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2021 | Book

TensorFlow 2.x in the Colaboratory Cloud

An Introduction to Deep Learning on Google’s Cloud Service

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About this book

Use TensorFlow 2.x with Google's Colaboratory (Colab) product that offers a free cloud service for Python programmers. Colab is especially well suited as a platform for TensorFlow 2.x deep learning applications. You will learn Colab’s default install of the most current TensorFlow 2.x along with Colab’s easy access to on-demand GPU hardware acceleration in the cloud for fast execution of deep learning models. This book offers you the opportunity to grasp deep learning in an applied manner with the only requirement being an Internet connection. Everything else—Python, TensorFlow 2.x, GPU support, and Jupyter Notebooks—is provided and ready to go from Colab.
The book begins with an introduction to TensorFlow 2.x and the Google Colab cloud service. You will learn how to provision a workspace on Google Colab and build a simple neural network application. From there you will progress into TensorFlow datasets and building input pipelines in support of modeling and testing. You will find coverage of deep learning classification and regression, with clear code examples showing how to perform each of those functions. Advanced topics covered in the book include convolutional neural networks and recurrent neural networks.

This book contains all the applied math and programming you need to master the content. Examples range from simple to relatively complex when necessary to ensure acquisition of appropriate deep learning concepts and constructs. Examples are carefully explained, concise, accurate, and complete to perfectly complement deep learning skill development. Care is taken to walk you through the foundational principles of deep learning through clear examples written in Python that you can try out and experiment with using Google Colab from the comfort of your own home or office.


What You Will LearnBe familiar with the basic concepts and constructs of applied deep learningCreate machine learning models with clean and reliable Python codeWork with datasets common to deep learning applicationsPrepare data for TensorFlow consumptionTake advantage of Google Colab’s built-in support for deep learningExecute deep learning experiments using a variety of neural network modelsBe able to mount Google Colab directly to your Google Drive accountVisualize training versus test performance to see model fit
Who This Book Is For
Readers who want to learn the highly popular TensorFlow 2.x deep learning platform, those who wish to master deep learning fundamentals that are sometimes skipped over in the rush to be productive, and those looking to build competency with a modern cloud service tool such as Google Colab

Table of Contents

Frontmatter
Chapter 1. Introduction to Deep Learning
Abstract
We introduce the basic concepts of deep learning. We use TensorFlow 2.x, the Google cloud service, and Google Drive interaction to make the concepts come alive with Python coding examples.
David Paper
Chapter 2. Build Your First Neural Network with Google Colab
Abstract
We work through a complete deep learning example with Python’s TensorFlow 2.x library in the Google Colab cloud service. We also demonstrate how to link your Google Drive with the Colab cloud service.
David Paper
Chapter 3. Working with TensorFlow Data
Abstract
We introduce TensorFlow datasets (TFDS). We discuss many facets of a TFDS with code examples. We continue with a complete TFDS modeling example.
David Paper
Chapter 4. Working with Other Data
Abstract
In the previous chapter, we showed you how to work with a TFDS. But what if we want to work with another type of dataset? In this chapter, we show you how to work with other types of data with TensorFlow.
David Paper
Chapter 5. Classification
Abstract
Classification is a supervised learning method for predicting a class label for a given example of input data. Although we introduced classification with MNIST, we work through the famous Fashion-MNIST dataset to delve deeper into the topic.
David Paper
Chapter 6. Regression
Abstract
Regression is a supervised learning method for predicting a continuous output of an event based on the relationship between variables (or features) obtained from a dataset. A continuous outcome is a real value such as an integer or floating-point value often quantified as amounts and sizes. Regression is a widely popular type of deep learning modeling.
David Paper
Chapter 7. Convolutional Neural Networks
Abstract
With feedforward neural networks, we achieved good training performance with MNIST and Fashion-MNIST datasets. But images in these datasets are simple and centered within the input space that contains them. That is, they are centered within the pixel matrix that holds them. Input space is all the possible inputs to a model.
David Paper
Chapter 8. Automated Text Generation
Abstract
Feedforward neural nets are generally great for classification and regression problems. CNNs are great for complex image classification. But activations for feedforward nets and CNNs flow only in one direction, from the input layers to the output layer. Since signals flow in only one direction, feedforward and convolutional nets are not ideal if patterns in data change over time. So we need a different network architecture to work with data impacted by time.
David Paper
Chapter 9. Sentiment Analysis
Abstract
We've already demonstrated how to train a character-level RNN to create original text. Now, we create a word-level RNN to analyze sentiment.
David Paper
Chapter 10. Time Series Forecasting with RNNs
Abstract
We’ve already leveraged RNNs for NLP. In this chapter, we create experiments to forecast with time series data. We use the famous Weather dataset to demonstrate both a univariate and a multivariate example.
David Paper
Backmatter
Metadata
Title
TensorFlow 2.x in the Colaboratory Cloud
Author
David Paper
Copyright Year
2021
Publisher
Apress
Electronic ISBN
978-1-4842-6649-6
Print ISBN
978-1-4842-6648-9
DOI
https://doi.org/10.1007/978-1-4842-6649-6

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