Overview
- Introduces Google’s Colab cloud service for executing deep learning applications in Python
- Provides examples in downloadable Jupyter notebooks for easy execution
- Teaches foundational principles of deep learning that are needed for success in the field
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Table of contents (10 chapters)
Keywords
About this book
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 Learn
- Be familiar with the basic concepts and constructs of applied deep learning
- Create machine learning models with clean and reliable Python code
- Work with datasets common to deep learning applications
- Prepare data for TensorFlow consumption
- Take advantage of Google Colab’s built-in support for deep learning
- Execute deep learning experiments using a variety of neural network models
- Be able to mount Google Colab directly to your Google Drive account
- Visualize 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
Authors and Affiliations
About the author
Bibliographic Information
Book Title: TensorFlow 2.x in the Colaboratory Cloud
Book Subtitle: An Introduction to Deep Learning on Google’s Cloud Service
Authors: David Paper
DOI: https://doi.org/10.1007/978-1-4842-6649-6
Publisher: Apress Berkeley, CA
eBook Packages: Professional and Applied Computing, Apress Access Books, Professional and Applied Computing (R0)
Copyright Information: David Paper 2021
Softcover ISBN: 978-1-4842-6648-9Published: 14 January 2021
eBook ISBN: 978-1-4842-6649-6Published: 13 January 2021
Edition Number: 1
Number of Pages: XXIII, 264
Number of Illustrations: 5 b/w illustrations
Topics: Machine Learning, Data Structures and Information Theory, Artificial Intelligence