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Machine Learning Algorithms

You're reading from   Machine Learning Algorithms Popular algorithms for data science and machine learning

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Product type Paperback
Published in Aug 2018
Publisher Packt
ISBN-13 9781789347999
Length 522 pages
Edition 2nd Edition
Languages
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Author (1):
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Giuseppe Bonaccorso Giuseppe Bonaccorso
Author Profile Icon Giuseppe Bonaccorso
Giuseppe Bonaccorso
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Table of Contents (19) Chapters Close

Preface 1. A Gentle Introduction to Machine Learning FREE CHAPTER 2. Important Elements in Machine Learning 3. Feature Selection and Feature Engineering 4. Regression Algorithms 5. Linear Classification Algorithms 6. Naive Bayes and Discriminant Analysis 7. Support Vector Machines 8. Decision Trees and Ensemble Learning 9. Clustering Fundamentals 10. Advanced Clustering 11. Hierarchical Clustering 12. Introducing Recommendation Systems 13. Introducing Natural Language Processing 14. Topic Modeling and Sentiment Analysis in NLP 15. Introducing Neural Networks 16. Advanced Deep Learning Models 17. Creating a Machine Learning Architecture 18. Other Books You May Enjoy

MLPs with Keras

Keras (https://keras.io) is a high-level deep learning framework that works seamlessly with low-level deep learning backends such as TensorFlow, Theano, and Microsoft Cognitive Toolkit (CNTK). In Keras, a model is like a sequence of layers where each output is fed into the following computational block until the final layer is reached and the cost function can be evaluated and differentiated.

The generic structure of a model is as follows:

from keras.models import Sequential

model = Sequential()

model.add(...)
model.add(...)
...
model.add(...)

The Sequential class defines a generic empty sequential model that already implements all the methods needed to add layers, compile the model according to the underlying framework (that is, transforming the high-level description into a set of commands compatible with the underlying backend), to fit and evaluate the model and...

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