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## You're reading fromPython Machine Learning, Second Edition - Second Edition

Product type Book
Published in Sep 2017
Publisher Packt
ISBN-13 9781787125933
Pages 622 pages
Edition 2nd Edition
Languages
Concepts
Authors (2):
Vahid Mirjalili
Dr. Sebastian Raschka
View More author details

Python Machine Learning Second Edition
Credits
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Preface
1. Giving Computers the Ability to Learn from Data 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

## Utilizing control flow mechanics in building graphs

Now let's learn about an interesting TensorFlow mechanic. TensorFlow provides a mechanism for making decisions when building a graph. However, there are some subtle differences when we use Python's control flow statements compared to TensorFlow's control flow functions, when constructing computation graphs.

To illustrate these differences with some simple code examples, let's consider implementing the following equation in TensorFlow:

In the following code, we may naively use Python's `if` statement to build a graph that corresponds to the preceding equation:

```>>> import tensorflow as tf
>>>
>>> x, y = 1.0, 2.0
>>>
>>> g = tf.Graph()
>>> with g.as_default():
...     tf_x = tf.placeholder(dtype=tf.float32,
...                           shape=None, name='tf_x')
...     tf_y = tf.placeholder(dtype=tf.float32,
...                           shape=None, name='tf_y')
...     if x < y:
...    ...```
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