Python machine learning. Machine learning and deep learning whit Python, scikit-learn and TensorFlow
Raschka, Sebastian
Python machine learning. Machine learning and deep learning whit Python, scikit-learn and TensorFlow - Second edition - Birmingham (Inglaterra): Packt Publishing 2017 - 595 páginas (23x19 cm)
Giving computers the ability to learn from data.-- Training simple machine learning algorithms for classification.-- A tour of machine learning classifiers using scikit-learn.-- Building good training sets - data preprocessing.-- Compressing data via dimensionality reduction.-- Learning best practices for madel evaluation and hyperparameter tuning.-- Combining different model for ensamble learning.-- Embedding a machine learning model into a web application.-- Prediicting continuous target variables with regression analysis.-- Working with unlabiled data - clustering analysis.-- Implementing a multilayer artificial neural network from scratch.-- Parallelizing neural nentwork training with ternsorflow.-- Classifying images with deep convolutional neural networks.-- Modeling sequential data using recurrent neural networks.-- Introducing sequential data.-- RNNs for modeling sequenses.-- Implement a multiplayer RNN for sequence modeling in tensorflow
9781787125933
MECANICA
006.31 / RASp
Python machine learning. Machine learning and deep learning whit Python, scikit-learn and TensorFlow - Second edition - Birmingham (Inglaterra): Packt Publishing 2017 - 595 páginas (23x19 cm)
Giving computers the ability to learn from data.-- Training simple machine learning algorithms for classification.-- A tour of machine learning classifiers using scikit-learn.-- Building good training sets - data preprocessing.-- Compressing data via dimensionality reduction.-- Learning best practices for madel evaluation and hyperparameter tuning.-- Combining different model for ensamble learning.-- Embedding a machine learning model into a web application.-- Prediicting continuous target variables with regression analysis.-- Working with unlabiled data - clustering analysis.-- Implementing a multilayer artificial neural network from scratch.-- Parallelizing neural nentwork training with ternsorflow.-- Classifying images with deep convolutional neural networks.-- Modeling sequential data using recurrent neural networks.-- Introducing sequential data.-- RNNs for modeling sequenses.-- Implement a multiplayer RNN for sequence modeling in tensorflow
9781787125933
MECANICA
006.31 / RASp