46
Internet of Things (IoT)
with hundreds of cars in line or may be disastrous in health with malfunction of the
pacemaker, and the lists goes on.
3.5 Deep Learning and its Role for IoT
In this section, an introduction to deep learning and its suitability to IoT datasets
are discussed. In deep learning, sometimes referred to as fog clouding as shown in
Figure 3.2, edge nodes are more capable of local computation among them through the
network. In the more technical sense, deep learning is a single or set of algorithms that
learn in the layer and mimics the brain as well, so that a hierarchy of complex concepts
out of simple ones may be built by a computer.
3.5.1 How Does a Computer Learn? and How Do Deep Learning Algorithms Learn?
To understand deep learning with ease, it is required to have knowledge of how thinking and learning are done by computer with a top-down approach with all possible rules
for its operations. But, now, it has a paradigm shift to have a bottom-up approach where
the computer can learn from labeled data and the system is suitably trained based on
responses. A notable example of this scenario is playing chess with 32 chess piece as
primitives and 64 actions squares, but in real-life scenario, deep earning presents huge
problem space with infinite alternatives. Such a huge problem suffers from the curse of
dimensionality, making the computer difficult to learn. In the case of text mining, such as
sentiment analysis or recognizing words or face recognition, the data available are intuitive (or sparse) in nature and the problem domain is not finite; hence, it is very difficult to
represent possible combinations to have meaningful analysis.
Deep learning is a machine learning algorithm that is best suited to address these
intuitive problems that are not only hard to learn but also have no rules with high dimensionality (Najafabadi et al., 2015), in order to deal with hidden circumstances without
knowing the rules a priori.
Network devices
Social network
Medical
Smart grid
Intelligence through deep learning network
Intermediate
network
Cloud
FIGURE 3.2
Deep learning and IoT.
Internet of Things (IoT)
with hundreds of cars in line or may be disastrous in health with malfunction of the
pacemaker, and the lists goes on.
3.5 Deep Learning and its Role for IoT
In this section, an introduction to deep learning and its suitability to IoT datasets
are discussed. In deep learning, sometimes referred to as fog clouding as shown in
Figure 3.2, edge nodes are more capable of local computation among them through the
network. In the more technical sense, deep learning is a single or set of algorithms that
learn in the layer and mimics the brain as well, so that a hierarchy of complex concepts
out of simple ones may be built by a computer.
3.5.1 How Does a Computer Learn? and How Do Deep Learning Algorithms Learn?
To understand deep learning with ease, it is required to have knowledge of how thinking and learning are done by computer with a top-down approach with all possible rules
for its operations. But, now, it has a paradigm shift to have a bottom-up approach where
the computer can learn from labeled data and the system is suitably trained based on
responses. A notable example of this scenario is playing chess with 32 chess piece as
primitives and 64 actions squares, but in real-life scenario, deep earning presents huge
problem space with infinite alternatives. Such a huge problem suffers from the curse of
dimensionality, making the computer difficult to learn. In the case of text mining, such as
sentiment analysis or recognizing words or face recognition, the data available are intuitive (or sparse) in nature and the problem domain is not finite; hence, it is very difficult to
represent possible combinations to have meaningful analysis.
Deep learning is a machine learning algorithm that is best suited to address these
intuitive problems that are not only hard to learn but also have no rules with high dimensionality (Najafabadi et al., 2015), in order to deal with hidden circumstances without
knowing the rules a priori.
Network devices
Social network
Medical
Smart grid
Intelligence through deep learning network
Intermediate
network
Cloud
FIGURE 3.2
Deep learning and IoT.