Getting Started with TensorFlow (Paperback)

Giancarlo Zaccone



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



  • 獲得市場上第一本展示TensorFlow關鍵方面、工作原理以及如何應用於第二代機器學習的書籍

  • 想在數據科學領域進行更快、更準確的計算嗎?這本書將讓您認識一個全新的、令人耳目一新的庫-TensorFlow!

  • 深入探索下一代數值計算,並通過這本快速指南充分利用您的數據






  • 在Python環境中安裝和使用TensorFlow來解決數學問題

  • 了解基本的機器學習和深度學習概念

  • 訓練和測試神經網絡以擬合您的數據模型

  • 使用回歸算法進行預測

  • 使用聚類程序分析您的數據

  • 開發用於聚類和數據分類的算法

  • 使用GPU計算分析大數據