Cargando…
Mastering Predictive Analytics with Scikit-Learn and TensorFlow : Implement Machine Learning Techniques to Build Advanced Predictive Models Using Python.
In this book, you will find a range of methods to improve the performance of almost any predictive model, from ensemble methods to dimensionality reduction and cross-validation. You will learn the tools to produce advanced predictive models. In addition, you will dive into the exiting field of Deep...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing Ltd,
2018.
|
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
| LEADER | 00000cam a2200000Mi 4500 | ||
|---|---|---|---|
| 001 | EBSCO_on1056906409 | ||
| 003 | OCoLC | ||
| 005 | 20231017213018.0 | ||
| 006 | m o d | ||
| 007 | cr cnu---unuuu | ||
| 008 | 181013s2018 enk o 000 0 eng d | ||
| 040 | |a EBLCP |b eng |e pn |c EBLCP |d TEFOD |d LVT |d OCLCO |d OCLCF |d YDX |d OCLCQ |d UKAHL |d OCLCQ |d N$T |d NLW |d K6U |d OCLCO |d OCLCQ |d PSYSI |d OCLCQ | ||
| 019 | |a 1056479440 | ||
| 020 | |a 9781789612240 |q (electronic bk.) | ||
| 020 | |a 1789612241 |q (electronic bk.) | ||
| 029 | 1 | |a AU@ |b 000065065944 | |
| 035 | |a (OCoLC)1056906409 |z (OCoLC)1056479440 | ||
| 037 | |a 1A98563A-0816-40E9-8B0D-74B69167723F |b OverDrive, Inc. |n http://www.overdrive.com | ||
| 050 | 4 | |a Q325.5 .F846 2018 | |
| 082 | 0 | 4 | |a 006.31 |
| 049 | |a UAMI | ||
| 100 | 1 | |a Fuentes, Alvaro. | |
| 245 | 1 | 0 | |a Mastering Predictive Analytics with Scikit-Learn and TensorFlow : |b Implement Machine Learning Techniques to Build Advanced Predictive Models Using Python. |
| 260 | |a Birmingham : |b Packt Publishing Ltd, |c 2018. | ||
| 300 | |a 1 online resource (149 pages) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 588 | 0 | |a Print version record. | |
| 505 | 0 | |a Cover; Title Page; Copyright and Credits; Packt Upsell; Contributor; Table of Contents; Preface; Chapter 1: 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. | |
| 505 | 8 | |a Boosting modelSummary; Chapter 2: 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; Chapter 3: 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. | |
| 505 | 8 | |a Creating new featuresImproving models with feature engineering; Training your model; Reducible and irreducible error; Summary; Chapter 4: 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; Chapter 5: Predictive Analytics with TensorFlow and Deep Neural Networks; Predictions with TensorFlow; Introduction to the MNIST dataset. | |
| 505 | 8 | |a Building classification models using MNIST datasetElements 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. | |
| 505 | 8 | |a Placeholders for input values and labelsBuilding 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. | |
| 500 | |a Evaluating the model with a set threshold. | ||
| 520 | |a In this book, you will find a range of methods to improve the performance of almost any predictive model, from ensemble methods to dimensionality reduction and cross-validation. You will learn the tools to produce advanced predictive models. In addition, you will dive into the exiting field of Deep Learning using TensorFlow. | ||
| 590 | |a eBooks on EBSCOhost |b EBSCO eBook Subscription Academic Collection - Worldwide | ||
| 650 | 0 | |a Data mining. | |
| 650 | 0 | |a Big data. | |
| 650 | 0 | |a Decision making |x Data processing. | |
| 650 | 0 | |a Application software |x Development. | |
| 650 | 0 | |a Python (Computer program language) | |
| 650 | 2 | |a Data Mining | |
| 650 | 6 | |a Exploration de données (Informatique) | |
| 650 | 6 | |a Données volumineuses. | |
| 650 | 6 | |a Prise de décision |x Informatique. | |
| 650 | 6 | |a Logiciels d'application |x Développement. | |
| 650 | 6 | |a Python (Langage de programmation) | |
| 650 | 7 | |a Information theory. |2 bicssc | |
| 650 | 7 | |a Computer modelling & simulation. |2 bicssc | |
| 650 | 7 | |a Natural language & machine translation. |2 bicssc | |
| 650 | 7 | |a Information architecture. |2 bicssc | |
| 650 | 7 | |a Computers |x Natural Language Processing. |2 bisacsh | |
| 650 | 7 | |a Computers |x Computer Simulation. |2 bisacsh | |
| 650 | 7 | |a Computers |x Information Theory. |2 bisacsh | |
| 650 | 7 | |a Application software |x Development. |2 fast |0 (OCoLC)fst00811707 | |
| 650 | 7 | |a Big data. |2 fast |0 (OCoLC)fst01892965 | |
| 650 | 7 | |a Data mining. |2 fast |0 (OCoLC)fst00887946 | |
| 650 | 7 | |a Decision making |x Data processing. |2 fast |0 (OCoLC)fst00889041 | |
| 650 | 7 | |a Python (Computer program language) |2 fast |0 (OCoLC)fst01084736 | |
| 776 | 0 | 8 | |i Print version: |a Fuentes, Alvaro. |t Mastering Predictive Analytics with Scikit-Learn and TensorFlow : Implement Machine Learning Techniques to Build Advanced Predictive Models Using Python. |d Birmingham : Packt Publishing Ltd, ©2018 |z 9781789617740 |
| 856 | 4 | 0 | |u https://ebsco.uam.elogim.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1905995 |z Texto completo |
| 938 | |a Askews and Holts Library Services |b ASKH |n BDZ0037799358 | ||
| 938 | |a EBL - Ebook Library |b EBLB |n EBL5532295 | ||
| 938 | |a YBP Library Services |b YANK |n 15738737 | ||
| 938 | |a EBSCOhost |b EBSC |n 1905995 | ||
| 994 | |a 92 |b IZTAP | ||