finelybook
Mastering TensorFlow 1.x: Advanced machine learning and deep learning concepts using TensorFlow 1.x and Keras
by 作者: Armando Fandango
ISBN-10 书号: 1788292065
ISBN-13 书号: 9781788292061
Publisher Finelybook 出版日期: 2018-01-22
Pages: 474
Publisher Finelybook 出版社: Packt
Mastering TensorFlow 1.x: Advanced machine learning and deep learning concepts using TensorFlow 1.x and Keras
Build,scale,and deploy deep neural network models using the star libraries in Python
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.
The 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
Master advanced concepts of deep learning such as transfer learning,reinforcement learning,generative models and more,using TensorFlow and Keras
Perform supervised (classification and regression) and unsupervised (clustering) learning to solve machine learning tasks
Build end-to-end deep learning (CNN,RNN,and Autoencoders) models with TensorFlow
Scale and deploy production models with distributed and high-performance computing on GPU and clusters
Build TensorFlow models to work with multilayer perceptrons using Keras,TFLearn,and R
Learn the functionalities of smart apps by building and deploying TensorFlow models on iOS and Android devices
Supercharge TensorFlow with distributed training and deployment on Kubernetes and TensorFlow Clusters
