 | return condeseq([(eq, , )for in map(primeit count( ))elsereturn success if isprime(xelse fail nowwe need to declare variable which will be usedx var(print((set(run( , ,(membero, ,( , , , , , , , , , , , , , , , ),(prime_check, ))))print((run( , ,prime_check( )))the output of the above code will be as follows{ ( solving... |
18,625 | we are solving it for the question who owns zebra with the help of python let us import the necessary packagesfrom kanren import from kanren core import lall import time nowwe need to define two functions left(and next(to check whose house is left or next to who' housedef left(qplist)return membero(( , )zip(listlist[ :... |
18,626 | (var()var()var()'cats'var())houses)(next,(var()'dunhill'var()var()var())(var()var()var()'horse'var())houses)(membero,(var()'blue master''beer'var()var())houses)(membero,('german''prince'var()var()var())houses)(next,('norwegian'var()var()var()var())(var()var()var()var()'blue')houses)(next,(var()'blend'var()var()var())(v... |
18,627 | ai with python unsupervised learningaiclustering unsupervised machine learning algorithms do not have any supervisor to provide any sort of guidance that is why they are closely aligned with what some call true artificial intelligence in unsupervised learningthere would be no correct answer and no teacher for the guida... |
18,628 | step we need to specify the desired number of subgroups step fix the number of clusters and randomly assign each data point to cluster or in other words we need to classify our data based on the number of clusters in this stepcluster centroids should be computed as this is an iterative algorithmwe need to update the lo... |
18,629 | herewe are initializing kmeans to be the kmeans algorithmwith the required parameter of how many clusters (n_clusterskmeans kmeans(n_clusters= we need to train the -means model with the input data kmeans fit(xy_kmeans kmeans predict(xplt scatter( [: ] [: ] =y_kmeanss= cmap='viridis'centers kmeans cluster_centers_ the c... |
18,630 | mean shift algorithm it is another popular and powerful clustering algorithm used in unsupervised learning it does not make any assumptions hence it is non-parametric algorithm it is also called hierarchical clustering or mean shift cluster analysis followings would be the basic steps of this algorithmfirst of allwe ne... |
18,631 | from sklearn datasets samples_generator import make_blobs we can visualize the dataset with the following codecenters [[ , ],[ , ],[ , ]x_ make_blobs(n_samples centers centerscluster_std plt scatter( [:, ], [:, ]plt show(nowwe need to train the mean shift cluster model with the input data ms meanshift(ms fit(xlabels ms... |
18,632 | [ ]estimated clusters the code given below will help plot and visualize the machine' findings based on our dataand the fitment according to the number of clusters that are to be found colors *[' ',' ',' ',' ',' ',' ',' 'for in range(len( ))plt plot( [ ][ ] [ ][ ]colors[labels[ ]]markersize plt scatter(cluster_centers[:... |
18,633 | measuring the clustering performance the real world data is not naturally organized into number of distinctive clusters due to this reasonit is not easy to visualize and draw inferences that is why we need to measure the clustering performance as well as its quality it can be done with the help of silhouette analysis s... |
18,634 | from sklearn datasets samples_generator import make_blobs xy_true make_blobs(n_samples= centers= cluster_std= random_state= initialize the variables as shownscores [values np arange( we need to iterate the -means model through all the values and also need to train it with the input data for num_clusters in valueskmeans... |
18,635 | finding nearest neighbors if we want to build recommender systems such as movie recommender system then we need to understand the concept of finding the nearest neighbors it is because the recommender system utilizes the concept of nearest neighbors the concept of finding nearest neighbors may be defined as the process... |
18,636 | nowwe need to build the nearest neighbor the object also needs to be trainedknn_model nearestneighbors(n_neighbors=kalgorithm='auto'fit(xdistancesindices knn_model kneighbors([test_data]nowwe can print the nearest neighbors as followsprint("\nk nearest neighbors:"for rankindex in enumerate(indices[ ][: ]start= )print(s... |
18,637 | output nearest neighbors is is is -nearest neighbors classifier -nearest neighbors (knnclassifier is classification model that uses the nearest neighbors algorithm to classify given data point we have implemented the knn algorithm in the last sectionnow we are going to build knn classifier using that algorithm concept ... |
18,638 | example we are building knn classifier to recognize digits for thiswe will use the mnist dataset we will write this code in the jupyter notebook import the necessary packages as shown below here we are using the kneighborsclassifier module from the sklearn neighbors packagefrom sklearn datasets import import pandas as ... |
18,639 | image_display( image of is displayed as followsdigit keys(nowwe need to create the training and testing data set and supply testing data set to the knn classifiers train_xdigit['data'][: train_y digit['target'][: knn kneighborsclassifier( knn fit(train_x,train_ythe following output will create the nearest neighbor clas... |
18,640 | image_display( image of is displayed as followsnow we will predict the test data as followsknn predict(test the above code will generate the following outputarray([ ]nowconsider the followingdigit['target_names'the above code will generate the following outputarray([ ] |
18,641 | ai with python natural language processing natural language processing (nlprefers to ai method of communicating with intelligent systems using natural language such as english processing of natural language is required when you want an intelligent system like robot to perform as per your instructionswhen you want to he... |
18,642 | syntax level ambiguity sentence can be parsed in different ways for example"he lifted the beetle with red cap did he use cap to lift the beetle or he lifted beetle that had red capreferential ambiguity referring to something using pronouns for examplerima went to gauri she said" am tired exactly who is tirednlp termino... |
18,643 | discourse integration the meaning of any sentence depends upon the meaning of the sentence just before it in additionit also brings about the meaning of immediately succeeding sentence pragmatic analysis during thiswhat was said is re-interpreted on what it actually meant it involves deriving those aspects of language ... |
18,644 | in this we will learn how to get started with the natural language toolkit package prerequisite if we want to build applications with natural language processing then the change in context makes it most difficult the context factor influences how the machine understands particular sentence hencewe need to develop natur... |
18,645 | installing other necessary packages for building natural language processing applications by using nltkwe need to install the necessary packages the packages are as followsgensim it is robust semantic modeling library that is useful for many applications we can install it by executing the following commandpip install g... |
18,646 | word_tokenize package this package divides the input text into words we can import this package with the help of the following python codefrom nltk tokenize import word_tokenize wordpuncttokenizer package this package divides the input text into words as well as the punctuation marks we can import this package with the... |
18,647 | snowballstemmer package this python package will use the snowball' algorithm to extract the base form we can import this package with the help of the following python codefrom nltk stem snowball import snowballstemmer for exampleif we will give the word 'writingas the input to this stemmer then we will get the word 'wr... |
18,648 | types of chunking there are two types of chunking the types are as followschunking up in this process of chunkingthe objectthingsetc move towards being more general and the language gets more abstract there are more chances of agreement in this processwe zoom out for exampleif we will chunk up the question that "for wh... |
18,649 | the parser parses the sentence as followsparser_chunking parse(sentencenextwe need to get the output the output is generated in the simple variable called output_chunk output_chunk=parser_chunking parse(sentenceupon execution of the following codewe can draw our output in the form of tree output draw(bag of word (bowmo... |
18,650 | weighted combination of various words by setting the threshold and choosing the words that are more meaningfulwe can build histogram of all the words in the documents that can be used as feature vector following is an example to understand the concept of document term matrixexample suppose we have the following two sen... |
18,651 | inverse document frequency(idfit is the measure of how unique word is to this document in the given set of documents for calculating idf and formulating distinctive feature vectorwe need to reduce the weights of commonly occurring words like the and weigh up the rare words building bag of words model in nltk in this se... |
18,652 | from sklearn datasets import fetch_ newsgroups from sklearn naive_bayes import multinomialnb from sklearn feature_extraction text import tfidftransformer from sklearn feature_extraction text import countvectorizer define the category map we are using five different categories named religionautossportselectronics and sp... |
18,653 | transform the input data using the count vectorizerinput_tc vectorizer_count transform(input_datanowwe will transform the vectorized data using the tfidf transformerinput_tfidf tfidf transform(input_tcwe will predict the output categoriespredictions classifier predict(input_tfidfthe output is generated as followsfor se... |
18,654 | the classifier we will be using the labeled data from the scikit-learn package following is the python code to build gender finderlet us import the necessary packagesimport random from nltk import naivebayesclassifier from nltk classify import accuracy as nltk_accuracy from nltk corpus import names now we need to extra... |
18,655 | train_datatest_data features[:train_sample]features[train_sample:classifier naivebayesclassifier train(train_datathe accuracy of the classifier can be computed as followsaccuracy_classifier round( nltk_accuracy(classifiertest_data) print('accuracy str(accuracy_classifier'%'nowwe can predict the outputfor name in namesi... |
18,656 | gaurav -female swati -female shubha -female number of end letters accuracy rajesh -female gaurav -female swati -female shubha -female in the above outputwe can see that accuracy in maximum number of end letters are two and it is decreasing as the number of end letters are increasing topic modelingidentifying patterns i... |
18,657 | non-negative matrix factorization (nmfit is also based upon linear algebra all of the above mentioned algorithms for topic modeling would have the number of topics as parameterdocument-word matrix as an input and wtm (word topic matrixtdm (topic document matrixas output |
18,658 | ai with python analyzing time series data predicting the next in given input sequence is another important concept in machine learning this gives you detailed explanation about analyzing time series data introduction time series data means the data that is in series of particular time intervals if we want to build sequ... |
18,659 | hmmlearn it is an open source bsd-licensed library which consists of simple algorithms and models to learn hidden markov models(hmmin python you can install it with the help of the following commandpip install hmmlearn if you are using anaconda and want to install by using the conda package managerthen you can use the ... |
18,660 | example the following example shows you handling and slicing the time series data by using pandas note that here we are using the monthly arctic oscillation datawhich can be downloaded from current ascii and can be converted to text format for our use handling time series data for handling time series datayou will have... |
18,661 | timeseries read_data(input_filefinallyplot and visualize the datausing the commands shownplt figure(timeseries plot(plt show(you will observe the plots as shown in the following images |
18,662 | slicing time series data slicing involves retrieving only some part of the time series data as part of the examplewe are slicing the data only from to observe the following code that performs this tasktimeseries[' ':' 'plot(plt show( |
18,663 | when you run the code for slicing the time series datayou can observe the following graph as shown in the image hereextracting statistic from time series data you will have to extract some statistics from given datain cases where you need to draw some important conclusion meanvariancecorrelationmaximum valueand minimum... |
18,664 | minimum you can use the min(functionfor finding minimumas shown heretimeseries min(then the output that you will observe for the example discussed is- getting everything at once if you want to calculate all statistics at timeyou can use the describe(function as shown heretimeseries describe(then the output that you wil... |
18,665 | thenyou can observe the following graph as the output of resampling using mean()re-sampling with median(you can use the following code to resample the data using the median()methodtimeseries_mm timeseries resample(" "median(timeseries_mm plot(plt show( |
18,666 | thenyou can observe the following graph as the output of re-sampling with median()rolling mean you can use the following code to calculate the rolling (movingmeantimeseries rolling(window= center=falsemean(plot(style='- 'plt show(thenyou can observe the following graph as the output of the rolling (movingmean |
18,667 | analyzing sequential data by hidden markov model (hmmhmm is statistic model which is widely used for data having continuation and extensibility such as time series stock market analysishealth checkupand speech recognition this section deals in detail with analyzing sequential data using hidden markov model (hmmhidden m... |
18,668 | prior probability matrix (pit is the probability of starting at particular state from various states of the system it is denoted by hencea hmm may be defined as (soab)wheres { snis set of possible stateso { omis set of possible observation symbolsa is an nxn state transition probability matrix (tpm) is an nxm observati... |
18,669 | in this stepwe will extract the closing quotes every day for thisuse the following commandclosing_quotes np array([quote[ for quote in quotes]nowwe will extract the volume of shares traded every day for thisuse the following commandvolumes np array([quote[ for quote in quotes])[ :heretake the percentage difference of c... |
18,670 | plt show( |
18,671 | ai with python speech recognition in this we will learn about speech recognition using ai with python speech is the most basic means of adult human communication the basic goal of speech processing is to provide an interaction between human and machine speech processing system has mainly three tasksfirstspeech recognit... |
18,672 | speaking stylea read speech may be in formal styleor spontaneous and conversational with casual style the latter is harder to recognize speaker dependencyspeech can be speaker dependentspeaker adaptiveor speaker independent speaker independent is the hardest to build type of noisenoise is another factor to consider whi... |
18,673 | import the necessary packages as shown hereimport numpy as np import matplotlib pyplot as plt from scipy io import wavfile nowread the stored audio file it will return two valuesthe sampling frequency and the audio signal provide the path of the audio file where it is storedas shown herefrequency_samplingaudio_signal w... |
18,674 | signal shape( ,signal datatypeint signal duration seconds characterizing the audio signaltransforming to frequency domain characterizing an audio signal involves converting the time domain signal into frequency domainand understanding its frequency componentsby this is an important step because it gives lot of informat... |
18,675 | in this stepwe will display the parameters like sampling frequency of the audio signaldata type of signal and its durationusing the commands given belowprint('\nsignal shape:'audio_signal shapeprint('signal datatype:'audio_signal dtypeprint('signal duration:'round(audio_signal shape[ float(frequency_sampling) )'seconds... |
18,676 | x_axis np arange( len_half (frequency_sampling length_signal nowvisualize the characterization of signal as followsplt figure(plt plot(x_axissignal_powercolor='black'plt xlabel('frequency (khz)'plt ylabel('signal power (db)'plt show(you can observe the output graph of the above code as shown in the image belowgeneratin... |
18,677 | import matplotlib pyplot as plt from scipy io wavfile import write provide the file where the output file should be savedoutput_file 'audio_signal_generated wavnowspecify the parameters of your choiceas shownduration in seconds frequency_sampling in hz frequency_tone min_val - np pi max_val np pi in this stepwe can gen... |
18,678 | you can observe the plot as shown in the figure given herefeature extraction from speech this is the most important step in building speech recognizer because after converting the speech signal into the frequency domainwe must convert it into the usable form of feature vector we can use different feature extraction tec... |
18,679 | nowread the stored audio file it will return two valuesthe sampling frequency and the audio signal provide the path of the audio file where it is stored frequency_samplingaudio_signal wavfile read("/users/admin/audio_file wav"note that here we are taking first samples for analysis audio_signal audio_signal[: use the mf... |
18,680 | as result of the steps aboveyou can observe the following outputsfigure for mfcc and figure for filter bank recognition of spoken words speech recognition means that when humans are speakinga machine understands it here we are using google speech api in python to make it happen we need to install the following packages... |
18,681 | speechrecognitionthis package can be installed by using pip install speechrecognition google-speech-apiit can be installed by using the command pip install google-api-python-client example observe the following example to understand about recognition of spoken wordsimport the necessary packages as shownimport speech_re... |
18,682 | you can see the following outputplease say somethingyou saidfor exampleif you said tutorialspoint comthen the system recognizes it correctly as followstutorialspoint com |
18,683 | heuristic search plays key role in artificial intelligence in this you will learn in detail about it concept of heuristic search in ai heuristic is rule of thumb which leads us to the probable solution most problems in artificial intelligence are of exponential nature and have many possible solutions you do not know ex... |
18,684 | real world problem solved by constraint satisfaction the previous sections dealt with creating constraint satisfaction problems nowlet us apply this to real world problems too some examples of real world problems solved by constraint satisfaction are as followssolving algebraic relation with the help of constraint sati... |
18,685 | nowcreate the object of getsolution(module using the following commandsolutions problem getsolutions(lastlyprint the output using the following commandprint (solutionsyou can observe the output of the above program as follows[{' ' ' ' }{' ' ' ' }{' ' ' ' }{' ' ' ' }{' ' ' ' }magic square magic square is an arrangement ... |
18,686 | if len(set(sum_list))> return false return true nowgive the value of the matrix and check the outputprint(magic_square([[ , , ][ , , ][ , , ]])you can observe that the output would be false as the sum is not up to the same number print(magic_square([[ , , ][ , , ][ , , ]])you can observe that the output would be true a... |
18,687 | ai with python games are played with strategy every player or team would make strategy before starting the game and they have to change or build new strategy according to the current situation(sin the game search algorithms you will have to consider computer games also with the same strategy as above note that search a... |
18,688 | alpha-beta pruning major issue with minimax algorithm is that it can explore those parts of the tree that are irrelevantleads to the wastage of resources hence there must be strategy to decide which part of the tree is relevant and which is irrelevant and leave the irrelevant part unexplored alpha-beta pruning is one s... |
18,689 | self players players self nplayer nowdefine the number of coins in the gamehere we are using coins for the game self num_coins define the maximum number of coins player can take in move self max_coins now there are some certain things to define as shown in the following code define possible moves def possible_moves(sel... |
18,690 | lastcoin_game ttentry lambda selfself num_coins solving the game with the following code blockrdm id_solve(lastcoin_gamerange( )win_score= tt=ttprint(rdmdeciding who will start the game game lastcoin_game([ai_player(tt)human_player()]game play(you can find the following output and simple play of this gamed: : : : : : :... |
18,691 | bot to play tic tac toe tic-tac-toe is very familiar and one of the most popular games let us create this game by using the easyai library in python the following code is the python code of this gameimport the packages as shownfrom easyai import twoplayersgameai_playernegamax from easyai player import human_player inhe... |
18,692 | return any([all([(self board[ - =self nopponentfor in combination]for combination in possible_combinations]define check for the finish of game def is_over(self)return (self possible_moves(=[]or self condition_for_lose(show the current position of the players in the game def show(self)print('\ '+'\njoin([join([['' '' ']... |
18,693 | player what do you play move # player plays move # player plays player what do you play move # player plays move # player plays |
18,694 | ai with python neural networks neural networks are parallel computing devices that are an attempt to make computer model of brain the main objective behind is to develop system to perform various computational task faster than the traditional systems these tasks include pattern recognition and classificationapproximati... |
18,695 | perceptron based classifier perceptrons are the building blocks of ann if you want to know more about perceptronyou can follow the linkrvised_learning htm following is stepwise execution of the python code for building simple neural network perceptron based classifierimport the necessary packages as shownimport matplot... |
18,696 | you can see the following graph showing the training progress using the error metricsingle layer neural networks in this examplewe are creating single layer neural network that consists of independent neurons acting on input data to produce the output note that we are using the text file named neural_simple txt as our ... |
18,697 | [ ][ ][ ][ ][ ]]nowseparate these four columns into data columns and labelsdata input_data[: : labels input_data[: :plot the input data using the following commandsplt figure(plt scatter(data[:, ]data[:, ]plt xlabel('dimension 'plt ylabel('dimension 'plt title('input data'nowdefine the minimum and maximum values for ea... |
18,698 | plt ylabel('training error'plt title('training error progress'plt grid(plt show(nowuse the test data-points in above classifierprint('\ntest results:'data_test [[ ][ ][ ],[ ]for item in data_testprint(item'-->'neural_net sim([item])[ ]you can find the test results as shown here[ --[ [ --[ [ --[ [ --[ |
18,699 | you can see the following graphs as the output of the code discussed till nowmulti-layer neural networks in this examplewe are creating multi-layer neural network that consists of more than one layer to extract the underlying patterns in the training data this multilayer neural network will work like regressor we are g... |
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