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Mastering Predictive Analytics with scikit：learn and TensorFlow
Pythonisaprogramminglanguagethatprovidesawiderangeoffeaturesthatcanbeusedinthefieldofdatascience.MasteringPredictiveAnalyticswithscikit-learnandTensorFlowcoversvariousimplementationsofensemblemethods,howtheyareusedwithreal-worlddatasets,andhowtheyimprovepredictionaccuracyinclassificationandregressionproblems.Thisbookstartswithensemblemethodsandtheirfeatures.Youwillseethatscikit-learnprovidestoolsforchoosinghyperparametersformodels.Asyoumakeyourwaythroughthebook,youwillcoverthenitty-grittyofpredictiveanalyticsandexploreitsfeaturesandcharacteristics.YouwillalsobeintroducedtoartificialneuralnetworksandTensorFlow,andhowitisusedtocreateneuralnetworks.Inthefinalchapter,youwillexplorefactorssuchascomputationalpower,alongwithimprovementmethodsandsoftwareenhancementsforefficientpredictiveanalytics.Bytheendofthisbook,youwillbewell-versedinusingdeepneuralnetworkstosolvecommonproblemsinbigdataanalysis.
目錄(122章)
倒序
- 封面
- Title Page
- Copyright and Credits
- Mastering Predictive Analytics with scikit-learn and TensorFlow
- Packt Upsell
- Why subscribe?
- Packt.com
- Contributor
- About the author
- Packt is searching for authors like you
- Preface
- Who this book is for
- What this book covers
- To get the most out of this book
- Download the example code files
- Download the color images
- Conventions used
- Get in touch
- Reviews
- Ensemble Methods for Regression and Classification
- Ensemble methods and their working
- Bootstrap sampling
- Bagging
- Random forests
- Boosting
- Ensemble methods for regression
- The diamond dataset
- Training different regression models
- KNN model
- Bagging model
- Random forests model
- Boosting model
- Using ensemble methods for classification
- Predicting a credit card dataset
- Training different regression models
- Logistic regression model
- Bagging model
- Random forest model
- Boosting model
- Summary
- Cross-validation and Parameter Tuning
- Holdout cross-validation
- K-fold cross-validation
- Implementing k-fold cross-validation
- Comparing models with k-fold cross-validation
- Introduction to hyperparameter tuning
- Exhaustive grid search
- Hyperparameter tuning in scikit-learn
- Comparing tuned and untuned models
- Summary
- Working with Features
- Feature selection methods
- Removing dummy features with low variance
- Identifying important features statistically
- Recursive feature elimination
- Dimensionality reduction and PCA
- Feature engineering
- Creating new features
- Improving models with feature engineering
- Training your model
- Reducible and irreducible error
- Summary
- Introduction to Artificial Neural Networks and TensorFlow
- Introduction to ANNs
- Perceptrons
- Multilayer perceptron
- Elements of a deep neural network model
- Deep learning
- Elements of an MLP model
- Introduction to TensorFlow
- TensorFlow installation
- Core concepts in TensorFlow
- Tensors
- Computational graph
- Summary
- Predictive Analytics with TensorFlow and Deep Neural Networks
- Predictions with TensorFlow
- Introduction to the MNIST dataset
- Building classification models using MNIST dataset
- Elements of the DNN model
- Building the DNN
- Reading the data
- Defining the architecture
- Placeholders for inputs and labels
- Building the neural network
- The loss function
- Defining optimizer and training operations
- Training strategy and valuation of accuracy of the classification
- Running the computational graph
- Regression with Deep Neural Networks (DNN)
- Elements of the DNN model
- Building the DNN
- Reading the data
- Objects for modeling
- Training strategy
- Input pipeline for the DNN
- Defining the architecture
- Placeholders for input values and labels
- Building the DNN
- The loss function
- Defining optimizer and training operations
- Running the computational graph
- Classification with DNNs
- Exponential linear unit activation function
- Classification with DNNs
- Elements of the DNN model
- Building the DNN
- Reading the data
- Producing the objects for modeling
- Training strategy
- Input pipeline for DNN
- Defining the architecture
- Placeholders for inputs and labels
- Building the neural network
- The loss function
- Evaluation nodes
- Optimizer and the training operation
- Run the computational graph
- Evaluating the model with a set threshold
- Summary
- Other Books You May Enjoy
- Leave a review - let other readers know what you think 更新時間：2021-07-23 16:42:54
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