Book Image

Deep Learning with TensorFlow - Second Edition

By : Giancarlo Zaccone, Md. Rezaul Karim
Book Image

Deep Learning with TensorFlow - Second Edition

By: Giancarlo Zaccone, Md. Rezaul Karim

Overview of this book

Deep learning is a branch of machine learning algorithms based on learning multiple levels of abstraction. Neural networks, which are at the core of deep learning, are being used in predictive analytics, computer vision, natural language processing, time series forecasting, and to perform a myriad of other complex tasks. This book is conceived for developers, data analysts, machine learning practitioners and deep learning enthusiasts who want to build powerful, robust, and accurate predictive models with the power of TensorFlow, combined with other open source Python libraries. Throughout the book, you’ll learn how to develop deep learning applications for machine learning systems using Feedforward Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Autoencoders, and Factorization Machines. Discover how to attain deep learning programming on GPU in a distributed way. You'll come away with an in-depth knowledge of machine learning techniques and the skills to apply them to real-world projects.
Table of Contents (15 chapters)
Deep Learning with TensorFlow - Second Edition
Contributors
Preface
Other Books You May Enjoy
Index

Summary


In this chapter, we introduced CNNs. We have seen that CNNs are suitable for image classification problems, making the training phase faster and the test phase more accurate.

The most common CNN architectures have been described: the LeNet-5 model, designed for handwritten and machine-printed character recognition; AlexNet, which competed in the ILSVRC in 2012; the VGG model, which achieves a top-5 test accuracy of 92.7% in ImageNet (a dataset of over 14 million images belonging to 1,000 classes); and finally the Inception-v3 model, which was responsible for setting the standard for classification and detection in the ILSVRC in 2014.

The description of each CNN architecture was followed by a code example. Also, the AlexNet network and VGG examples have helped to explain the concepts of the transfer and style learning techniques.

Finally, we built a CNN to classify emotions in a dataset of images; we tested the network on a single image and evaluated the limits and the quality of our...