Book Image

Deep Learning with TensorFlow and Keras – 3rd edition - Third Edition

By : Amita Kapoor, Antonio Gulli, Sujit Pal
5 (2)
Book Image

Deep Learning with TensorFlow and Keras – 3rd edition - Third Edition

5 (2)
By: Amita Kapoor, Antonio Gulli, Sujit Pal

Overview of this book

Deep Learning with TensorFlow and Keras teaches you neural networks and deep learning techniques using TensorFlow (TF) and Keras. You'll learn how to write deep learning applications in the most powerful, popular, and scalable machine learning stack available. TensorFlow 2.x focuses on simplicity and ease of use, with updates like eager execution, intuitive higher-level APIs based on Keras, and flexible model building on any platform. This book uses the latest TF 2.0 features and libraries to present an overview of supervised and unsupervised machine learning models and provides a comprehensive analysis of deep learning and reinforcement learning models using practical examples for the cloud, mobile, and large production environments. This book also shows you how to create neural networks with TensorFlow, runs through popular algorithms (regression, convolutional neural networks (CNNs), transformers, generative adversarial networks (GANs), recurrent neural networks (RNNs), natural language processing (NLP), and graph neural networks (GNNs)), covers working example apps, and then dives into TF in production, TF mobile, and TensorFlow with AutoML.
Table of Contents (23 chapters)
21
Other Books You May Enjoy
22
Index

Creating a DeepDream network

Another interesting application of CNNs is DeepDream, a computer vision program created by Google [8] that uses a CNN to find and enhance patterns in images. The result is a dream-like hallucinogenic effect. Similar to the previous example, we are going to use a pretrained network to extract features. However, in this case, we want to “enhance” patterns in images, meaning that we need to maximize some functions. This tells us that we need to use a gradient ascent and not a descent. First, let’s see an example from Google gallery (available at https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/tutorials/generative/deepdream.ipynb) where the classic Seattle landscape is “incepted” with hallucinogenic dreams such as birds, cards, and strange flying objects.

Google released the DeepDream code as open source (available at https://github.com/google/deepdream), but we will use a simplified example...