Packt Publishing
Getting Started with TensorFlow: Efficient, scalable, and user-friendly machine learning for all
Key Metrics
- Giancarlo Zaccone
- Packt Publishing
- Paperback
- 9781786468574
- 9.25 X 7.5 X 0.38 inches
- 0.7 pounds
- Computers > Artificial Intelligence - Computer Vision & Pattern Recognition
- English
Secure TransactionBook Description
Get up and running with the latest numerical computing library by Google and dive deeper into your data!
Key Features
- Get the first book on the market that shows you the key aspects TensorFlow, how it works, and how to use it for the second generation of machine learning
- Want to perform faster and more accurate computations in the field of data science? This book will acquaint you with an all-new refreshing library-TensorFlow!
- Dive into the next generation of numerical computing and get the most out of your data with this quick guide
Book Description
Google's TensorFlow engine, after much fanfare, has evolved in to a robust, user-friendly, and customizable, application-grade software library of machine learning (ML) code for numerical computation and neural networks.
This book takes you through the practical software implementation of various machine learning techniques with TensorFlow. In the first few chapters, you'll gain familiarity with the framework and perform the mathematical operations required for data analysis. As you progress further, you'll learn to implement various machine learning techniques such as classification, clustering, neural networks, and deep learning through practical examples.
By the end of this book, you'll have gained hands-on experience of using TensorFlow and building classification, image recognition systems, language processing, and information retrieving systems for your application.
What you will learn
- Install and adopt TensorFlow in your Python environment to solve mathematical problems
- Get to know the basic machine and deep learning concepts
- Train and test neural networks to fit your data model
- Make predictions using regression algorithms
- Analyze your data with a clustering procedure
- Develop algorithms for clustering and data classification
- Use GPU computing to analyze big data
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