TensorFlow for Dummies
- 4h 30m
- Matthew Scarpino
- John Wiley & Sons (US)
- 2018
Become a machine learning pro!
Google TensorFlow has become the darling of financial firms and research organizations, but the technology can be intimidating and the learning curve is steep. Luckily, TensorFlow For Dummies is here to offer you a friendly, easy-to-follow book on the subject. Inside, you’ll find out how to write applications with TensorFlow, while also grasping the concepts underlying machine learning—all without ever losing your cool!
Machine learning has become ubiquitous in modern society, and its applications include language translation, robotics, handwriting analysis, financial prediction, and image recognition. TensorFlow is Google's preeminent toolset for machine learning, and this hands-on guide makes it easy to understand, even for those without a background in artificial intelligence.
- Install TensorFlow on your computer
- Learn the fundamentals of statistical regression and neural networks
- Visualize the machine learning process with TensorBoard
- Perform image recognition with convolutional neural networks (CNNs)
- Analyze sequential data with recurrent neural networks (RNNs)
- Execute TensorFlow on mobile devices and the Google Cloud Platform (GCP)
If you’re a manager or software developer looking to use TensorFlow for machine learning, this is the book you’ll want to have close by.
About the Author
Matthew Scarpino has been a programmer and engineer for more than 20 years. He has worked extensively with machine learning applications, especially those involving financial analysis, cognitive modeling, and image recognition. Matthew is a Google Certified Data Engineer and blogs about TensorFlow at tfblog.com.
In this Book
-
Introduction
-
Introducing Machine Learning with TensorFlow
-
Getting Your Feet Wet
-
Creating Tensors and Operations
-
Executing Graphs in Sessions
-
Training
-
Analyzing Data with Statistical Regression
-
Introducing Neural Networks and Deep Learning
-
Classifying Images with Convolutional Neural Networks (CNNs)
-
Analyzing Sequential Data with Recurrent Neural Networks (RNNs)
-
Accessing Data with Datasets and Iterators
-
Using Threads, Devices, and Clusters
-
Developing Applications with Estimators
-
Running Applications on the Google Cloud Platform (GCP)
-
The Ten Most Important Classes
-
Ten Recommendations for Training Neural Networks
