Deep Learning Vs Neural Networks –  As the name suggests “Neural Networks”, or ANN are inspired by the human brain system though nowhere close to it. Deep learning’s main job is to transform and extract features to establish relationships, the computer trains itself to process and learns from data. Two terms are often used interchangeably. The differences between the two are noticeable though. ANNs are more of a framework than an algorithm.  Deep Learning incorporates Neural Networks within its architecture.

This post is written in simple English for readers who are new to these terms. Ph.D. scholars and professionals with experience are welcome to comment to improve this post.

How Neural Network Algorithms Works: An Overview

Neural Networks – What is it? – Deep Learning Vs Neural Networks

AILabPage defines – Artificial neural networks (ANNs) as “A complex computer code written with several simple, highly interconnected processing elements which are inspired by human brain structure for simulating human brain working & processing data (Information) models“. Or simply “a mathematical mesh”, point to note, it’s way different than traditional computer program though.

As per Dr. Robert Hecht-Nielsen, the inventor of one of the first neuron computers, ANN is “A computing system made up of several simple, highly connected processing elements, which process information by their dynamic state response to external inputs.”

Artificial Neural networks are designed to take several binary inputs to give a binary output. Professor Frank Rosenblatt was the first one to use neural networks. They can process information in the form audio, video, images, texts, numbers, or in any form of data. Neurons i.e. perceptrons (as known in the early days) were staged as a decision function in those times.

“Deep Learning Vs Neural Networks “

The human brain needs a much smaller data set when processing or classifying data sets as compared to computer simulation (neural networks) to learn patterns and caption images. The human brain is so powerful and steps ahead of super-fast terminology when we need to identify images, and handwriting, speak different languages, and watch videos to understand & re-create them. For machine same skills are very complex but ANNs are making it possible now. For humans, understanding the brain is easy by using their own brains though.

What is Deep Learning?

AILabPage defines Deep learning as “Undeniably a mind-blowing synchronization technique applied on the bases of 3 foundation pillars large data, computing power, skills (enriched algorithms) and experience which practically has no limits“.

Deep Learning is a subfield of the machine learning domain. Deep learning is entirely concerned with algorithms inspired by the structure and function of artificial neural networks which are inspired by the human brain (inspired only pls).

NN & Brain

Deep learning is used with too much ease to predict the unpredictable. In our opinion “We all are so busy in creating artificial intelligence by using a combination of non-bio neural networks and natural intelligence rather than exploring what we have in hand.

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.” which is the case for other methods

It is a special/critical point of discussion for everyone and puzzling game of all times as well. I am tempted to compare Artificial Neural networks with the human brain. It is fascinating to me to know, how the human brain is able to decode technologies, numbers, puzzles, handle entertainment, understand science, set body mode into pleasure, aggression, art, etc. How does the brain train itself to name a certain object by just looking 2-3 images where ANN’s need millions of those.

Books Referred & Other material referred

  • Open Internet reading and research work
  • AILabPage (group of self-taught engineers) members hands-on lab work.

Points to Note:

When to use artificial neural networks as oppose to traditional machine learning algorithms is a complex one to answer.  Neural network architecture and its algorithms may look different to many people but in the end, there is nothing wrong to have them in your tool kit. It entirely depends upon the problem at hand to solve. One needs to be patient and experienced enough to have the correct answer. All credits if any remain on the original contributor only. The next upcoming post will talk about Recursive Neural Networks in detail.

Feedback & Further Question

Do you have any questions about Quantum technologies, Artificial Intelligence , and its subdomains like Deep Learning or Machine Learning? etc. Leave a comment or ask your question via email. Will try my best to answer it.


Conclusion – Neural Networks and Deep Learning, for any effective machine learning model requirement, are the only one which is reliable data pipelines. We have seen in the post above that ANNs don’t create or invent any new information or facts. ANN helps us make sense of what’s already in front of us hidden in our data.

Deep Learning, in short, is going much beyond machine learning and its algorithms that are either supervised or unsupervised. In DL it uses many layers of nonlinear processing units for feature extraction and transformation. ANN’s structure is what enables artificial intelligence, machine learning, and supercomputing to flourish. Neural networks are powered by language translation, face recognition, picture captioning, text summarization, and a lot more.

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Posted by V Sharma

A Technology Specialist boasting 22+ years of exposure to Fintech, Insuretech, and Investtech with proficiency in Data Science, Advanced Analytics, AI (Machine Learning, Neural Networks, Deep Learning), and Blockchain (Trust Assessment, Tokenization, Digital Assets). Demonstrated effectiveness in Mobile Financial Services (Cross Border Remittances, Mobile Money, Mobile Banking, Payments), IT Service Management, Software Engineering, and Mobile Telecom (Mobile Data, Billing, Prepaid Charging Services). Proven success in launching start-ups and new business units - domestically and internationally - with hands-on exposure to engineering and business strategy. "A fervent Physics enthusiast with a self-proclaimed avocation for photography" in my spare time.

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