pdfcoffee
PrefaceChapter 13, TensorFlow for Mobile and IoT and TensorFlow.js, this chapter focuses onthe math behind DL. The goal of the chapter is to provide readers a glimpse of whatis happening "under the hood" when you play with neural networks. The chapterreviews the high school concepts of derivatives and gradients, and will introduce thegradient descent and backpropagation algorithms commonly used to optimize deeplearning networks.Chapter 14, An Introduction to AutoML, this chapter discusses the cloud environmentand how to utilize it for training and deploying your model. It will cover the stepsneeded to set up Amazon Web Services (AWS) for DL. The steps needed to set upGoogle Cloud Platform for DL applications will also be covered. It will also coverhow to set up Microsoft Azure for DL applications. The chapter will include variouscloud 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, discussesthe math behind deep learning. In the chapter, we'll get "under the hood" and seewhat's going on when we perform deep learning. The chapter begins with a briefhistory regarding the origins of deep learning programming and backpropagation.Next, it introduces some mathematical tools and derivations, which help us inunderstanding the concepts to be covered. The remainder of the chapter detailsbackpropagation 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 networkmathematical operations. In this chapter we are going to compare CPUs and GPUswith the three generations of TPUs and with Edge TPUs. The chapter will includecode examples of using TPUs.What you need for this bookTo be able to smoothly follow through the chapters, you will need the followingpieces of software:• TensorFlow 2.0 or higher• Matplotlib 3.0 or higher• Scikit-learn 0.18.1 or higher• NumPy 1.15 or higherThe hardware specifications are as follows:• Either 32-bit or 64-bit architecture• 2+ GHz CPU[ xxi ]
Preface• 4 GB RAM• At least 10 GB of hard disk space availableDownloading the example codeYou can download the example code files for this book from your account at www.packt.com/. If you purchased this book elsewhere, you can visit www.packtpub.com/support and register to have the files emailed directly to you.You can download the code files by following these steps:1. Log in or register at http://www.packt.com.2. Select the Support tab.3. Click on Code Downloads.4. Enter the name of the book in the Search box and follow the on-screeninstructions.Once the file is downloaded, please make sure that you unzip or extract the folderusing the latest version of:• WinRAR / 7-Zip for Windows• Zipeg / iZip / UnRarX for Mac• 7-Zip / PeaZip for LinuxThe code bundle for the book is also hosted on GitHub at https://github.com/PacktPublishing/Deep-Learning-with-TensorFlow-2-and-Keras. In case there'san update to the code, it will be updated on the existing GitHub repository.We also have other code bundles from our rich catalog of books and videos availableat https://github.com/PacktPublishing/. Check them out!Download the color imagesWe also provide a PDF file that has color images of the screenshots/diagrams usedin this book. You can download it here:https://static.packt-cdn.com/downloads/9781838823412_ColorImages.pdf[ xxii ]
- Page 2 and 3: Deep Learning withTensorFlow 2 and
- Page 4 and 5: packt.comSubscribe to our online di
- Page 6 and 7: I want to thank my kids, Aurora, Le
- Page 8 and 9: Sujit Pal is a Technology Research
- Page 10 and 11: Table of ContentsPrefacexiChapter 1
- Page 12 and 13: [ iii ]Table of ContentsConverting
- Page 14 and 15: Table of ContentsSo what is the pro
- Page 16 and 17: [ vii ]Table of ContentsChapter 10:
- Page 18 and 19: Table of ContentsPretrained models
- Page 20 and 21: PrefaceDeep Learning with TensorFlo
- Page 22 and 23: • Supervised learning, in which t
- Page 24 and 25: PrefaceThe complexity of deep learn
- Page 26 and 27: PrefaceFigure 5: Adoption of deep l
- Page 28 and 29: Chapter 1, Neural Network Foundatio
- Page 32 and 33: ConventionsThere are a number of te
- Page 34: PrefaceReferences1. Deep Learning w
- Page 37 and 38: Neural Network Foundations with Ten
- Page 39 and 40: Neural Network Foundations with Ten
- Page 41 and 42: Neural Network Foundations with Ten
- Page 43 and 44: Neural Network Foundations with Ten
- Page 45 and 46: Neural Network Foundations with Ten
- Page 47 and 48: Neural Network Foundations with Ten
- Page 49 and 50: Neural Network Foundations with Ten
- Page 51 and 52: Neural Network Foundations with Ten
- Page 53 and 54: Neural Network Foundations with Ten
- Page 55 and 56: Neural Network Foundations with Ten
- Page 57 and 58: Neural Network Foundations with Ten
- Page 59 and 60: Neural Network Foundations with Ten
- Page 61 and 62: Neural Network Foundations with Ten
- Page 63 and 64: Neural Network Foundations with Ten
- Page 65 and 66: Neural Network Foundations with Ten
- Page 67 and 68: Neural Network Foundations with Ten
- Page 69 and 70: Neural Network Foundations with Ten
- Page 71 and 72: Neural Network Foundations with Ten
- Page 73 and 74: Neural Network Foundations with Ten
- Page 75 and 76: Neural Network Foundations with Ten
- Page 77 and 78: Neural Network Foundations with Ten
- Page 79 and 80: Neural Network Foundations with Ten
Preface
• 4 GB RAM
• At least 10 GB of hard disk space available
Downloading the example code
You can download the example code files for this book from your account at www.
packt.com/. If you purchased this book elsewhere, you can visit www.packtpub.
com/support and register to have the files emailed directly to you.
You can download the code files by following these steps:
1. Log in or register at http://www.packt.com.
2. Select the Support tab.
3. Click on Code Downloads.
4. Enter the name of the book in the Search box and follow the on-screen
instructions.
Once the file is downloaded, please make sure that you unzip or extract the folder
using the latest version of:
• WinRAR / 7-Zip for Windows
• Zipeg / iZip / UnRarX for Mac
• 7-Zip / PeaZip for Linux
The code bundle for the book is also hosted on GitHub at https://github.com/
PacktPublishing/Deep-Learning-with-TensorFlow-2-and-Keras. In case there's
an update to the code, it will be updated on the existing GitHub repository.
We also have other code bundles from our rich catalog of books and videos available
at https://github.com/PacktPublishing/. Check them out!
Download the color images
We also provide a PDF file that has color images of the screenshots/diagrams used
in this book. You can download it here:
https://static.packt-cdn.com/downloads/9781838823412_ColorImages.pdf
[ xxii ]
Change language