Hands-On Deep Learning for Images with TensorFlow: Build intelligent computer vision applications using TensorFlow and Keras

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Book Description

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TensorFlow 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 learn

• Build machine learning models particularly focused on the MNIST digits

• Work with Docker and Keras to build an image classifier

• Understand natural language models to process text and images

• Prepare your dataset for machine learning

• Create classical, convolutional, and deep neural networks

• Create a RESTful image classification server

Who this book is for

Hands-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.

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Table of Contents

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1. Machine Learning Toolkit

• Installing Docker

• The machine learning Docker file

• Sharing data

• Machine learning REST service

• Summary

2. Image Data

• MNIST digits

• Tensors – multidimensional arrays

• Turning images into tensors

• Turning categories into tensors

• Summary

3. Classical Neural Network

• Comparison between classical dense neural networks

• Activation and nonlinearity

• Softmax

• Training and testing data

• Dropout and Flatten

• Solvers

• Hyperparameters

• Grid searches

• Summary

4. A Convolutional Neural Network

• Convolutions

• Pooling

• Building a convolutional neural network

• Deep neural network

• Summary

5. An Image Classification Server

• REST API definition

• Trained models in Docker containers

• Making predictions

• Summary