Book Image

Deep Learning with TensorFlow

By : Giancarlo Zaccone, Md. Rezaul Karim, Ahmed Menshawy
Book Image

Deep Learning with TensorFlow

By: Giancarlo Zaccone, Md. Rezaul Karim, Ahmed Menshawy

Overview of this book

Deep learning is the step that comes after machine learning, and has more advanced implementations. Machine learning is not just for academics anymore, but is becoming a mainstream practice through wide adoption, and deep learning has taken the front seat. As a data scientist, if you want to explore data abstraction layers, this book will be your guide. This book shows how this can be exploited in the real world with complex raw data using TensorFlow 1.x. Throughout the book, you’ll learn how to implement deep learning algorithms for machine learning systems and integrate them into your product offerings, including search, image recognition, and language processing. Additionally, you’ll learn how to analyze and improve the performance of deep learning models. This can be done by comparing algorithms against benchmarks, along with machine intelligence, to learn from the information and determine ideal behaviors within a specific context. After finishing the book, you will be familiar with machine learning techniques, in particular the use of TensorFlow for deep learning, and will be ready to apply your knowledge to research or commercial projects.
Table of Contents (11 chapters)

Summary

In this chapter, we introduced Convolutional Neural Networks (CNNs).

We have seen how the architecture of these networks yield CNNs, which are particularly suitable for image classification problems, making the training phase faster and the test phase more accurate.

We have therefore implemented an image classifier, testing it on MNIST dataset, where have achieved a 99 percent accuracy.

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

The next chapter describes autoencoders, these algorithms are useful for dimensionality reduction, classification, regression, collaborative filtering, feature learning and topic modeling. We will carry out further data analysis using autoencoders and measure classification performance using image datasets.

...