Advanced machine learning with Python : solve challenging data science problems by mastering cutting-edge machine learning techniques in Python

Advanced machine learning with Python solve challenging data science problems by mastering cutting-edge machine learning techniques in Python

Solve challenging data science problems by mastering cutting-edge machine learning techniques in Python About This Book Resolve complex machine learning problems and explore deep learning Learn to use Python code for implementing a range of machine learning algorithms and techniques A practical tuto...

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Detalles Bibliográficos
Otros Autores: Hearty, John, author (author)
Formato: Libro electrónico
Idioma:Inglés
Publicado: Birmingham : Packt Publishing 2016.
Edición:1st edition
Colección:Community experience distilled.
Materias:
Machine learning.
Python (Computer program language)
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009629872306719
Tabla de Contenidos:
  • Cover
  • Copyright
  • Credits
  • About the Author
  • About the Reviewers
  • www.PacktPub.com
  • Table of Contents
  • Preface
  • Chapter 1: Unsupervised Machine Learning
  • Principal component analysis
  • PCA - a primer
  • Employing PCA
  • Introducing k-means clustering
  • Clustering - a primer
  • Kick-starting clustering analysis
  • Tuning your clustering configurations
  • Self-organizing maps
  • SOM - a primer
  • Employing SOM
  • Further reading
  • Summary
  • Chapter 2: Deep Belief Networks
  • Neural networks - a primer
  • The composition of a neural network
  • Network topologies
  • Restricted Boltzmann Machine
  • Introducing the RBM
  • Topology
  • Training
  • Applications of the RBM
  • Further applications of the RBM
  • Deep belief networks
  • Training a DBN
  • Applying the DBN
  • Validating the DBN
  • Further reading
  • Summary
  • Chapter 3: Stacked Denoising Autoencoders
  • Autoencoders
  • Introducing the autoencoder
  • Topology
  • Training
  • Denoising autoencoders
  • Applying a dA
  • Stacked Denoising Autoencoders
  • Applying the SdA
  • Assessing SdA performance
  • Further reading
  • Summary
  • Chapter 4: Convolutional Neural Networks
  • Introducing the CNN
  • Understanding the convnet topology
  • Understanding convolution layers
  • Understanding pooling layers
  • Training a convnet
  • Putting it all together
  • Applying a CNN
  • Further Reading
  • Summary
  • Chapter 5: Semi-Supervised Learning
  • Introduction
  • Understanding semi-supervised learning
  • Semi-supervised algorithms in action
  • Self-training
  • Implementing self-training
  • Finessing your self-training implementation
  • Contrastive Pessimistic Likelihood Estimation
  • Further reading
  • Summary
  • Chapter 6: Text Feature Engineering
  • Introduction
  • Text feature engineering
  • Cleaning text data
  • Text cleaning with BeautifulSoup
  • Managing punctuation and tokenizing.
  • Tagging and categorising words
  • Creating features from text data
  • Stemming
  • Bagging and random forests
  • Testing our prepared data
  • Further reading
  • Summary
  • Chapter 7: Feature Engineering Part II
  • Introduction
  • Creating a feature set
  • Engineering features for ML applications
  • Using rescaling techniques to improve the learnability of features
  • Creating effective derived variables
  • Reinterpreting non-numeric features
  • Using feature selection techniques
  • Performing feature selection
  • Feature engineering in practice
  • Acquiring data via RESTful APIs
  • Testing the performance of our model
  • Twitter
  • Deriving and selecting variables using feature engineering techniques
  • Further reading
  • Summary
  • Chapter 8: Ensemble Methods
  • Introducing ensembles
  • Understanding averaging ensembles
  • Using bagging algorithms
  • Using random forests
  • Applying boosting methods
  • Using XGBoost
  • Using stacking ensembles
  • Applying ensembles in practice
  • Using models in dynamic applications
  • Understanding model robustness
  • Identifying modeling risk factors
  • Strategies to managing model robustness
  • Further reading
  • Summary
  • Chapter 9: Additional Python Machine Learning Tools
  • Alternative development tools
  • Introduction to Lasagne
  • Getting to know Lasagne
  • Introduction to TensorFlow
  • Getting to know TensorFlow
  • Using TensorFlow to iteratively improve our models
  • Knowing when to use these libraries
  • Further reading
  • Summary
  • Appendix: Chapter Code Requirements
  • Index.

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