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Python Machine Learning, Second Edition

You're reading from   Python Machine Learning, Second Edition Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow

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Product type Paperback
Published in Sep 2017
Publisher Packt
ISBN-13 9781787125933
Length 622 pages
Edition 2nd Edition
Languages
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Authors (2):
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Vahid Mirjalili Vahid Mirjalili
Author Profile Icon Vahid Mirjalili
Vahid Mirjalili
Sebastian Raschka Sebastian Raschka
Author Profile Icon Sebastian Raschka
Sebastian Raschka
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Table of Contents (18) Chapters Close

Preface 1. Giving Computers the Ability to Learn from Data FREE CHAPTER 2. Training Simple Machine Learning Algorithms for Classification 3. A Tour of Machine Learning Classifiers Using scikit-learn 4. Building Good Training Sets – Data Preprocessing 5. Compressing Data via Dimensionality Reduction 6. Learning Best Practices for Model Evaluation and Hyperparameter Tuning 7. Combining Different Models for Ensemble Learning 8. Applying Machine Learning to Sentiment Analysis 9. Embedding a Machine Learning Model into a Web Application 10. Predicting Continuous Target Variables with Regression Analysis 11. Working with Unlabeled Data – Clustering Analysis 12. Implementing a Multilayer Artificial Neural Network from Scratch 13. Parallelizing Neural Network Training with TensorFlow 14. Going Deeper – The Mechanics of TensorFlow 15. Classifying Images with Deep Convolutional Neural Networks 16. Modeling Sequential Data Using Recurrent Neural Networks Index

Putting everything together to build a CNN

So far, we've learned about the basic building blocks of convolutional neural networks. The concepts illustrated in this chapter are not really more difficult than traditional multilayer neural networks. Intuitively, we can say that the most important operation in a traditional neural network is the matrix-vector multiplication.

For instance, we use matrix-vector multiplications to pre-activations (or net input) as in Putting everything together to build a CNN. Here, x is a column vector representing pixels, and W is the weight matrix connecting the pixel inputs to each hidden unit. In a convolutional neural network, this operation is replaced by a convolution operation, as in Putting everything together to build a CNN, where X is a matrix representing the pixels in a height x width arrangement. In both cases, the pre-activations are passed to an activation function to obtain the activation of a hidden unit Putting everything together to build a CNN, where Putting everything together to build a CNN is the activation function. Furthermore, recall that subsampling is another building block of a convolutional...

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