Book Image

Machine Learning Using TensorFlow Cookbook

By : Luca Massaron, Alexia Audevart, Konrad Banachewicz
Book Image

Machine Learning Using TensorFlow Cookbook

By: Luca Massaron, Alexia Audevart, Konrad Banachewicz

Overview of this book

The independent recipes in Machine Learning Using TensorFlow Cookbook will teach you how to perform complex data computations and gain valuable insights into your data. Dive into recipes on training models, model evaluation, sentiment analysis, regression analysis, artificial neural networks, and deep learning - each using Google’s machine learning library, TensorFlow. This cookbook covers the fundamentals of the TensorFlow library, including variables, matrices, and various data sources. You’ll discover real-world implementations of Keras and TensorFlow and learn how to use estimators to train linear models and boosted trees, both for classification and regression. Explore the practical applications of a variety of deep learning architectures, such as recurrent neural networks and Transformers, and see how they can be used to solve computer vision and natural language processing (NLP) problems. With the help of this book, you will be proficient in using TensorFlow, understand deep learning from the basics, and be able to implement machine learning algorithms in real-world scenarios.
Table of Contents (15 chapters)
5
Boosted Trees
11
Reinforcement Learning with TensorFlow and TF-Agents
13
Other Books You May Enjoy
14
Index

Applying StyleNet and the neural style project

Once we have an image recognition CNN trained, we can use the network itself for some interesting data and image processing. StyleNet is a procedure that attempts to learn an image style from one picture and apply it to a second picture while keeping the second image structure (or content) intact. To do so, we have to find intermediate CNN nodes that correlate strongly with a style, separately from the content of the image.

StyleNet is a procedure that takes two images and applies the style of one image to the content of the second image. It is based on a famous paper by Leon Gatys in 2015, A Neural Algorithm of Artistic Style (refer to the first bullet point under the next See also section). The authors found a property in some CNNs containing intermediate layers. Some of them seem to encode the style of a picture, and some others its content. To this end, if we train the style layers on the style picture...