Classification Using Neural Networks

Oliver Knocklein
Towards Data Science
8 min readJun 5, 2019

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Neural networks are one of those cool words that are often used to lend credence to research. But what exactly are they? After reading this article you should have a rough understanding of the internal mechanics of neural nets, and convolution neural networks, and be able to code your own simple neural network model in Python.

What are Neural Networks

Neural nets take inspiration from the learning process occurring in human brains. They consists of an artificial network of functions, called parameters, which allows the computer to learn, and to fine tune itself, by analyzing new data. Each parameter, sometimes also referred to as neurons, is a function which produces an output, after receiving one or multiple inputs. Those outputs are then passed to the next layer of neurons, which use them as inputs of their own function, and produce further outputs. Those outputs are then passed on to the next layer of neurons, and so it continues until every layer of neurons have been considered, and the terminal neurons have received their input. Those terminal neurons then output the final result for the model.

Figure 1 shows a visual representation of such a network. The initial input is x, which is then passed to the first layer of neurons (the h bubbles in Figure 1), where three functions consider the input that they receive, and generate an output. That output is then passed to the second layer (the g bubbles in Figure 1). There further output is calculated, based on the output from the first layer. That…

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