A Deeper Understanding Is Needed To Improve Neural Networks

The development of neural networks is not a new thing. In fact, neural networks have been around since the 1940s, according to MIT News . No one has really been interested in the application of this technology until now. To begin, let’s define a neural network. According to the definition by Investopedia : “A neural network is a series of algorithms that endeavors to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates. Neural networks can adapt to changing input; so, the network generates the best possible result without needing to redesign the output criteria.” The inspiration for algorithms comes directly from the biological function of the human brain. As MIT News notes, "The one-layer networks of the 1960s and the two- to three-layer networks of the 1980s [have blossomed] into the 10-, 15-, even 50-layer networks of today." So, when we talk about deep learning, in English, it means that today there is layer upon layer of networks all interconnected in functionality and communication. Neural networks are systems of hardware and/or software patterned after the workings of human neurons in the human brain. There are layers upon layers of data all being associated to perform the act of learning and memory, known as artificial intelligence (AI). I believe that the marriage of data and artificial neural networks are our best chance to move toward real AI. As we move further into the 21st century, we will be working alongside products built on deep learning technology. The problem is that the union of data analytics and neural networks (known as deep learning) are so new that data scientists, upper management and CEOs alike don’t really understand what can be done with this technology. Thus, how can this new technology […]

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