Differences and Similarities Between Neural Networks and Deep Learning

Neural networks ‘ and ‘ profound learning ‘ are two such terms that I noticed individuals using interchangeably, although the two differ. Therefore, I describe neural networks as well as deep learning in this article and look at how they vary.

Over the past few years, AI may have gone on in leaps and bounds, but we still have some way to go from genuinely smart machines–machines that can reason and make choices like humans. The response to this can be given by artificial neural networks (ANNs for brief). Human brains consist of linked neuron networks. ANNs are trying to simulate these networks and get computers to behave like interconnected brain cells so they can learn and create more humane choices.

Various sections of the human brain are accountable for processing various pieces of data, and these sections of the brain are organized hierarchically or in layers. As data enters the brain, each neuronal level processes the data gives understanding and transfers the data to the next, higher layer.

For example, in multiple stages your brain can process the delicious smell of pizza wafting from a street café:’ I smell pizza,’ (that’s your input information) …’ I love pizza! (thought)…’ I will get some of that pizza’ (decision making) …’ Oh, but I pledged to cut out junk food ‘ (memory)… ‘Surely one slice won’t hurt?’ (reasoning) ‘I’m doing it!’ (action). ANNs are attempting to simulate this layered approach to processing data and making choices.

An ANN can only have three layers of neurons in its easiest form: the entry layer (where the data enters the system), the concealed layer (where the data is processed) and the output layer (where the system chooses what to do based on the data). But ANNs can become much more complicated, including various hidden layers. Whether it’s three or more layers, as in the human brain, data flows from one layer to another.

Neural Networks vs Deep Learning

The very cutting edge of artificial intelligence (AI) is deep learning. Instead of teaching computers how to process and learn from information (which is how machine learning works), the computer trains itself to process and learn from information through deep learning. Thanks to ANN layers, this is all feasible. As we know ANN has only three layers in its easiest form? Well, an ANN consisting of more than three layers, i.e. an input layer, an output layer, and various concealed layers, is called a “profound neural network,” and that’s what underpins deep learning.

A deep learning system is self-teaching, learning as it does by comparably filtering data to humans through various hidden layers. As you can see, the two are closely connected in that one relies on the other to function. Without neural networks, there would be no deep learning. They’ve been developed further, and today deep neural networks and deep learning achieve outstanding performance on many important problems in computer vision, speech recognition, and natural language processing. They’re being deployed on a large scale by companies such as Google, Microsoft, and Facebook.

Deep learning is a fancy new term for multi-layer neural networks, with the difference now being that we have found effective ways to train deep (5 + hidden layers) neural networks that were previously not possible. Due to three fields of improvement, equipment, methods, and information, profound learning is feasible. Hardware being GPU computing (not popular in 2006 before Jeff Hinton) and techniques being better weight initialization from unsupervised techniques like denouncing auto decoders. And, of course, we now have large public data sets (image network, etc.) and even more, private data (think Google, Uber, Quora) which are what deep neural networks need because they have a few parameters to tune.

Recent breakthroughs in recognition of end-to-end vision using coevolutionary neural networks are the result of deep neural networks. Deep networks are needed because of the complex, highly nonlinear structural data in images that need to be learned on multiple abstractions, i.e. first layer learns to spot edges, the next layer combines learned edges to recognize shapes, etc. The same “deep” architecture can be used for other variants of neural networks such as those with recurrent connections, such as the popular LSTM. This similar stacking fashion of deep LSTM networks leads breakthrough research into highly complex sequential data modeling, such as speech recognition and machine translation.

Differences Between Neural Networks and Deep learning

ANN and deep learning can be easily compared and can be different in some ways. The differences between Neural Networks and Deep learning are described in the following points:

  • Neural networks use neurons that are used in the form of input values and output values to communicate information. Using networks or links, they are used to transfer information. On the other side, deep learning is linked to the conversion and extraction of function that tries to create a connection between stimuli and associated neural reactions in the brain.
  • Application areas for neural networking includes system identification, natural resource management, process control, vehicle control, quantum chemistry, decision making, game playing, face identification, pattern recognition, signal classification, sequence recognition, object recognition, finance, medical diagnosis, visualization, data mining, machine translation, email spam filtering, social network filtering, etc. whereas application of deep learning includes Automatic speech recognition, image recognition, visual art processing, Natural language processing, drug discovery and toxicology, customer relationship management, recommendation engines, Mobile advertising, bioinformatics, Image restoration, etc.
  • Criticism experienced for Neural networks includes problems such as training problems, theoretical problems, hardware problems, practical criticism counterexamples, hybrid methods, whereas for deep learning it is connected to theory, mistakes, cyber danger, etc.

In Conclusion

AI is an incredibly strong and exciting field that will only become more omnipresent and crucial to moving forward and will certainly have enormous effects on society as a whole. These two techniques are some of the very powerful tools of AI to solve complex problems and for us to leverage them in the future will continue to develop and grow.

Deep learning and neural network can be compared in three different stages. They are the stages that tell us the two terms of AI are different from each other which are by definitions, by the components, and by the architecture. Summing up deep learning is the collection and use of many neural networks.


Also read: Basic Introduction of Neural Networks