Build, scale, and deploy deep neural network models using the star libraries in Python
Key Features
Book Description
TensorFlow is the most popular numerical computation library built from the ground up for distributed, cloud, and mobile environments. TensorFlow represents the data as tensors and the computation as graphs.
This book is a comprehensive guide that lets you explore the advanced features of TensorFlow 1.x. Gain insight into TensorFlow Core, Keras, TF Estimators, TFLearn, TF Slim, Pretty Tensor, and Sonnet. Leverage the power of TensorFlow and Keras to build deep learning models, using concepts such as transfer learning, generative adversarial networks, and deep reinforcement learning. Throughout the book, you will obtain hands-on experience with varied datasets, such as MNIST, CIFAR-10, PTB, text8, and COCO-Images.
You will learn the advanced features of TensorFlow1.x, such as distributed TensorFlow with TF Clusters, deploy production models with TensorFlow Serving, and build and deploy TensorFlow models for mobile and embedded devices on Android and iOS platforms. You will see how to call TensorFlow and Keras API within the R statistical software and learn the required techniques for debugging when the TensorFlow API-based code does not work as expected.
This book helps you obtain in-depth knowledge of TensorFlow, making you the go-to person for solving artificial intelligence problems. By the end of this guide, you will have mastered the offerings of TensorFlow and Keras, and gained the skills you need to build smarter, faster, and efficient machine learning and deep learning systems.
What you will learn
Table of Contents
"synopsis" may belong to another edition of this title.
"The book is an ambitious undertaking interweaving between Keras and core TensorFlow libraries. The book delves into complex themes and libraries like Sonnet, distributed TensorFlow with TF Clusters, deploying production models with TensorFlow Serving, TensorFlow mobile and TensorFlow for embedded devices.
In that sense, this is an advanced book. But the author covers deep learning models such as RNN, CNN, autoencoders, generative adversarial models and deep reinforcement learning through Keras.
Armando has clearly drawn upon his experience to make this complex journey easier for readers"
--Ajit Jaokar, Data Science for IoT Course Creator and Lead Tutor at the University of Oxford / Principal Data Scientist"About this title" may belong to another edition of this title.
Shipping:
US$ 2.64
Within U.S.A.
Book Description Condition: New. Seller Inventory # 30709913-n
Book Description Condition: New. Seller Inventory # ABLIING23Mar2912160179573
Book Description Condition: New. Seller Inventory # I-9781788292061
Book Description Paperback. Condition: New. Brand New! This item is printed on demand. Seller Inventory # 1788292065
Book Description PAP. Condition: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000. Seller Inventory # L0-9781788292061
Book Description Condition: New. PRINT ON DEMAND Book; New; Fast Shipping from the UK. No. book. Seller Inventory # ria9781788292061_lsuk
Book Description PF. Condition: New. Seller Inventory # 6666-IUK-9781788292061
Book Description Condition: New. Seller Inventory # 30709913-n
Book Description Condition: New. Book is in NEW condition. 1.96. Seller Inventory # 1788292065-2-1
Book Description PAP. Condition: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000. Seller Inventory # L0-9781788292061