Advanced Data Analytics Using Python_ With Machine Learning, Deep Learning and NLP Examples ( 2023)

CHAPTER 5
Deep Learning
and Neural Networks
Neural networks, specifically known as artificial neural networks
(ANNs), were developed by the inventor of one of the first neurocomputers,
Dr. Robert Hecht-Nielsen. He defines a neural network as follows:
“…a computing system made up of a number of simple, highly
interconnected processing elements, which process information by their
dynamic state response to external inputs.”
Customarily, neutral networks are arranged in multiple layers. The
layers consist of several interconnected nodes containing an activation
function. The input layer, communicating to the hidden layers, delineates
the patterns. The hidden layers are linked to an output layer.
Neural networks have many uses. As an example, you can cite the fact
that in a passenger load prediction in the airline domain, passenger load
in month t is heavily dependent on t-12 months of data rather on t-1 or t-2
data. Hence, the neural network normally produces a better result than
the time-series model or even image classification. In a chatbot dialogue
system, the memory network, which is actually a neural network of a bag
of words of the previous conversation, is a popular approach. There are
many ways to realize a neural network. In this book, I will focus only the
backpropagation algorithm because it is the most popular.
© Sayan Mukhopadhyay 2018
S. Mukhopadhyay, Advanced Data Analytics Using Python,
https://doi.org/10.1007/978-1-4842-3450-1_5
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