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![Deep Learning with Keras
by Antonio Gulli
>>> http://shorl.com/provegrydidrydru DOWNLOAD BOOK
# Import the modules from sklearn.metrics from sklearn.metrics import confusionmatrix, precisionscore, recallscore, f1score, cohenkappascore # Confusion matrix confusionmatrix(ytest, ypred) array([[1585, 3], [ 8, 549]]) # Precision precisionscore(ytest, ypred) 0.994565217391 # Recall recallscore(ytest, ypred) 0.98563734290843807 # F1 score f1score(ytest,ypred) 0.99008115419296661 # Cohen's kappa cohenkappascore(ytest, ypred) 0.98662321692498967 All these scores are very good! You have made a pretty accurate model despite the fact that you have considerably more rows that are of the white wine typeIf you need to, you can further configure your optimizerChoose Kitematic, which will have you sign up for Docker HubIts expressed in g/(dm^3) in the two data setsYoull read more about this in the next sectionThe higher the precision, the more accurate the classifierIn other words, the training data is modelled too well! Note that when you dont have that much training data available, you should prefer to use a a small network with very few hidden layers (typically only one, like in the example above)This variables is expressed in mg/(dm^3) in the dataDespite that, the results for simply setting a flag are extremely impressive, and it is worth testing Keras models on both CNTK and TensorFlow now to see which is better before deploying them to productionHere, youll see that its expressed in g((sodium chloride))/(dm^3)Try to use 2 or 3 hidden layers; Use layers with more hidden units or less hidden units; Take the quality column as the target labels and the rest of the data (including the encoded type column!) as your dataPreprocess Data Since the quality variable becomes your target class, you will now need to isolate the quality labels from the rest of the data setMy network avoids converging early with only a minor cost to training speed in the TensorFlow case; unfortunately, CNTK speed is much slower than the simple model, but still faster than TensorFlow in the advanced modelDeep learning is one of the hottest trends in machine learning at the moment, and there are many problems where deep learning shines, such as robotics, image recognition and Artificial Intelligence (AI)These algorithms are usually called Artificial Neural Networks (ANN)Now youre completely set to start exploring, manipulating and modeling your data! Data Exploration With the data at hand, its easy for you to learn more about these wines! One of the first things that youll probably want to do is to start off with getting a quick view on both of your DataFrames: 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](http://cdn08.dayviews.com/500/_u3/_u8/_u7/_u1/_u8/_u5/u3871854/67984_1499330034/Deep_Learning_with_Keras_by_Antonio_Gulli_http_shorl_com_provegrydidrydru_DOWNLOAD_BOOK_Import_the.jpg)
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Deep Learning with Keras
by Antonio Gulli
>>> http://shorl.com/provegrydidrydru DOWNLOAD BOOK
# Import the modules from sklearn.metrics from sklearn.metrics import confusionmatrix, precisionscore, recallscore, f1score, cohenkappascore # Confusion matrix confusionmatrix(ytest, ypred) array([[1585, 3], [ 8, 549]]) # Precision precisionscore(ytest, ypred) 0.994565217391 # Recall recallscore(ytest, ypred) 0.98563734290843807 # F1 score f1score(ytest,ypred) 0.99008115419296661 # Cohen's kappa cohenkappascore(ytest, ypred) 0.98662321692498967 All these scores are very good! You have made a pretty accurate model despite the fact that you have considerably more rows that are of the white wine typeIf you need to, you can further configure your optimizerChoose Kitematic, which will have you sign up for Docker HubIts expressed in g/(dm^3) in the two data setsYoull read more about this in the next sectionThe higher the precision, the more accurate the classifierIn other words, the training data is modelled too well! Note that when you dont have that much training data available, you should prefer to use a a small network with very few hidden layers (typically only one, like in the example above)This variables is expressed in mg/(dm^3) in the dataDespite that, the results for simply setting a flag are extremely impressive, and it is worth testing Keras models on both CNTK and TensorFlow now to see which is better before deploying them to productionHere, youll see that its expressed in g((sodium chloride))/(dm^3)Try to use 2 or 3 hidden layers; Use layers with more hidden units or less hidden units; Take the quality column as the target labels and the rest of the data (including the encoded type column!) as your dataPreprocess Data Since the quality variable becomes your target class, you will now need to isolate the quality labels from the rest of the data setMy network avoids converging early with only a minor cost to training speed in the TensorFlow case; unfortunately, CNTK speed is much slower than the simple model, but still faster than TensorFlow in the advanced modelDeep learning is one of the hottest trends in machine learning at the moment, and there are many problems where deep learning shines, such as robotics, image recognition and Artificial Intelligence (AI)These algorithms are usually called Artificial Neural Networks (ANN)Now youre completely set to start exploring, manipulating and modeling your data! Data Exploration With the data at hand, its easy for you to learn more about these wines! One of the first things that youll probably want to do is to start off with getting a quick view on both of your DataFrames: 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 Now is the time to check whether your import was successful: double check whether the data contains all the variables that the data description file of the UCI Machine Learning Repository promised youUser friendlinessThere are legal limits for sulfur levels in wines: in the EU, red wines can only have 160mg/L, while white and rose wines can have about 210mg/L
On the Keras Slack channelThis will give insights more quickly about which variables correlate: 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 As you would expect, there are some variables that correlate, such as density and residual sugarEasy extensibility(#7238) Jul 5, 2017 tests Improved error message for missing classweightsStart your free account with DataCampAs you can imagine, binary means 0 or 1, yes or noAn example of a sigmoid function that you might already know is the logistic functionYou can learn more about Max here, view his data analysis portfolio here, or view his coding portfolio hereNote that the sigmoid function is a mathematical function that results in an S shaped curve; Youll read more about this laterThe 25,000 reviews in the dataset are tagged as positive or negativeThis is a typical setup for scalar regression, where you are trying to predict a single continuous value)When using the CNTK backend: CNTK See installation instructionsNot every architecture supported by Keras and other deep learning frameworks is supported yet, but were working to expand the number of nets that can be imported from Keras to DL4J.Additionally, you might want to save your (optimized) model or load it back in another timeNow that you have already inspected your data to see if the import was successful and correct, its time to dig a little bit deeperHere’s the generated text output from the TensorFlow-trained model on my architecture: hinks the rich man must be wholly perverity and connection of the english sin of the philosophers of the basis of the same profound of his placed and evil and exception of fear to plants to me such as the case of the will seems to the will to be every such a remark as a primates of a strong of [.] And here’s the output from the CNTK-trained model: (x2js1hevjg4z?za?qgpmj:sn![?(f3ch=lhw4y n6)gkh kujau momu,?!lj7g)k,!?[45 0as9[d.68hhptvsx jdni,z!cwkr"f6-mu(epp [.] Wait, what? Apparently my model architecture caused CNTK to hit a legitimate bug when making predictions, which did not happen with CNTK + the simple LSTM architectureUse this link to request an invitation to the channelYou can ask questions and join the development discussion: 07f867cfac
An auto-encoder tries to reconstruct the original input by minimizing the error during the reconstruction processRate this book Clear rating 1 of 5 stars2 of 5 stars3 of 5 stars4 of 5 stars5 of 5 stars A Collection of Advanced Data Science and Machine Learning Interview Questions Solved in Python and Spark (II): Hands-On Big Data and Machine Learning it was amazing 5.00 avg rating — 2 ratings Want to Read saving Error rating book{deep learning autoencoders} Here I am interested in finding a specific innovation discovered in deep learningKeywords: P2P networks, distributed marketplaces, eBay, public-key cryptography
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