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

Implementing DeepDream

Another use for trained CNNs is exploiting the fact that some intermediate nodes detect features of labels (for instance, a cat's ear, or a bird's feather). Using this fact, we can find ways to transform any image to reflect those node features for any node we choose. This recipe is an adapted version of the official TensorFlow DeepDream tutorial (refer to the first bullet point in the next See also section). Feel free to visit the Google AI blog post written by DeepDream's creator, named Alexander Mordvintsev (second bullet point in the next See also section). The hope is that we can prepare you to use the DeepDream algorithm to explore CNNs, and features created in them.

Getting ready

Originally, this technique was invented to better understand how a CNN sees. The goal of DeepDream is to over-interpret the patterns that the model detects and generate inspiring visual content with surreal patterns. This algorithm is...