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Hands-On Deep Learning for Images with TensorFlow: Build intelligent computer vision applications using TensorFlow and Keras
Explore TensorFlow's capabilities to perform efficient deep learning on images
Key FeaturesDiscover image processing for machine visionBuild an effective image classification system using the power of CNNsLeverage TensorFlow’s capabilities to perform efficient deep learningBook DescriptionTensorFlow is Google’s popular offering for machine learning and deep learning, quickly becoming a favorite tool for performing fast, efficient, and accurate deep learning tasks.
Hands-On Deep Learning for Images with TensorFlow shows you the practical implementations of real-world projects, teaching you how to leverage TensorFlow’s capabilities to perform efficient image processing using the power of deep learning. With the help of this book, you will get to grips with the different paradigms of performing deep learning such as deep neural nets and convolutional neural networks, followed by understanding how they can be implemented using TensorFlow.
By the end of this book, you will have mastered all the concepts of deep learning and their implementation with TensorFlow and Keras.
What you will learnBuild machine learning models particularly focused on the MNIST digitsWork with Docker and Keras to build an image classifierUnderstand natural language models to process text and imagesPrepare your dataset for machine learningCreate classical, convolutional, and deep neural networksCreate a RESTful image classification serverWho this book is forHands-On Deep Learning for Images with TensorFlow is for you if you are an application developer, data scientist, or machine learning practitioner looking to integrate machine learning into application software and master deep learning by implementing practical projects in TensorFlow. Knowledge of Python programming and basics of deep learning are required to get the best out of this book.
Table of ContentsMachine Learning ToolkitImage DataClassic Neural NetworkConvulutional Neural NetworkImage Classification Server
Key FeaturesDiscover image processing for machine visionBuild an effective image classification system using the power of CNNsLeverage TensorFlow’s capabilities to perform efficient deep learningBook DescriptionTensorFlow is Google’s popular offering for machine learning and deep learning, quickly becoming a favorite tool for performing fast, efficient, and accurate deep learning tasks.
Hands-On Deep Learning for Images with TensorFlow shows you the practical implementations of real-world projects, teaching you how to leverage TensorFlow’s capabilities to perform efficient image processing using the power of deep learning. With the help of this book, you will get to grips with the different paradigms of performing deep learning such as deep neural nets and convolutional neural networks, followed by understanding how they can be implemented using TensorFlow.
By the end of this book, you will have mastered all the concepts of deep learning and their implementation with TensorFlow and Keras.
What you will learnBuild machine learning models particularly focused on the MNIST digitsWork with Docker and Keras to build an image classifierUnderstand natural language models to process text and imagesPrepare your dataset for machine learningCreate classical, convolutional, and deep neural networksCreate a RESTful image classification serverWho this book is forHands-On Deep Learning for Images with TensorFlow is for you if you are an application developer, data scientist, or machine learning practitioner looking to integrate machine learning into application software and master deep learning by implementing practical projects in TensorFlow. Knowledge of Python programming and basics of deep learning are required to get the best out of this book.
Table of ContentsMachine Learning ToolkitImage DataClassic Neural NetworkConvulutional Neural NetworkImage Classification Server
98 pages, Kindle Edition
Published July 31, 2018
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4 people want to read
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February 12, 2021
This is a quite short book (84 pages) that quickly goes through several topics from installing Docker containers to the basics of neural network analysis of the MNIST database. I initially thought that the book was not really well focused in the initial chapter, which is one of the longest and purely decidated to installation issues, too complex in my view. But then I liked the brief introduction to tensors, and activation functions, and the rapid description and coding of basic networks (fully connected and convolutional) in keras. The last chapter (setting up a server from a docker container) is again out of topic in my opinion but may be of interest for other readers.
In summary, a very too short book with some details of interest for people starting to use Keras.
In summary, a very too short book with some details of interest for people starting to use Keras.
Displaying 1 of 1 review