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the predictive power of the model when the data itself possesses a great deal of complexity that needs to be captured. • minimum description length ( mdl ) pruning is a post - pruning method that seeks to find the least complex form of a decision tree that meets an acceptable measure of accuracy. in general, there is a... | openstax_principles-of-data-science-web | [
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6. 4 • decision trees 319 import matplotlib. pyplot as plt # read data file data = pd. read _ csv ('redblue. csv') inputs = data [ ['x ','y'] ] output = data ['label'] # train decision tree classifier clf = decisiontreeclassifier ( max _ depth = 3 ) clf = clf. fit ( inputs, output ) # plot tree with customization plt. ... | openstax_principles-of-data-science-web | [
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6. 5 other machine learning techniques learning outcomes by the end of this section, you should be able to : • 6. 5. 1 discuss the concept of a random forest as a bootstrapping method for decision trees. • 6. 5. 2 create a random forest model and use it to classify data. • 6. 5. 3 define conditional probability and exp... | openstax_principles-of-data-science-web | [
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then our decision tree may have come out rather differently. in the real world, obtaining a sufficient amount of data to train a machine learning model may be difficult, time - consuming, and expensive. it may not be practical to go out and find additional training sets just to create and evaluate a variety of decision... | openstax_principles-of-data-science-web | [
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csv ( https : / / openstax. org / r / temps ). since there are a lot of input features, it will not be possible to visualize the data. moreover, some features are likely not to contribute much to the predictions. how do we sort everything out? a random forest model may be just the right tool. the python library sklearn... | openstax_principles-of-data-science-web | [
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6. 5 • other machine learning techniques 321 # read input file features = pd. read _ csv ('temps. csv'). dropna ( ) # use'actual'as the response variable labels = features ['actual'] # convert text data into numerical values # this is called " one - hot " encoding features = pd. get _ dummies ( features ) # the other c... | openstax_principles-of-data-science-web | [
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, 0, 1, 0, 0, 0, 0 ] • thursday : [ 0, 0, 0, 1, 0, 0, 0 ] • friday : [ 0, 0, 0, 0, 1, 0, 0 ] • saturday : [ 0, 0, 0, 0, 0, 1, 0 ] • sunday : [ 0, 0, 0, 0, 0, 0, 1 ] the dataset is split into training ( 75 % ) and testing ( 25 % ) sets, and the random forest model is trained on the training set, as seen in this python c... | openstax_principles-of-data-science-web | [
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3. 91 with a mean absolute error of only 3. 91, predicted temperatures are only off by about 4°f on average, so the random forest seems to do a good job with the test set, given that it is notoriously difficult to predict weather data since it typically shows high variance. now let ’ s find out which features were most... | openstax_principles-of-data-science-web | [
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6. 5 • other machine learning techniques 323 variable : week _ sun importance : 0. 0 variable : week _ thurs importance : 0. 0 variable : week _ tues importance : 0. 0 variable : week _ wed importance : 0. 0 as we can see, the single best predictor of daily temperatures is the temperature on the previous day ( “ temp _... | openstax_principles-of-data-science-web | [
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, entertainment, and technology. you may have noticed certain patterns, such as sport articles tend to mention team names and give numerical scores, while articles about politics mention the words “ democrat ” and “ republican ” fairly often. business articles might use words such as “ outlook ” and “ downturn ” much m... | openstax_principles-of-data-science-web | [
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% of them are defective. a defective battery is delivered without information about its origin. what is the probability that it came from acme electronics? solution first, we find the probability that a delivered battery is defective. let,, and stand for the proportions of batteries from each of the companies, acme ele... | openstax_principles-of-data-science-web | [
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using the word counts found in the training data, find the conditional probabilities :,,,, now we will only use the numerator of bayes ’ formula, which is proportional to the exact probability. this | openstax_principles-of-data-science-web | [
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6. 5 • other machine learning techniques 325 will produce scores that we can compare. for part ( a ) : score : score : since the score for fake is greater than the score for real, we would classify the article as fake. for part ( b ), the process is analogous. note that probabilities are multiplied together when there ... | openstax_principles-of-data-science-web | [
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status, ” which may take the values for death, for censored ( meaning that the patient did not die during the observation period ), and cl for censored due to liver transplantation. # import libraries import pandas as pd # # for dataset management from sklearn. model _ selection import train _ test _ split from sklearn... | openstax_principles-of-data-science-web | [
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:. 2f } " ) print ( " confusion matrix with label order d, c, cl : \ n ", confusion ) the resulting output will look like this : accuracy : 0. 71 confusion matrix with label order d, c, cl : [ [ 10 14 2 ] [ 0 30 0 ] [ 0 0 0 ] ] the accuracy score of 0. 71 means that correct labels were assigned 71 % of the time. we can... | openstax_principles-of-data-science-web | [
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be streamed in real time or stored in huge databases for later analysis. one example is in the area of very high - definition video, which can be captured, stored, and | openstax_principles-of-data-science-web | [
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6. 5 • other machine learning techniques 327 analyzed at a rate of gigabits per hour. • variety : big data also encompasses diverse types of data, including structured data ( e. g., databases ), unstructured data ( e. g., text, images, videos ), and anything in between. this variety adds considerable complexity to data... | openstax_principles-of-data-science-web | [
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next step after data cleaning. common data mining tasks include clustering, classification, regression, and anomaly detection. for unlabeled data, unsupervised machine learning algorithms may be used to discover clusters in smaller samples of the data, which can then be assigned labels that would be used for more far -... | openstax_principles-of-data-science-web | [
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intricate patterns, have become increasingly important in big data applications. we will delve into this topic in deep learning and ai basics. • dimensionality reduction : techniques like principal component analysis ( pca ) help reduce the dimensionality of data, making it more manageable while retaining essential inf... | openstax_principles-of-data-science-web | [
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6. 5 • other machine learning techniques 329 key terms accuracy for machine learning in general, a measure of the correctness of a machine learning model with respect to predictions. bias error introduced by overly - simplistic or overly - rigid models that do not capture important features of the data big data extreme... | openstax_principles-of-data-science-web | [
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tree entropy measure of the average amount of information or uncertainty error - based ( reduced - error ) pruning pruning method that removes branches that do not significantly improve the overall accuracy of the decision tree f1 score combination of precision and recall. facial recognition application of machine lear... | openstax_principles-of-data-science-web | [
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accuracy multiclass ( multinomial ) classification classification of data into more than two categories multiple regression regression techniques that use more than one input variable naive bayes classification also known as multinomial naive bayes classification, a classification algorithm that makes use of prior prob... | openstax_principles-of-data-science-web | [
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classified or for which classification data is not known yet unsupervised learning machine learning methods that do not require data to be labeled in order to learn ; often, unsupervised learning is a first step in discovering meaningful clusters that will be used to define labels variance error due to an overly sensit... | openstax_principles-of-data-science-web | [
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testing data and different pruning techniques to find a model with the best accuracy. generate some data points with random gpas and in - state statuses and use your model to predict college completion on your new data. project c : predicting outcomes in liver disease patients analyze the dataset cirrhosis. csv ( https... | openstax_principles-of-data-science-web | [
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. gpa scores are measured on a different scale than either sat or act scores. c. scores of 0 are impossible to obtain on the sat or act. d. students can have high gpa but sometimes not do well on standardized tests like the sat or act. 3. using the data about words found in news articles from example 6. 12, classify an... | openstax_principles-of-data-science-web | [
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9. 79 table 6. 10 100 - meter spring records using software such as excel, python, or similar tools, the regression line can be found. for this data, the linear model would be, where is years since 1900. compute the mae, mape, mse, and rmse for this model. b. use the model to predict the world record fastest time for t... | openstax_principles-of-data-science-web | [
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0. 05 table 6. 11 distribution x values b. the binomial distribution with and. recall, the binomial distribution is defined by : references 1. emspak, j. ( 2016, december 29 ). how a machine learns prejudice. scientific american. https : / / www. scientificamerican. com / article / how - a - machine - learns - prejudic... | openstax_principles-of-data-science-web | [
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7. 5 natural language processing introduction how does the human brain learn new information? this question has puzzled scientists for ages. the field of neuroscience is dedicated to finding out how the brain functions, but there are still so many things that we do not know. one major advance was the discovery of how n... | openstax_principles-of-data-science-web | [
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to complete. the inability to process large datasets or perform complex calculations in a reasonable time frame greatly restricted the potential of ai. fast forward to the present — advancements in computational power and technology have dramatically transformed the landscape of ai. one prominent example of this transf... | openstax_principles-of-data-science-web | [
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7. 1 introduction to neural networks learning outcomes by the end of this section, you should be able to : • 7. 1. 1 define neural networks and discuss the types of problems for which they may be useful. • 7. 1. 2 summarize the roles of weights and biases in a neural network. • 7. 1. 3 construct a simple neural network... | openstax_principles-of-data-science-web | [
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may consist of hundreds, thousands, or even millions of neurons connected to each other in layers, or groups of neurons that all receive the same inputs from previous layers and forward signals in aggregate to the next layer. there are always at least two layers, the input layer ( containing neurons that accept the ini... | openstax_principles-of-data-science-web | [
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outliers in large, complex datasets. an example is microsoft ’ s azure anomaly detector ( https : / / openstax. org / r / azure ), which can detect anomalies in time series data. • autonomous vehicles and robotics, including tesla ’ s autopilot technology, are becoming more and more prominent as automation alleviates s... | openstax_principles-of-data-science-web | [
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7. 1 • introduction to neural networks 337 exploring further tensorflow if you want to get your feet wet with neural networks, check out this interactive web - based neural network tool, called tensorflow playground ( https : / / openstax. org / r / playground ), which uses tensorflow to train and update outputs in rea... | openstax_principles-of-data-science-web | [
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that is most activated would indicate the classification, as shown in figure 7. 4. figure 7. 4 output neurons. in this figure, there are four output neurons, labeled a, b, c, and d. since b has the highest activation level, the output of the neural network is b. each connection from one neuron to another has two parame... | openstax_principles-of-data-science-web | [
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that the neural network learns during the training process, a topic we will explain in backpropagation. a typical neural network may have hundreds or thousands of inputs for each neuron, and so the equation can be difficult to work with. it would be more convenient to regard all the inputs as parts of a single mathemat... | openstax_principles-of-data-science-web | [
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values have the same sign as, with a smooth transition through 0. 4. rectified linear unit ( relu ) function,. the relu function is 0 for negative values and equal to the input when is positive. | openstax_principles-of-data-science-web | [
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7. 1 • introduction to neural networks 339 5. leaky relu function,, for some small positive parameter. leaky relu acts much like relu except that the values get progressively more negative when gets more negative. often, the optimal “ leakiness ” parameter is determined during the training phase. 6. softplus function,,... | openstax_principles-of-data-science-web | [
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each part : a., since b. ( note, 0. 32 was already computed in part a. ) c., since ( note, 0. 32 was already computed in part a. ) d. in the next section, we explore neural networks that use the simplest of the activation functions, the step function. perceptrons although the idea of using structures modeled after biol... | openstax_principles-of-data-science-web | [
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immediately causing a large effect on input. however, with repeated training, the perceptron will learn the appropriate values of and to achieve the desired output. for example, suppose a perception with three neurons currently has weights and bias. on input, we get : suppose that the true output should have been. so t... | openstax_principles-of-data-science-web | [
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7. 1 • introduction to neural networks 341 on the same input, the perceptron now has a value of : in this simple example, the value changed from 1 to 0, eliminating the error. however, there is no guarantee that the perceptron will classify all input without error, regardless of the number of training steps that are ta... | openstax_principles-of-data-science-web | [
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example, the first 100 rows of data were selected for training and testing, using 75 % of the data for training and the remaining 25 % for testing. ( recall from decision - making using machine learning basics that it is good practice to use about 70 % – 80 % of the data for training, with the rest used for testing. ) ... | openstax_principles-of-data-science-web | [
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while the target for classifying not setosa is. this is reasonable as the values of approach as increases to positive infinity and as decreases to negative infinity. for example, if after training, the ideal weights were found to be, and the bias is, then the response on an input would be : since, classify the input as... | openstax_principles-of-data-science-web | [
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7. 1 • introduction to neural networks 343 # import the sklearn library ( specific modules ) from sklearn. datasets import load _ iris from sklearn. linear _ model import perceptron from sklearn. model _ selection import train _ test _ split from sklearn. preprocessing import standardscaler from sklearn. metrics import... | openstax_principles-of-data-science-web | [
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larger and less regular, we do not expect 100 % accuracy. there could also be significant overfitting in the model, which would lead to high variance when classifying data not found in the original dataset. here is how to use the perceptron model we just created to classify new data ( outside of the original dataset ) ... | openstax_principles-of-data-science-web | [
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7. 2 backpropagation learning outcomes by the end of this section, you should be able to : • 7. 2. 1 discuss the goal of adjusting weights and bias to reduce loss / error in a neural network. • 7. 2. 2 use static backpropagation to train a neural network. • 7. 2. 3 define recurrent neural networks and discuss their adv... | openstax_principles-of-data-science-web | [
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7. 2 • backpropagation 345 exploring further backpropagation a full treatment of backpropagation requires familiarity with matrix operations, calculus, and numerical analysis, among other things. such topics fall well outside the scope of this text, there are many resources online that go into more depth, such as neura... | openstax_principles-of-data-science-web | [
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neurons in that layer. indeed, is no longer just a vector in this context, but a matrix, which may be regarded as a table of numbers. finally, the activation function is applied to all entries of the vector input, resulting in a vector output. these details are not essential to the fundamental understanding of the proc... | openstax_principles-of-data-science-web | [
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346 7 • deep learning and ai basics access for free at openstax. org used in binary classification tasks — that is, when the output is either 0 or 1. ( note : the minus sign in front of the formula is there to make the overall value positive, as the values of the logarithms will generally be negative. the term entropy ... | openstax_principles-of-data-science-web | [
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7. 2 • backpropagation 347 data point 1 : data point 2 : average loss : c. hinge loss first, compute the values of. then if any of these are negative, you would use 0 instead in the sum. data point 1 :.... data point 2 :.... average loss : note : the fact that each of the three loss functions gives a different result m... | openstax_principles-of-data-science-web | [
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that a neural network has only one neuron with weight and bias. input is fed into the neuron, the weight and bias are applied to, and then an activation function is applied to the result to produce an output. we ’ ll use the sigmoid, as activation function for simplicity. now, the result,, is compared to the true value... | openstax_principles-of-data-science-web | [
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. if we label the direction of steepest decrease, then we could move from the point ( 0. 5, 0. 25 ) to a new point,, where is a small number that affects the learning rate of the neural network. this is the main idea behind gradient descent. in our example, we could find the direction of steepest descent by simply look... | openstax_principles-of-data-science-web | [
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7. 2 • backpropagation 349 figure 7. 8 simple application of gradient descent. the point has loss of. the arrow indicates the direction of steepest descent from the point on the surface. one final point to mention is that gradient descent only works when the function f is differentiable, meaning that there are no corne... | openstax_principles-of-data-science-web | [
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and an output layer with 10 neurons, one for each digit. there are no hidden layers in this model. the command tf. keras. layers. dense builds the output layer of 10 neurons that is fully connected to the input layer of 784 layers ( the value of x _ train _ scaled. shape [ 1 ] ). the softmax activation function is used... | openstax_principles-of-data-science-web | [
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_ test _ scaled = scaler. transform ( x _ test ) # cast the target variable as an integer ( int32 ) y _ train = y _ train. astype ('int32') y _ test = y _ test. astype ('int32') # define the neural network architecture model = tf. keras. sequential ( [ tf. keras. layers. dense ( 10, activation ='softmax ', input _ shap... | openstax_principles-of-data-science-web | [
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7. 2 • backpropagation 351 a stock and forecast the price of the stock tomorrow? then you could maximize your profits by buying low and selling high with full knowledge of when those high and low points in price would occur. given the extremely unpredictable nature of the stock market, it is unlikely that a simple fore... | openstax_principles-of-data-science-web | [
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into the neuron, it gets the extra signal of added to it. if the connecting weight is positive, then this generally causes the neuron to become more active over time. on the other hand, if the connecting weight is negative, then a negative feedback loop exists, which generally dampens the activity of the neuron over ti... | openstax_principles-of-data-science-web | [
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. ) unrolling an rnn the effect of feedback loops may be visualized by “ unrolling ” the rnn. consider the simplest case of a single feedback loop from one neuron to itself. the effect of the connecting weight is equivalent to connecting to a copy of the same rnn ( with identical weights and biases ). of course, since ... | openstax_principles-of-data-science-web | [
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##m ) network is a type of rnn designed to overcome the problems of exploding or vanishing gradients by incorporating memory cells that can capture long - term dependencies better than simple feedback loops can. lstms were introduced in 1997 and have since become widely used in various applications, including natural l... | openstax_principles-of-data-science-web | [
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7. 2 • backpropagation 353 rnns in python consider the dataset monthlycoalconsumption. csv ( https : / / openstax. org / r / datach7 ), which we analyzed using basic time series methods in time series and forecasting. the dataset contains observations up to the end of 2022. suppose you want to predict the monthly coal ... | openstax_principles-of-data-science-web | [
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x = np. array ( x ) y = np. array ( y ) # split data into training and testing sets split = int ( 0. 8 * len ( x ) ) x _ train, x _ test = x [ : split ], x [ split : ] y _ train, y _ test = y [ : split ], y [ split : ] # define the rnn architecture model = tf. keras. sequential ( [ tf. keras. layers. simplernn ( units ... | openstax_principles-of-data-science-web | [
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_ months ) : prediction = model. predict ( last _ window ) predicted _ values. append ( prediction [ 0, 0 ] ) last _ window = np. append ( last _ window [ :, 1 :, : ], prediction. reshape ( 1, 1, 1 ), axis = 1 ) # inverse transform the predicted births to get actual values predicted _ values = scaler. inverse _ transfo... | openstax_principles-of-data-science-web | [
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7. 2 • backpropagation 355 import matplotlib. pyplot as plt from matplotlib. ticker import maxnlocator # # for graph formatting from matplotlib. ticker import funcformatter # # for formatting y - axis # function to format the y - axis values def y _ format ( value, tick _ number ) : return f'{ value :,. 0f }'# plot ori... | openstax_principles-of-data-science-web | [
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= xticks _ labels, rotation = 45 ) # apply the formatter to the y - axis plt. gca ( ). yaxis. set _ major _ formatter ( funcformatter ( y _ format ) ) plt. show ( ) the resulting output will look like this : python code 356 7 • deep learning and ai basics access for free at openstax. org | openstax_principles-of-data-science-web | [
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7. 3 introduction to deep learning learning outcomes by the end of this section, you should be able to : • 7. 3. 1 discuss the role of hidden layers in a neural network. • 7. 3. 2 describe loss / error functions and their role in training and testing a neural network. • 7. 3. 3 set up, test, and train a deep learning n... | openstax_principles-of-data-science-web | [
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horizontal strokes to further assist in classifying the | openstax_principles-of-data-science-web | [
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7. 3 • introduction to deep learning 357 numeral. however, hidden layers do not necessarily pick up on the same kinds of patterns that humans would. a recent direction in deep learning is in developing models that are capable of extracting higher - level features ( e. g., loops, edges, textures ) and reporting reasons ... | openstax_principles-of-data-science-web | [
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mse is not scale - independent, meaning that the units of mse are not the same as the units of the data. the following example illustrates this last point. example 7. 4 problem a neural network is being trained to predict the maximum height of a variety of corn plant based on a number of features, such as soil acidity,... | openstax_principles-of-data-science-web | [
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significantly more of one class than another ), and is well - suited to handle output that may be interpreted on a probability scale from 0 to 1. hinge loss is another common loss function for binary classification tasks. it is suitable for scenarios where the goal is to maximize the margin between classes while minimi... | openstax_principles-of-data-science-web | [
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similarity between the predicted class probabilities and the true class labels. the term sparse refers to the nature of the output as integers versus one - hot encoded vectors ( recall one - hot encoding from other machine learning techniques ). ( sparse ) categorical cross entropy is often paired with softmax activati... | openstax_principles-of-data-science-web | [
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7. 3 • introduction to deep learning 359 # import the libraries import tensorflow as tf from sklearn. datasets import fetch _ openml from sklearn. model _ selection import train _ test _ split from sklearn. preprocessing import standardscaler # load mnist digits dataset mnist = fetch _ openml ('mnist _ 784 ', version =... | openstax_principles-of-data-science-web | [
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_ scaled, y _ train, epochs = 10, batch _ size = 32, validation _ split = 0. 2 ) # evaluate the model on test data test _ loss, test _ accuracy = model. evaluate ( x _ test _ scaled, y _ test ) print ( " test loss : ", test _ loss ) print ( " test accuracy : ", test _ accuracy ) the resulting output will look like this... | openstax_principles-of-data-science-web | [
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7. 4 convolutional neural networks learning outcomes by the end of this section, you should be able to : • 7. 4. 1 define convolutional neural networks and describe some problems where they may perform better than standard neural networks. • 7. 4. 2 describe feature maps. • 7. 4. 3 train and test a convolutional neural... | openstax_principles-of-data-science-web | [
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, consider the list of data [ 0, 0, 0, 1, 1, 1, 0, 0, 0, 2, 2, 2 ]. this data can be compressed to [ 0, 1, 0, 2 ]. ( in real applications, some kind of averaging or voting procedure is used to determine the down - sampled values. ) • fully connected layers : these layers are typically used at the end of the network to ... | openstax_principles-of-data-science-web | [
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7. 4 • convolutional neural networks 361 figure 7. 11 semantic segmentation of an image. small cars appear in blue, large vans / trucks in yellow, traffic signs in red, and buildings and other barriers in brown. ( credit : " semantic segmentation image annotation kotwel " by kotwel inc. / flickr, cc by 2. 0 ) feature m... | openstax_principles-of-data-science-web | [
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color may appear drastically different depending on factors such as lighting, surrounding colors, and intensity. • shapes and structures. building upon edge - detection features, shapes and larger structures can be built up as higher - level features. • object parts and high - level patterns. even higher in the hierarc... | openstax_principles-of-data-science-web | [
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visual scenes, making semantic segmentation a crucial task in various computer vision applications, including autonomous driving, medical image analysis, scene understanding, and augmented reality. semantic segmentation is typically performed using deep learning techniques, particularly convolutional neural networks, w... | openstax_principles-of-data-science-web | [
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7. 5 natural language processing learning outcomes by the end of this section, you should be able to : • 7. 5. 1 provide a brief history of the significant developments of natural language processing. • 7. 5. 2 discuss the importance of speech recognition and text - to - speech algorithms in everyday life and provide s... | openstax_principles-of-data-science-web | [
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7. 5 • natural language processing 363 figure 7. 12 chatgpt conversation openai. ( 2024 ). chatgpt ( june 16 version ) [ large language model ]. https : / / chat. openai. com / chat ( created at : https : / / chat. openai. com / ( https : / / openstax. org / r / chat ) using the prompt : “ hi! tell me a little about yo... | openstax_principles-of-data-science-web | [
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architecture that use a very sophisticated internal memory system to capture long - range and large - scale dependencies in the input data. today, nlps power a diverse array of real - world applications. 364 7 • deep learning and ai basics access for free at openstax. org figure 7. 13 a screenshot of eliza ( credit : "... | openstax_principles-of-data-science-web | [
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., revolutionized the world of nlps. instead of considering words one by one, a transformer model looks at sentences, paragraphs, and larger groups of words using a paradigm called self - attention, which allows the model to evaluate the importance of different words or phrases within a larger context. 2018 openai intr... | openstax_principles-of-data-science-web | [
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7. 5 • natural language processing 365 year development 2020 openai released gpt - 3 ( generative pretrained transformer 3 ), an even larger and more powerful language model, with up to 175 billion parameters. 2022 chatgpt, a variant of gpt - 3 fine - tuned for conversational tasks, was released, gaining widespread att... | openstax_principles-of-data-science-web | [
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openai, craiyon ( https : / / openstax. org / r / craiyon ) generates images from textual descriptions, demonstrating the ability to create diverse and imaginative visual content. figure 7. 14 shows some examples of artistic works created by craiyon. 366 7 • deep learning and ai basics access for free at openstax. org ... | openstax_principles-of-data-science-web | [
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instruments simultaneously. amadeus code : amadeus code ( https : / / openstax. org / r / amadeuscode ) is an ai - powered music composition platform that uses deep learning algorithms to generate melodies and chord progressions. it allows users to input musical ideas and preferences and generates original compositions... | openstax_principles-of-data-science-web | [
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7. 5 • natural language processing 367 platform that uses nlp to transcribe audio and video files in multiple languages. it offers features such as speaker identification, timecode alignment, and captioning, helping video editors streamline the post - production workflow. computer coding at the time of this writing, it... | openstax_principles-of-data-science-web | [
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/ natural language ) is a dataset and research project that aims to bridge the gap between natural language descriptions and code snippets. it uses nlp techniques to generate code snippets from natural language descriptions of programming tasks, helping automate code synthesis. speech recognition and text - to - speech... | openstax_principles-of-data-science-web | [
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##metric synthesis, or using neural network – based generative models. future directions for nlp and llm where will natural language processing and large language models be in the future? given the rapid pace of development over the past few decades, it is hard to predict. some of the more recent tools such as chatgpt ... | openstax_principles-of-data-science-web | [
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via natural language models. there are already news channels ( https : / / openstax. org / r / echioh8fawe ) that are completely ai - generated! moreover, nlp applications will have a huge impact on education. colleges and universities have already been considering what to do about tools such as chatgpt in the classroo... | openstax_principles-of-data-science-web | [
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training future data scientists, who will use ai themselves! ) can offer several advantages. natural language processing models can efficiently generate high - quality content, covering a wide range of topics and providing explanations that are clear and accessible to learners. think of ai as a force multiplier, freein... | openstax_principles-of-data-science-web | [
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7. 5 • natural language processing 369 with the learning objectives of the course or text. chatgpt and ethical considerations of using nlp in the preceding sections, we introduced the powerful natural language processing model chatgpt. while ethical issues must be considered throughout the entire data science process (... | openstax_principles-of-data-science-web | [
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and serious ethical issues. the use of these models raises concerns about appropriate protections for human artists and their creations. one major concern is how the models are trained. essentially, anything that is available on the internet could be used as input to train an nlp model. this includes original text, art... | openstax_principles-of-data-science-web | [
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access for free at openstax. org between unions and studios established what one observer termed “ the most progressive ai protections in industry history ” ( luna & draper, 2023 ). disclosure and attribution the deployment of chatgpt and similar ai models in content creation necessitates transparent disclosure and att... | openstax_principles-of-data-science-web | [
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strange? first of all, the bass seems to be floating in front of the woman ( or is she balancing it by some sort of handle sticking out of the neck of the instrument? ). there are too few strings ( there should be four ). even the machine heads ( tuners, on the headstock, or very top, of the instrument ) are a bit off,... | openstax_principles-of-data-science-web | [
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7. 5 • natural language processing 371 figure 7. 15 image of a musician created by openart. while the picture resembles a real photo at first, there are numerous details that do not make sense. ( image generated using openart [ link to : https : / / openart. ai / create ( https : / / openstax. org / r / openart1 ) ] fr... | openstax_principles-of-data-science-web | [
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llms, a major concern is the potential for bias and unfairness due to unforeseen problems in its training data. ai systems, including chatgpt, can inadvertently perpetuate stereotypes or amplify existing societal biases present in the training data. moreover, nlp models like chatgpt may struggle with understanding and ... | openstax_principles-of-data-science-web | [
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as ibm's ai fairness 360, google's what - if tool, and microsoft's fairlearn. each tool offers unique features to identify, assess, and mitigate biases in datasets and models. malicious uses of ai user privacy, data security, potential copyright infringement, and devaluation of human - created content are just the tip ... | openstax_principles-of-data-science-web | [
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. the main goal of responsible ai is to foster the development and deployment of ai technologies that align with human values, respect human rights, and contribute to a more equitable and sustainable future. datasets note : the primary datasets referenced in the chapter code may also be downloaded here ( https : / / op... | openstax_principles-of-data-science-web | [
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7. 5 • natural language processing 373 key terms activation for a neuron, the process of sending an output signal after having received appropriate input signals activation function non - decreasing function f that determines whether the neuron activates artificial intelligence ( ai ) branch of computer science that ai... | openstax_principles-of-data-science-web | [
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round of training of a neural network using the entire training set ( or a batch thereof ) exploding gradient problem failure to train an rnn due to instability introduced by having connecting weights at values larger than 1 feature map output of convolutional layers in a cnn, representing the learned features of the i... | openstax_principles-of-data-science-web | [
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written or spoken language and generating new language content neural network structure made up of neurons that takes in input and produces output that classifies the input information neuron individual decision - making unit of a neural network that takes some number of inputs and produces an output nonlinear not line... | openstax_principles-of-data-science-web | [
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diagnoses patients for cirrhosis ( severe scarring of the liver ) based on numerical and categorical factors. you will use the dataset cirrhosis. csv ( https : / / openstax. org / r / datach7 ). a. the dataset has some missing data ( “ na ” ). remove all rows with missing data. b. the input data x should consist of all... | openstax_principles-of-data-science-web | [
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