What is TensorFlow?
TensorFlow is an open source software library for machine learning developed by Google – Google Brain team. Name TensorFlow derives from the operations which neural networks perform on multidimensional data arrays, often referred to as “tensors”. It is using data flow graphs and is capable of building and training variety of different machine learning algorithms and deep neural networks, but it is general enough to be applicable in a wide variety of other domains as well. Flexible architecture allows deploying computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.
TensorFlow is Google Brain’s second generation machine learning system, released as open source software in 2015. TensorFlow is available on 64-bit Linux, MacOS, and mobile computing platforms including Android and iOS. TensorFlow provides a Python API, as well as C++, Haskell, Java and Go APIs. In 2016 Google announced Tensor Processing Unit (TPU), a custom built for machine learning programmable AI accelerator designed to provide high throughput of low-precision arithmetic.
Among a variety of applications for which TensorFlow is used and listed in the TesnsorFlow website are:
RankBrain – A large-scale deployment of deep neural nets for search ranking on Google.
Inception Image Classification Model – highly accurate computer vision models, starting with the model that won the 2014 Imagenet image classification challenge.
SmartReply – Deep LSTM model to automatically generate email responses
Massively Multitask Networks for Drug Discovery – A deep neural network model for identifying promising drug candidates.
On-Device Computer Vision for OCR – computer vision model to do optical character recognition to enable real-time translation.
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Recommended reading list:
|Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
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, minimal theory, and two production-ready Python frameworks—scikit-learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started.
Explore the machine learning landscape, particularly neural nets
Use scikit-learn to track an example machine-learning project end-to-end
Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods
Use the TensorFlow library to build and train neural nets
Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning
Learn techniques for training and scaling deep neural nets
Apply practical code examples without acquiring excessive machine learning theory or algorithm details
|TensorFlow Machine Learning Cookbook
This guide starts with the fundamentals of the TensorFlow library which includes variables, matrices, and various data sources. Moving ahead, you will get hands-on experience with Linear Regression techniques with TensorFlow. The next chapters cover important high-level concepts such as neural networks, CNN, RNN, and NLP.
Once you are familiar and comfortable with the TensorFlow ecosystem, the last chapter will show you how to take it to production.
What you will learn
Become familiar with the basics of the TensorFlow machine learning library
Get to know Linear Regression techniques with TensorFlow
Learn SVMs with hands-on recipes
Implement neural networks and improve predictions
Apply NLP and sentiment analysis to your data
Master CNN and RNN through practical recipes
Take TensorFlow into production
|Learning TensorFlow: A Guide to Building Deep Learning Systems
TensorFlow is currently the leading open-source software for deep learning, used by a rapidly growing number of practitioners working on computer vision, Natural Language Processing (NLP), speech recognition, and general predictive analytics. This book is an end-to-end guide to TensorFlow designed for data scientists, engineers, students and researchers.
With this book you will learn how to:
Get up and running with TensorFlow, rapidly and painlessly
Build and train popular deep learning models for computer vision and NLP
Apply your advanced understanding of the TensorFlow framework to build and adapt models for your specific needs
Train models at scale, and deploy TensorFlow in a production setting
|TensorFlow for Machine Intelligence: A Hands-On Introduction to Learning Algorithms
TensorFlow, a popular library for machine learning, embraces the innovation and community-engagement of open source, but has the support, guidance, and stability of a large corporation. Because of its multitude of strengths, TensorFlow is appropriate for individuals and businesses ranging from startups to companies as large as, well, Google. TensorFlow is currently being used for natural language processing, artificial intelligence, computer vision, and predictive analytics. TensorFlow, open sourced to the public by Google in November 2015, was made to be flexible, efficient, extensible, and portable. Computers of any shape and size can run it, from smartphones all the way up to huge computing clusters. This book is for anyone who knows a little machine learning (or not) and who has heard about TensorFlow, but found the documentation too daunting to approach. It introduces the TensorFlow framework and the underlying machine learning concepts that are important to harness machine intelligence. After reading this book, you should have a deep understanding of the core TensorFlow API.
|Machine Learning with TensorFlow
Being able to make near-real-time decisions is becoming increasingly crucial. To succeed, we need machine learning systems that can turn massive amounts of data into valuable insights. But when you're just starting out in the data science field, how do you get started creating machine learning applications? The answer is TensorFlow, a new open source machine learning library from Google. The TensorFlow library can take your high level designs and turn them into the low level mathematical operations required by machine learning algorithms.
Machine Learning with TensorFlow teaches readers about machine learning algorithms and how to implement solutions with TensorFlow. It starts with an overview of machine learning concepts and moves on to the essentials needed to begin using TensorFlow. Each chapter zooms into a prominent example of machine learning. Readers can cover them all to master the basics or skip around to cater to their needs. By the end of this book, readers will be able to solve classification, clustering, regression, and prediction problems in the real world.