TensorFlow™ is a symbolic mathematics system based on Dataflow programming. It is widely used in the programming implementation of machine learning algorithms. It is the former product of DistBelief, Google’s neural network algorithm library.
Tensorflow has a multi-tier architecture that can be deployed on a variety of servers, PCS and web pages and supports GPU and TPU high-performance numerical computing. Tensorflow is widely used in Google product development and scientific research in various fields.
TensorFlow is developed and maintained by Google’s AI team, Google Brain, It has several projects including TensorFlow Hub, TensorFlow Lite, TensorFlow Research Cloud, and various Application Programming interfaces (apis). As of November 9, 2015, TensorFlow is open source under the Apache 2.0 Open Source License.
Today’s book list will introduce several excellent books on TensorFlow in the future.
TensorFlow book list
Toward TensorFlow 2.0: A quick introduction to Deep learning Application programming
This book explores the application of TensorFlow 2.0, an open source machine learning software library, covering a variety of popular application scenarios, including image recognition, natural language talking robots, generative network based image style transfer, text sentiment analysis, and more.
This book is written for “Landing apps”, with lots of code and comments for each chapter to help you get started and get started faster. The first two chapters of the book introduce Python usage and the basics of TensorFlow, respectively, and the last chapter explores how to deploy tensorFlow-trained models into a production environment. This book is of great reference value to technicians who are interested in conducting research in related fields and producing prototypes quickly.
The authors introduce
Life of Lebanon but
Professor, School of Computer, Zhejiang University
Deep learning has been gradually applied in the industrial field, especially its combination with the Internet of Things, which has broad development space in various industrial scenarios such as smart home, smart city, smart transportation, smart medical care, smart education and smart industry.
Ya-bo dong
Associate Professor, School of Computer science, Zhejiang University, Deputy Director of Artificial Intelligence Research Institute
In collaboration with the author of this book, his extensive experience in TensorFlow development enabled the project to proceed smoothly. Lucky to be able to see the book sample chapter, the book is short and concise, there are a lot of practical examples. This book provides a quick introduction to AI system development based on TensorFlow 2.0.
MouLei)
Principal of artificial intelligence Detection project of Seismic data Quality, Institute of Geophysics, China Earthquake Administration
The biggest difference between this wave of ARTIFICIAL intelligence and what we have discussed in the past is that it has been rapidly applied in the industrial field.
TensorFlow of actual combat
TensorFlow In Action aims to guide you through TensorFlow in an easy-to-understand language (based on the version 1.0 API). In TensorFlow In Action we discussed the fundamentals of TensorFlow, the differences and similarities between TF and other frameworks.
Various types of deep neural networks are completely implemented with specific codes: AutoEncoder, MLP, CNN (AlexNet, VGGNet, Inception Net, ResNet), Word2Vec, RNN (LSTM, Bi-rnn) and Deep Reinforcement Learning(Policy Network, Value Network). TensorFlow In Action also covers TensorBoard, multi-GPU parallelism, distributed parallelism, Tf. Learn, and other tF. Contrib components.
TensorFlow In Action aims to give readers a quick introduction to TensorFlow and deep learning, and quickly turn ideas into practical models in industry or research.
Author’s brief introduction
Huang Wenjian, PPmoney big Data algorithm director, is responsible for the group’s risk control, financial management, Internet securities and other business data mining work.
At Chicago-based Uptake, Tang led the team that built a data science engine for condition and health monitoring for multiple iot domains, as well as the company’s predictive model engine, which is used in aerospace, energy and other large machinery sectors.
Deep learning principles and TensorFlow practices
Principles of Deep Learning and TensorFlow Practice mainly introduces the basic principles of deep learning and the basic usage methods of TensorFlow system. TensorFlow is one of the outstanding computing systems in the field of machine learning and deep learning. “Principles of Deep Learning and TensorFlow Practice” introduces the detailed methods and steps of developing machine learning applications using TensorFlow with examples.
Meanwhile, Principles of Deep Learning and TensorFlow Practice focuses on explaining the theoretical knowledge of convolutional neural network for image recognition and cyclic neural network for natural language processing as well as the realization method of TensorFlow, and describes the application scope and effect of deep learning technology combined with practical scenes and examples.
Principles of Deep Learning and TensorFlow Practice is a great book for readers who are interested in machine learning and deep learning, or those who have some knowledge of deep learning theory and want to try more engineering practices, or those who have more experience with engineering products and want to learn deep learning theory.
Author’s brief introduction
Yan Yu, Bena Information (dolphin browser) research and development vice president. In 2007, I joined Microsoft Asia Engineering Institute, and in 2011, I joined Baina Information To take charge of overseas business line. I have done several projects from zero to one, and now I am committed to the research and application of AI and big data products.
Mo Yu worked for Microsoft and Dolphin Browser, developing search engines, music retrieval/humming search, content delivery recommendation algorithms and conversational robotics. He has been focusing on and practicing large-scale data algorithm performance optimization, search engine, recommendation system and artificial intelligence technology for a long time.
Wang Chen, master of Artificial Intelligence from The University of Edinburgh, UK, is now in charge of artificial intelligence in Baina Information Technology Co., LTD. In the early years, I participated in the Informatics Olympiad and won the first prize in Hebei Province and the third prize in China. I was recommended to enter Sun Yat-sen University. During the university, in the ACM competition has also won many achievements.
Toward TensorFlow 2.0: A quick introduction to Deep learning Application programming
TensorFlow is an artificial intelligence learning system developed by Google. It is an open source software library for numerical computation. TensorFlow Deep Learning Algorithm principle and Programming Practice introduces TensorFlow deep learning algorithm principle and programming skills in detail in the form of basic + practice.
Through the book, readers will not only have a systematic understanding of deep learning knowledge, but also have a deeper understanding of the process of using TensorFlow to design deep learning algorithms.
The authors introduce
Ziyang Jiang, with years of professional programming experience, has participated in many deep learning projects related to robot target recognition and positioning, and is good at image recognition algorithm and speech recognition algorithm. Related industries include finance, securities, automobile, public security and other fields.
In recent years, I have conducted in-depth research on machine learning and deep learning. With the emergence of TensorFlow, I began to transfer my energy to the research on the principle of TensorFlow deep learning algorithm, and I have specially derived most of the algorithms, so I have a unique understanding and in-depth understanding of this framework.
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