Neural Networks is the Future of Organisational Intelligence !!

What is the Neural Network??

Neural networks are a set of algorithms, modelled loosely after the human brain, that are designed to recognize patterns. Neural networks help us cluster and classify. Within the field of machine learning, neural networks are a subset of algorithms built around a model of artificial neurons spread across three or more layers. They interpret sensory data through a kind of machine perception, labelling or clustering raw input. The patterns they recognize are numerical, contained in vectors, into which all real-world data, be it images, sound, text or time series, must be translated.

Neural networks can also extract features that are fed to other algorithms for clustering and classification; so you can think of deep neural networks as components of larger machine-learning applications involving algorithms for reinforcement learning, classification and regression.

Deep Learning and Neural Network

How a child learns through constant experiences and replication. Deep learning and Neural Network could provide unexpected business models for companies.

We know that computers are better than people at crunching series of numbers or faster processing of monotonous job, but what about tasks that are more complex? How do you teach a computer what a Dog looks like? Or how to drive a car? Or how to play a complex strategy game? Or make predictions about the stock market?

These are some of the most difficult tasks in artificial intelligence, far outstripping the capabilities of normal machine learning techniques. In these cases, computer scientists turn to neural networks and deep learning.

Deep Learning and Neural Network

Architecture of Neural Network

Neural Networks are complex structures made of artificial neurons that can take in multiple inputs to produce a single output. This is the primary job of a Neural Network to transform input into a meaningful output. Usually, a Neural Network consists of an input and output layer with one or multiple hidden layers within.

Neural Networks has Structure and Working Similar to Human Brain !!

The majority of neural networks are fully connected from one layer to another. These connexions are weighted; the higher the number the greater influence one unit has on another, similar to a human brain. As the data goes through each unit the network is learning more about the data.

A biological and an artificial neuron

In a Neural Network, all the neurons influence each other, and hence, they are all connected. The network can acknowledge and observe every aspect of the dataset at hand and how the different parts of data may or may not relate to each other. This is how Neural Networks are capable of finding extremely complex patterns in vast volumes of data.

Backpropagation and Feed Forwarding:

Backpropagation is the heart of every neural network.Backpropagation is a training algorithm consisting of 2 steps:

1) Feed forward the values

2) calculate the error and propagate it back to the earlier layers. So to be precise, forward-propagation is part of the backpropagation algorithm but comes before back-propagatin

Back Propogation

Backpropagation is for calculating the gradients which is basically slope w.r.t weights efficiently, while optimizers is for training the neural network, using the gradients computed with backpropagation. In short, all backpropagation does for us is compute the gradients.

Types on Neural Network

Types of Artificial Neural Networks Currently Being Used in Market.

  1. Convolutional Neural Network:

Convolutional neural networks are similar to feed forward neural networks, where the neurons have learnable weights and biases. Its application has been in signal and image processing which takes over OpenCV in the field of computer vision.

Below is a representation of a ConvNet, in this neural network, the input features are taken in batch-wise like a filter. This will help the network to remember the images in parts and can compute the operations. These computations involve the conversion of the image from RGB or HSI scale to the Gray-scale. Once we have this, the changes in the pixel value will help to detect the edges and images can be classified into different categories.

Convolutional Neural Network

2.Recurrent Neural Network(RNN)

The Recurrent Neural Network works on the principle of saving the output of a layer and feeding this back to the input to help in predicting the outcome of the layer.
Here, the first layer is formed similar to the feed forward neural network with the product of the sum of the weights and the features. The recurrent neural network process starts once this is computed, this means that from one time step to the next each neuron will remember some information it had in the previous time-step.

This makes each neuron act like a memory cell in performing computations. In this process, we need to let the neural network to work on the front propagation and remember what information it needs for later use. Here, if the prediction is wrong we use the learning rate or error correction to make small changes so that it will gradually work towards making the right prediction during the back propagation. This is how a basic Recurrent Neural Network looks like,

Recurrent Neural Network

3.Artificial Neural Network:

This neural network is one of the simplest forms of ANN, where the data or the input travels in one direction. The data passes through the input nodes and exit on the output nodes. This neural network may or may not have the hidden layers. In simple words, it has a front propagated wave and no backpropagation by using a classifying activation function usually.

Below is a Single layer feed-forward network. Here, the sum of the products of inputs and weights are calculated and fed to the output. The output is considered if it is above a certain value i and the neuron fires with an activated output and if it does not fire, the deactivated value is emitted .

Artificial Neural Network

4.Modular Neural Network:

Modular Neural Networks have a collection of different networks working independently and contributing towards the output. Each neural network has a set of inputs that are unique compared to other networks constructing and performing sub-tasks. These networks do not interact or signal each other in accomplishing the tasks.

MNN Visual Representation

The advantage of a modular neural network is that it breakdowns a large computational process into smaller components decreasing the complexity. This breakdown will help in decreasing the number of connections and negates the interaction of these networks with each other, which in turn will increase the computation speed. However, the processing time will depend on the number of neurons and their involvement in computing the results.

Applications of Neural Network

Image Processing and Character recognition: Neural Networks are playing a big role in image and character recognition. Character recognition like handwriting has lot of applications in fraud detection (e.g. bank fraud) and even national security assessments. Image recognition is an ever-growing field with widespread applications from facial recognition in social media, cancer detention in medicine to satellite imagery processing for agricultural and defence usage.

Signature Verification Application: Signatures are one of the most useful ways to authorize and authenticate a person in legal transactions. With these feature sets, we have to train the neural networks using an efficient neural network algorithm.

Human Face Recognition :It is one of the biometric methods to identify the given face. It is a typical task because of the characterization of ‘non-face’ images.

Forecasting: Forecasting is required extensively in everyday business decisions. Forecasting includes sales, financial allocation between products, capacity utilization, in economic and monetary policy, in finance and stock market. More often, forecasting problems are complex, for example, predicting stock prices.

Character Recognition: It is an interesting problem which falls under the general area of Pattern Recognition. Many neural networks have been developed for automatic recognition of handwritten characters, either letters or digits.

Speech Recognition: Speech occupies a prominent role in human-human interaction. Therefore, it is natural for people to expect speech interfaces with computers.

Neural Network Case Study of Google!!

We are all familiar with Google and how it’s search engine revolutionized the internet for everyone. Google claims that the advancements in their search engine and its lineup of other products would not have been possible had the company not invested a sizable chunk of time, money, and efforts into evolving technologies such as Artificial Intelligence, Deep Learning, and Machine Learning.

Google has been a powerful force in championing the use of Neural Network— a technology now so prevalent in cutting edge applications that its name is pretty much synonymous with artificial intelligence. There’s a simple reason for this — it works. Putting deep learning to work has enabled data scientists to crack a number of difficult cases which had proved challenging for decades, such as speech and image recognition, and natural language generation.

Google services, for example, the image search and translation tools use sophisticated machine learning. This allows the computer to see, listen and speak in much the same way as humans do.

Why is Google is Interested in Neural Network in the First Place..?

Google AI is focused on bringing the benefits of AI to everyone.

The past couple of years have clearly shown how interested Google has been in building smarter technology for its users. The proof includes its heavily used search engine along with many other products that rely heavily upon technologies like Artificial Intelligence, Machine Learning, and Deep Learning. Google’s primary goal has always been to understand how its users actually use their services, the thought here may include the when, where, and how of the usage of its services.

Google uses machine learning algorithms to provide its customers with a valuable and personalized experience. Gmail, Google Search and Google Maps already have machine learning embedded in services.

Artificial Intelligence Powering Google Products

Now that we know why Google has been focussing its efforts into incorporating Artificial Intelligence into a variety of its services, let’s take a look at some of the popular applications, services, and hardware from Google, that we commonly use.

1.Google Search Engine

Google’s search engine has vastly changed since its release, and Artificial Intelligence has played a major role in that. Algorithms make up for a crucial part of any search engine. With time, Google has tweaked its search engine algorithms to support the various trends in the industry, but it was the breakthrough in Deep Learning that truly enabled Google to build algorithms so efficient that they could learn on their own.Without AI, Google could not have made improvements to its algorithm for search pattern identification to filter and avoid spam, as well as categorize and catalog images for search.

2.Google Translate


Google Translate is a simple online tool that allows users to translate any text from one language to another. Compared to what it was back in 2006 when it launched with its Statistical Machine Translation, Google Translate has made great strides in offering instant translations.
But it’s the recent advancements in AI, specifically Neural Machine Translation, which truly boosted the quality and reliability of translations, including over 109 languages to deliver relevant translations. Additionally, improvements in Natural Language Processing have also optimized the voice input capabilities in several Google services.

3.Google Assistant


You simply cannot talk about Google’s AI-powered innovations while leaving Google Assistant out of the discussion. You can think of Google Assistant as a smart extension of your phone that can help you get the most out of the digital tasks without even touching it, such as making a call, sending a text, making a note, setting a reminder, and such.

But that’s not the end of the list, Google Assistant also supports natural-sounding conversations for voice searching, it learns from your usage patterns and suggests actions, and it can even automate several tasks in a rule at once with a simple command.

4.Google Ads


AdWords, now called Google Ads is a part of Google’s Marketing suite of tools. Google Ads is a tool that lets businesses and users advertise their products online, giving users full control over the creation, management, and the placement of their ads. As Google continues to analyze and profile its users’ search behavior, it can leverage this data to effectively target the right advert to the right individual, which is the core idea behind Google Ads.

Google uses several ML algorithms that rank thousands of keywords based on several metrics, which are then used to pick the right ad to show to the users. Moreover, AI can also offer valuable corrective insights to its users.

5.Google Maps


Google Maps is a handy navigation system from Google and is available on Android, iOS, and Web. It is rated among the top navigations and mapping applications and is extremely popular among its users. Satellite imagery, 360-degree maps, indoor maps, and live traffic conditions are just some of the features offered by Google Maps.

Google has implemented several AI and ML-powered features into Google Maps, such as the integration with Google Assistant, which analyzes the user’s commuting routine and suggests routes with less traffic and delays based on live data. Another such feature is the recommendations for nearby points of interest, such as gas stations, places to eat, ATMs, and more.



We’re sure a majority of users primarily use Gmail as their preferred email service but do you know that Google has implemented a host of smart features to Gmail? One of these features is called smart reply, which analyzes the entire email and suggests a suitable short reply, eliminating the need to even type out the confirmations.

Gmail also has spam protection that filters any potential spam from getting to your inbox. Also, the AI in Gmail can smartly sort your emails into categories such as Promotions, Social, Updates, Primary, and Priority. Gmail can also predict the text while you’re trying to compose an email to make the job faster.

7.You Tube


The famous online video-sharing platform YouTube has grown exponentially both feature-wise and in scale since 2005. A vast number of brands use YouTube for marketing while millions of other people use it to consume the latest video content that interests them.

To provide a safe and seamless experience to both the brands and the consumers, YouTube has deployed several mechanisms that depend on AI and ML. Some of these AI-driven mechanisms include automatic identification and removal of objectionable content, automatic recommendation of content, and playback of the next related video based on the users’ interests and watch history.

8. Google Chrome


Possibly among the top used browsers, Google Chrome has gone through a host of changes since it was first introduced in the winter of 2008. Among the huge list of changes, there are a couple of recent changes that utilize the power of Artificial Intelligence to make the internet accessible for everyone.

Most Efficient Artificial Intelligence Powering Google Products —

  • Google Lens
  • Google Duplex
  • Google News
  • Google Finance
  • Cloud storage for consumers

Future Scope of Neural Network in Google:

The Future of Artificial Intelligence MNC’s like Google showcases the bi-directional movement of AI between research labs and company operations, where businesses are using the power of automated AI tools to enhance customer experience or execute high-speed data analysis.

“Neural Network is a core, transformative way by which we’re rethinking how we’re doing everything. We are thoughtfully applying it across all our products, be it search, ads, YouTube, or Play. And we’re in the early days, but you will see us — in a systematic way — apply machine learning in all these areas.”

– Sundar Pichai, Google CEO

Being a multifaceted company dealing with software and hardware catering to several domains, Google now understands how important it is to create AI-powered products that can help its users usher into the new and advanced era of smart technology. Google has declared itself a machine learning-first company.

With the changes around the globe, be it environmental, digital or even social, technology is the answer. The always updating platform and one step ahead search engine is nothing but, the state-of-the-art in the field. It applies AI to products and to new domains and helps in developing tools to ensure that everyone can access AI.

The main aim of Google is to organize the world’s information and make it universally accessible and useful to everyone. This is where AI is helpful, it helps to do things in exciting ways. It helps to solve problems for the users, customers, and the world.With machine learning, it can develop intelligent systems that are capable of taking decisions on an autonomous basis. It learns algorithms from the past instances of data through statistical analysis and pattern matching. Then, based on the learned data, it provides with the predicted results.

What’s Next….

If Google continues in the same direction with the same enthusiasm with Neural Network,Artificial Intelligence and Deep Learning it is very much likely that we might witness great leaps in several domains in the upcoming years.


Neural Network is the backbone of many organisations because of its huge benifits towards the businesses and good utilization of Huge Data .One of the Big MNC is Google who has become powerful and is enabling new solutions with the help of NN towards the problems in this modern age.

Thanks For Reading the Article…!!!



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