June 2019
Intermediate to advanced
308 pages
7h 21m
English
Content preview from Hands-On Deep Learning for IoT
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IoT devices, edge/fog computing, and cloud perspective
As we mentioned in Chapter 1, The End-to-End Life Cycle of IoT, an end-to-end IoT solution consists of three different key components or layers, which mainly includes IoT devices, edge/fog computing, and cloud platforms. All these components have their own challenges in regard to DL implementations. In the following list, we will briefly discuss those challenges:
- Resource-constrained IoT Devices: IoT devices are resource-constrained in terms of processors, battery energy, memory, and network connectivity. DL models developed for traditional computers may not be directly useful in IoT devices. Importantly, the training of DL models is not possible in IoT devices as the training process ...
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