Packt Publishing, 2017. — 174 p. — ISBN: 978-1-78728-277-3. This book is your guide to exploring the possibilities in the field of deep learning, making use of Google's TensorFlow. You will learn about convolutional neural networks, and logistic regression while training models for deep learning to gain key insights into your data. Dan Van Boxel’s Deep Learning with TensorFlow...
Apress, 2020. — 563 p. — ISBN-13 (electronic): 978-1-4842-5349-6. Build your own pipeline based on modern TensorFlow approaches rather than outdated engineering concepts. This book shows you how to build a deep learning pipeline for real-life TensorFlow projects. You’ll learn what a pipeline is and how it works so you can build a full application easily and rapidly. Then...
O’Reilly, 2017. — 581 p. — ISBN: 978-1-491-96229-9. Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how. By using concrete examples,...
Packt Publishing, 2017. — 536 p. — ISBN: 978-1788293594, 1788293592. Take the next step in implementing various common and not-so-common neural networks with Tensorflow 1.x Key Features Skill up and implement tricky neural networks using Google's TensorFlow 1.x An easy-to-follow guide that lets you explore reinforcement learning, GANs, autoencoders, multilayer perceptrons and...
O’Reilly Media, 2017. — 242 p. — ISBN: 978-1-491-97851-1. Roughly inspired by the human brain, deep neural networks trained with large amounts of data can solve complex tasks with unprecedented accuracy. This practical book provides an end-to-end guide to TensorFlow, the leading open-source software library that helps you build and train neural networks for computer vision,...
Apress, 2018. — 176 p. — ISBN-13 (electronic): 978-1-4842-3516-4. Build deep learning applications, such as computer vision, speech recognition, and chatbots, using frameworks such as TensorFlow and Keras. This book helps you to ramp up your practical know-how in a short period of time and focuses you on the domain, models, and algorithms required for deep learning...
Packt Publishing, 2018. — 320 p. — ISBN: 1788398068. Leverage the power of Tensorflow to design deep learning systems for a variety of real-world scenarios TensorFlow is one of the most popular frameworks used for machine learning and, more recently, deep learning. It provides a fast and efficient framework for training different kinds of deep learning models, with very high...
Packt Publishing, 2019. — 610 p. — ISBN: 978-1-78883-064-5. A practical guide to building high performance systems for object detection, segmentation, video processing, smartphone applications, and more. Computer vision solutions are becoming increasingly common, making their way in fields such as health, automobile, social media, and robotics. This book will help you explore...
Independently published, 2018. — 364 p. — ISBN: 1720092257. Tensorflow is the most popular Deep Learning Library out there. It has fantastic graph computations feature which helps data scientist to visualize his designed neural network using TensorBoard. This Machine learning library supports both Convolution as well as Recurrent Neural network. It supports parallel processing...
2nd Edition. — Apress, 2019. — 240 p. — ISBN-13 (electronic): 978-1-4842-5407-3. Explore the new Java programming language features and APIs introduced in Java 10 through Java 13. Java 13 Revealed is for experienced Java programmers looking to migrate to Java 13. Author Kishori Sharan begins by covering how to use local variable type inference to improve readability of your...
O’Reilly, 2020. — 479 p. — ISBN: 978-1-492-05204-3. Neural networks are getting smaller. Much smaller. The OK Google team, for example, has run machine learning models that are just 14 kilobytes in size — small enough to work on the digital signal processor in an Android phone. With this practical book, you’ll learn about TensorFlow Lite for Microcontrollers, a miniscule...
Packt Publishing, 2017. — 320 p. Delve into neural networks, implement deep learning algorithms, and explore layers of data abstraction with the help of this comprehensive TensorFlow guide. Deep learning is the step that comes after machine learning, and has more advanced implementations. Machine learning is not just for academics anymore, but is becoming a mainstream practice...
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