Deep learning is enabling the next generation of successful companies. The question is no longer whether enterprises will use deep learning (they will), but how involved each organization becomes with the technology.
Sean Murphy and Allen Leis introduce deep learning from an enterprise perspective and offer an overview of the TensorFlow library and ecosystem. If your company is adopting deep learning, this report will help you navigate the initial decisions you must make—from choosing a deep learning framework to integrating deep learning with the other data analysis systems already in place—to ensure you're building a system capable of handling your specific business needs.
- Explore fundamental concepts and core questions about deep learning in the enterprise
- Familiarize yourself with available framework options, including TensorFlow, MXNet, Microsoft Cognitive Toolkit, and Deeplearning4J
- Dive into TensorFlow's library and ecosystem, from tools such as estimators, prebuilt neural networks, Keras, ML Toolkit for TensorFlow, Tensor2Tensor (T2T), TensorBoard, and TensorFlow Debugger, to model deployment and management with TensorFlow Serving
- See how companies such as Jet.com and PingThings have implemented deep learning to improve the accuracy and enhance the performance of a number of tasks
Table of Contents
- 1. Choosing to Use Deep Learning
- 2. Selecting a Deep Learning Framework
3. Exploring the Library and the Ecosystem
- Improving Network Design and Training
- Deploying Networks for Inference
- Integrating with Other Systems
- Accelerating Training and Inference
- Title: Considering TensorFlow for the Enterprise
- Release date: December 2017
- Publisher(s): O'Reilly Media, Inc.
- ISBN: 9781491995075