David Paper

TensorFlow 2.x in the Colaboratory Cloud (eBook, PDF)

An Introduction to Deep Learning on Google's Cloud Service

• Format: PDF

Produktbeschreibung

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.

You will:

• 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

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Produktdetails

• Verlag: Apress
• Seitenzahl: 264
• Erscheinungstermin: 13. Januar 2021
• Englisch
• ISBN-13: 9781484266496
• Artikelnr.: 62288293

Autorenporträt

Dr. David Paper is a full professor at Utah State University (USU) in the Management Information Systems department. He has over 30 years of higher education teaching experience. At USU, he has over 26 years teaching in the classroom and distance education over satellite. Dr. Paper has taught a variety of classes at the undergraduate, graduate, and doctorate levels, but he specializes in technology education. He has competency in several programming languages, but his focus is currently on deep learning (Python) and database programming (PyMongo). Dr. Paper has published three technical books for industry professionals, including Web Programming for Business: PHP Object-Oriented Programming with Oracle, Data Science Fundamentals for Python and MongoDB (Apress), and Hands-on Scikit-Learn for Machine Learning Applications: Data Science Fundamentals with Python (Apress). He has authored more than 100 academic publications. Besides growing up in family businesses, Dr. Paper has worked for Texas Instruments, DLS, Inc., and the Phoenix Small Business Administration. He has performed IS consulting work for IBM, AT&T, Octel, Utah Department of Transportation, and the Space Dynamics Laboratory.

Inhaltsangabe

1. Introduction to Deep Learning.- 2. Build Your First Neural Network with Google Colab.- 3. Working with TensorFlow Data.- 4. Working with Other Data.- 5. Classification.- 6. Regression.- 7. Convolutional Neural Networks.- 8. Automated Text Generation.- 9. Sentiment Analysis.- 10. Time Series Forecasting with RNNs.