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You're reading from Deep Learning with Keras

Product typeBook

Published inApr 2017

Reading LevelIntermediate

PublisherPackt

ISBN-139781787128422

Edition1st Edition

Languages

Python

Tools

TensorFlow Keras

Concepts

Deep Learning

Authors (2):

Antonio Gulli

Sujit Pal

View More author details

Chapter 9. Conclusion

Congratulations on making it to the end of the book! Let us take a moment and see how far we have come since we started.

If you are like most readers, you started with some knowledge of Python and some background in machine learning, but you were interested in learning more about deep learning and wanted to be able to apply these deep learning skills using Python.

You learned how to install Keras on your machine and started using it to build simple deep learning models. You then learned about the original deep learning model, the multi-layer perceptron, also called the fully connected network (FCN). You learned how to build this network using Keras.

You also learned about the many tunable parameters that you need to tweak to get good results from your network. With Keras, a lot of the hard work has been done for you since it comes with sensible defaults, but there are occasions where this knowledge will be helpful to you.

Continuing on from there, you were introduced to...

Keras 2.0 — what is new

According to Francois Chollet, Keras was released two years ago, in March, 2015. It then proceeded to grow from one user to one hundred thousand. The following image, taken from the Keras blog, shows the growth of number of Keras users over time.

One important update with Keras 2.0 is that the API will now be a part of TensorFlow, starting with TensorFlow 1.2. Indeed, Keras is becoming more and more the lingua franca for deep learning, a spec used in an increasing number of deep learning contexts. For instance, Skymind is implementing Keras spec in Scala for ScalNet, and Keras.js is doing the same for JavaScript for running of deep learning directly in the browser. Efforts are also underway to provide a Keras API for MXNET and CNTK deep learning toolkits.

Installing Keras 2.0

Installing Keras 2.0 is very simple via the pip install keras --upgrade followed by pip install tensorflow --upgrade.

API changes

The Keras 2.0 changes implied the need to rethink some APIs. For full...

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Authors (2)

Antonio Gulli

Antonio Gulli has a passion for establishing and managing global technological talent for innovation and execution. His core expertise is in cloud computing, deep learning, and search engines. Currently, Antonio works for Google in the Cloud Office of the CTO in Zurich, working on Search, Cloud Infra, Sovereignty, and Conversational AI.
Read more about Antonio Gulli

Sujit Pal

Sujit Pal is a Technology Research Director at Elsevier Labs, an advanced technology group within the Reed-Elsevier Group of companies. His interests include semantic search, natural language processing, machine learning, and deep learning. At Elsevier, he has worked on several initiatives involving search quality measurement and improvement, image classification and duplicate detection, and annotation and ontology development for medical and scientific corpora.
Read more about Sujit Pal

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