Ballard - Hands-on deep learning for images with TensorFlow build intelligent computer vision applications using TensorFlow and Keras

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Will Ballard is the chief technology officer at GLG, responsible for engineering and IT. He was also responsible for the design and operation of large data centers that helped run site services for customers including Gannett, Hearst Magazines, NFL.com, NPR, The Washington Post, and Whole Foods. He has also held leadership roles in software development at NetSolve (now Cisco), NetSpend, and Works.com (now Bank of America).

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TensorFlow is Google's popular offering for machine learning and deep learning. It has quickly become a popular choice of tool for performing fast, efficient, and accurate deep learning tasks.

This book shows you practical implementations of real-world projects, teaching you how to leverage TensorFlow 's capabilities to perform efficient deep learning. In this book, you will be acquainted with the different paradigms of performing deep learning, such as deep neural nets, convolutional neural networks, recurrent neural networks, and more, and how they can be implemented using TensorFlow.

This will be demonstrated with the help of end-to-end implementations of three real-world projects on popular topic areas such as natural language processing, image classification, and fraud detection.

By the end of this book, you will have mastered all the concepts of deep learning and their implementations with TensorFlow and Keras.

This book is for application developers, data scientists, and machine learning practitioners looking to integrate machine learning into application software and master deep learning by implementing practical projects in TensorFlow. Knowledge of Python programming and the basics of deep learning is required to get the most out of this book.

, Machine Learning Toolkit , looks into installing Docker, setting up a machine learning Docker file, sharing data back with your host computer, and running a REST service to provide the environment.

, Image Data , teaches MNIST digits, how to acquire them, how tensors are really just multidimensional arrays, and how we can encode image data and categorical or classification data as a tensor. Then, we have a quick review and a cookbook approach to consider dimensions and tensors, in order to get data prepared for machine learning.

, Classical Neural Network , covers an awful lot of material! We see the structure of the classical, or dense, neural network. We learn about activation, nonlinearity, and softmax. We then set up testing and training data and learn how to construct the network with Dropout and Flatten. We also learn all about solvers, or how machine actually learns. We then explore hyperparameters, and finally, we fine-tune our model by means of grid search.

, A Convolutional Neural Network , teaches you convolutions, which are a loosely connected way of moving over an image to extract features. Then we learn about pooling, which summarizes the most important features. We will build a convolutional neural network using these techniques and we combine many layers of convolution and pooling in order to generate a deep neural network.

, An Image Classification Server , uses a Swagger API definition to create a REST API model, which then declaratively generates the Python framework in order for us to serve that API. Then, we create a Docker container that captures not only our running code (that is, our service) but also our pre-trained machine learning model. This then forms a package so that we are able to deploy and use our container. Finally, we use this container to serve and make predictions.