pdfcoffee

Preface
Chapter 13, TensorFlow for Mobile and IoT and TensorFlow.js, this chapter focuses on
the math behind DL. The goal of the chapter is to provide readers a glimpse of what
is happening "under the hood" when you play with neural networks. The chapter
reviews the high school concepts of derivatives and gradients, and will introduce the
gradient descent and backpropagation algorithms commonly used to optimize deep
learning networks.
Chapter 14, An Introduction to AutoML, this chapter discusses the cloud environment
and how to utilize it for training and deploying your model. It will cover the steps
needed to set up Amazon Web Services (AWS) for DL. The steps needed to set up
Google Cloud Platform for DL applications will also be covered. It will also cover
how to set up Microsoft Azure for DL applications. The chapter will include various
cloud services that allow you to run the Jupyter Notebook directly on the cloud.
Finally, the chapter will conclude with an introduction to TensorFlow Extended.
Chapter 15, The Math behind Deep Learning, this chapter, as the title implies, discusses
the math behind deep learning. In the chapter, we'll get "under the hood" and see
what's going on when we perform deep learning. The chapter begins with a brief
history regarding the origins of deep learning programming and backpropagation.
Next, it introduces some mathematical tools and derivations, which help us in
understanding the concepts to be covered. The remainder of the chapter details
backpropagation and some of its applications within CNNs and RNNs.
Chapter 16, Tensor Processing Unit, this chapter introduces the Tensor Processing Unit
(TPU), a special chip developed at Google for ultra-fast execution of neural network
mathematical operations. In this chapter we are going to compare CPUs and GPUs
with the three generations of TPUs and with Edge TPUs. The chapter will include
code examples of using TPUs.
What you need for this book
To be able to smoothly follow through the chapters, you will need the following
pieces of software:
• TensorFlow 2.0 or higher
• Matplotlib 3.0 or higher
• Scikit-learn 0.18.1 or higher
• NumPy 1.15 or higher
The hardware specifications are as follows:
• Either 32-bit or 64-bit architecture
• 2+ GHz CPU
[ xxi ]