Format:
1 online resource (278 pages)
Edition:
1st ed.
ISBN:
9781784393830
Note:
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
Additional Edition:
Print version Advanced Machine Learning with Python Birmingham : Packt Publishing, Limited,c2016
Language:
English
Keywords:
Electronic books
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Electronic books
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