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Advanced Machine Learning with Python.
Solve challenging data science problems by mastering cutting-edge machine learning techniques in PythonAbout 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 tutor...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Packt Publishing
2016.
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| Temas: | |
| Acceso en línea: | Texto completo |
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.
- TopologyTraining; 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 convnetPutting 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 dataStemming; 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 techniquesFurther 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.