pdfcoffee

Neural Network Foundations
with TensorFlow 2.0
In this chapter we learn the basics of TensorFlow, an open source library developed
by Google for machine learning and deep learning. In addition, we introduce the
basics of neural networks and deep learning, two areas of machine learning that
have had incredible Cambrian growth during the last few years. The idea behind
this chapter is to give you all the tools needed to do basic but fully hands-on deep
learning.
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.
[ 1 ]