Neural networks or Artificial Neural Networks, also known as synthetic synapses or robot brains, are like the turbo boosters of machine learning and keep the engine of deep learning chugging along.

neural network

Their name and composition are based on the human brain, kind of like a computer-age recreation of biology’s most impressive organ. Can neural coding simulate the human brain if yes then up to what extent? Artificial neural networks are extremely powerful for doing excellent and super-fast computation. Can we say this any better than the human mind for speedy mathematics?

This is the first post in the “Neural Networks – Plain Text” series


Deep Learning

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.

It has revolutionized today’s industries by demonstrating near human-level accuracy in certain tasks. tasks like pattern recognition, image classification, voice or text decoding, and many more. Self-driving cars are one of the best examples and biggest achievements so far.

The hype and optimism surrounding Artificial Intelligence have led to a widespread real “fake news disease” and misbelief about neural networks, resembling a prevalent issue of misinformation, as people mistakenly assume they function similarly to the human brain.

AILabPage Define Artificial Neural Networks as

Deep learning which is a subset of machine learning utilizes interconnected nodes to create a layered structure that emulates (try to) the operation of the human brain through connected neurons. Artificial neural networks strive to achieve precision in intricate duties such as identifying faces and condensing texts.

Artificial Neural Network – Outlook

Neural networks were intentionally crafted to emulate biological neural networks and serve as algorithms dedicated to this specific objective. The basic concept of neural networks relies on connecting neurons according to the unique arrangement of the network. Initially, the aim was to create an artificial system with the ability to function like the human brain sadly its far from the reality.

To be honest, the functioning of our brains is significantly more intricate than that of an artificial neural network. Put simply, an ANN is a mathematical tool that processes inputs (such as patterns or images) and uses specialised hidden layers to determine the appropriate output based on the weights of those inputs.

Deep Learning – Introduction to Artificial Neural Networks

How Neural Network Algorithms Works: An Overview

Yikes, researchers have gone off the deep end by trying to compare human brains to ANNs! They’re like two different galaxies and studying them together has caused a lot of chaos and confusion to people who don’t know much about the topic. These scientists (no offence to anyone) need to chill out, slow down, and figure out why they’re researching this in the first place.

In brief, Artificial Neural Networks (ANNs) are mathematical entities that were initially formulated to mimic biological neurons, although the degree of approximation remains open for further inquiry. Researchers are endeavouring to unravel the potential of a brain-computer interface. The task of simulating the human brain with AI is a formidable undertaking and is unlikely to be achieved within the next half-century or so.


What Makes Human Brain So Special

The question of what sets the human brain apart from that of other species and renders it unique frequently arises in comparative analysis. The topic under consideration has been the subject of profound scrutiny and engenders enigmatic intrigue, rendering it a perplexing puzzle of enduring interest.

The human brain possesses a remarkable cognitive aptitude that renders it highly suited for the generation of novel ideas and the identification of new concepts. We have successfully fabricated a wheel that surpasses the capabilities of both artificial intelligence and other animal species. The human brain represents a significant milestone in the progression of biological organisms as it is the most prominent accomplishment in evolution.

Neural Networks

How the human brain is designed and how it functions can not be covered in this post as I do not have any expertise in neuroscience. Out of curiosity, I am tempted to compare Artificial Neural networks with the human brain (With the help of talk shows on such topics).

The human brain is like a supercomputer, constantly churning out directives, knowledge, and decisions – all while using an astounding amount of energy for such a small device. Scientists still don’t have the answer to why the human brain has so many neurons (roughly 86 billion) to it’s credit – but we sure are glad it does.

Suzana Herculano-Houzel, a neuroscientist (video) found a novel way to count a number of neurons with the help of a “homogenous mixture” what she referred to as brain soup by dissolving the brain in it. In the video, you will find an interesting argument she made with the help of brain soup.

  1. The number of neurons relative to the brain (human) size/weighs with other primates may be consistent.
  2. Cerebral cortex –  The outer layer of the neural tissue of the cerebrum of the brain is the area responsible for higher cognition. It only holds around 20% of all our brain’s neurons. Interestingly a similar proportion found in other mammals as well. (From link)
  3. Brain size – the Bigger brain is not a better brain, otherwise cow, blue whale or elephant would have better cognitive abilities.
  4. The human brain is largest to the proportion of body mass – Not true.
  5. The higher the brain weighs, the higher the cognitive ability sadly not true again. The human brain weighs between 1.2kg to 1.5kg and the elephant roughly got around 4.5kg.

The human brain grows in size as it starts consuming more calories and most likely credit goes to the cooked food we eat.

Type and Kind of Neural Networks

There are several kinds of Neural Networks in deep learning. Neural networks consist of input and output layers and at least one hidden layer.

Neural Network Algorithms are based on radial basis function with can be used for strategic reasons. There are several other models of the neural network including what we have mentioned above.

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.

Points to Note:

Uh oh, it’s time to figure out when to use which “machine learning algorithm”—a tricky decision that can really only be tackled by the experts! So if you think you’ve got the right answer, take a bow and collect your credits! And don’t worry if you don’t get it right; this next post will walk us through neural networks’ “neural network architecture” in detail.

Books Referred & Other material referred

  • Open Internet research, news portals and white papers reading
  • Lab and hands-on experience of  @AILabPage (Self-taught learners group) members.
  • Self-Learning through Live Webinars, Conferences, Lectures, and Seminars, and AI Talkshows

Feedback & Further Question

Do you have any questions about AI, machine learning, data science, or big data analytics? Leave a question in a comment or ask via email. I will try my best to answer it.


Machine Learning (ML) - Everything You Need To Know

Conclusion –  Undeniably, ANN’s and the human brain are not the same, and function and working are also very different. We have seen in the post above that ANNs don’t create or invent any new information or facts, but the human brain does. ANN helps us make sense of what’s already available in the hidden format. ANN takes an empirical approach to a massive amount of data to give the best and most accurate results.

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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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