The human brain is an impressive feat of cognitive engineering, giving us the upper-hand when it comes to coming up with original ideas and concepts. We’ve even managed to create the wheel – something that not even our robot friends could do! This shows just how far we’ve come in terms of evolution – proving that humans are true masters of invention.
Deep learning, also called a subset of machine learning which is a specialist with an extremely complex skillset in order to achieve far better results from the same data set. It purely on the basis of NI (Natural Intelligence) mechanics of the biological neuron system. It has a complex skill set because of methods it uses for training i.e. learning in deep learning is based on “learning data representations” rather than “task-specific algorithms.”
ANNs are an emerging discipline and they are the subject of research, study, and emulation for the information-processing capabilities of neurons of the human brain. Sadly many researchers are too quick and pivot the “Human Brain and ANNs” under one pin tip which is causing & creating huge confusion for newcomers. Their point in forms of discovery is entirely different though.
The year 2020 will be remembered as a significant period in human history due to its remarkable strides in progressive innovation, more commonly referred to as “Emerging Technologies.” In this modern era, industrial and business practitioners are expected to establish robust foundational structures for the integration of SMAC (Social, Mobile, Analytics, and Cloud) architecture. It is projected that the prevalence of selection will attain its apex during the decade spanning from 2010 to 2020. The implementation of computerized transformation is of paramount significance for any entity, unless SMAC technology is integrated.
Backpropagation Algorithm – An important mathematical tool for making better and high accuracy predictions in machine learning. This algorithm uses supervised learning methods for training Artificial Neural Networks. The whole idea of training multi-layer perceptrons is to compute the derivatives of the error function or gradient descent concerning weights using the backpropagation algorithm. This algorithm is actually based on the linear algebraic operation with a goal of optimising error function by harnessing its intelligence and provisioning updates.
“Artificial neural networks (ANNs) are biologically inspired computing code with number of simple, highly interconnected processing elements for simulating human brain workings to process information model”.
Artificial neural networks are a type of computing model that takes inspiration from the structure and function of the neural networks found in the human brain. Nevertheless, machine learning has yet to attain authentic biological accuracy, given the present level of implementation and utilization. The process entails receiving multiple inputs and generating a single output.
Deep Learning uses neural networks to create the foundation of the working model architecture. A neural network that took the idea of the human brain working for its basic working model has its basic unit a neuron. Like the human brain artificial neural networks mimic a similar information processing model i.e.: