You're reading from Deep Learning with TensorFlow 2 and Keras - Second Edition

Product typeBook

Published inDec 2019

Reading LevelBeginner

PublisherPackt

ISBN-139781838823412

Edition2nd Edition

Languages

Python

Tools

TensorFlow

Concepts

Deep Learning

Authors (3):

Antonio Gulli

Amita Kapoor

Sujit Pal

View More author details

What is TensorFlow (TF)?

TensorFlow is a powerful open source software library developed by the Google Brain team for deep neural networks, the topic covered in this book. It was first made available under the Apache 2.0 License in November 2015 and has since grown rapidly; as of May 2019, its GitHub repository (https://github.com/tensorflow/tensorflow) has more than 51,000 commits, with roughly 1,830 contributors. This in itself provides a measure of the popularity of TensorFlow.

Let us first learn what exactly TensorFlow is and why it is so popular among deep neural network researchers and engineers. Google calls it "an open source software library for machine intelligence," but since there are so many other deep learning libraries like PyTorch (https://pytorch.org/), Caffe (https://caffe.berkeleyvision.org/), and MxNet (https://mxnet.apache.org/), what makes TensorFlow special? Most other deep learning libraries – like TensorFlow – have auto-differentiation (a useful mathematical tool used for optimization), many are open source platforms, most of them support the CPU/GPU option, have pretrained models, and support commonly used NN architectures like recurrent neural networks, convolutional neural networks, and deep belief networks.

So, what else is there in TensorFlow? Let me list the top features:

It works with all popular languages such as Python, C++, Java, R, and Go.
Keras – a high-level neural network API that has been integrated with TensorFlow (in 2.0, Keras became the standard API for interacting with TensorFlow). This API specifies how software components should interact.
TensorFlow allows model deployment and ease of use in production.
Support for eager computation (see Chapter 2, TensorFlow 1.x and 2.x) has been introduced in TensorFlow 2.0, in addition to graph computation based on static graphs.
Most importantly, TensorFlow has very good community support.

The number of stars on GitHub (see Figure 1) is a measure of popularity for all open source projects. As of March 2019, TensorFlow, Keras, and PyTorch have 123,000, 39,000, and 25,000 stars respectively, which makes TensorFlow the most popular framework for machine learning:

Figure 1: Number of stars for various deep learning projects on GitHub

Google Trends is another measure of popularity, and again TensorFlow and Keras are the two top frameworks (late 2019), with PyTorch rapidly catching up (see Figure 2).

Figure 2: Google Trends for various deep learning projects

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Authors (3)

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

Amita Kapoor

Amita Kapoor is an accomplished AI consultant and educator, with over 25 years of experience. She has received international recognition for her work, including the DAAD fellowship and the Intel Developer Mesh AI Innovator Award. She is a highly respected scholar in her field, with over 100 research papers and several best-selling books on deep learning and AI. After teaching for 25 years at the University of Delhi, Amita took early retirement and turned her focus to democratizing AI education. She currently serves as a member of the Board of Directors for the non-profit Neuromatch Academy, fostering greater accessibility to knowledge and resources in the field. Following her retirement, Amita also founded NePeur, a company that provides data analytics and AI consultancy services. In addition, she shares her expertise with a global audience by teaching online classes on data science and AI at the University of Oxford.
Read more about Amita Kapoor

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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