Additional information
by David Paper (Author)
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.
- 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
Back Jacket
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.
- 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
Author Biography
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.
