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Chapter 13
In this section, we have discussed how to use TensorFlow.js with both vanilla
JavaScript and with Node.js with sample applications for both the browser and for
backend computation.
Summary
In this chapter we have discussed how to use TensorFlow Lite for mobile devices
and IoT and deployed real applications on Android devices. Then, we also talked
about Federated Learning for distributed learning across thousands (millions) of
mobile devices, taking into account privacy concerns. The last section of the chapter
was devoted to TensorFlow.js for using TensorFlow with vanilla JavaScript or with
Node.js.
The next chapter is about AutoML, a set of techniques used to enable domain
experts who are unfamiliar with machine learning technologies to use ML
techniques easily.
References
1. Quantization-aware training https://github.com/tensorflow/tensorflow/
tree/r1.13/tensorflow/contrib/quantize
2. Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-
Only Inference, Benoit Jacob, Skirmantas Kligys, Bo Chen, Menglong Zhu,
Matthew Tang, Andrew Howard, Hartwig Adam, Dmitry Kalenichenko
(Submitted on 15 Dec 2017); https://arxiv.org/abs/1712.05877
3. MobileNetV2: Inverted Residuals and Linear Bottlenecks, Mark Sandler, Andrew
Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen (Submitted
on 13 Jan 2018 (v1), last revised 21 Mar 2019 (v4)) https://arxiv.org/
abs/1806.08342
4. MnasNet: Platform-Aware Neural Architecture Search for Mobile, Mingxing Tan,
Bo Chen, Ruoming Pang, Vijay Vasudevan, Mark Sandler, Andrew Howard,
Quoc V. Le https://arxiv.org/abs/1807.11626
5. DeepLab: Semantic Image Segmentation with Deep Convolutional Nets,
Atrous Convolution, and Fully Connected CRFs, Liang-Chieh Chen, George
Papandreou, Iasonas Kokkinos, Kevin Murphy, and Alan L. Yuille, May
2017, https://arxiv.org/pdf/1606.00915.pdf
6. BERT: Pre-training of Deep Bidirectional Transformers for Language
Understanding, Jacob Devlin, Ming-Wei Chang, Kenton Lee, Kristina
Toutanova (Submitted on 11 Oct 2018 (v1), last revised 24 May 2019 v2))
https://arxiv.org/abs/1810.04805
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