Neural Network and its Industry Use Cases

What is Neural Network ??
A neural network is a network or circuit of neurons, or in a modern sense, an artificial neural network, composed of artificial neurons or nodes. Thus a neural network is either a biological neural network, made up of real biological neurons, or an artificial neural network, for solving artificial intelligence (AI) problems. The connections of the biological neuron are modeled as weights.

“Human brains and artificial neural networks do learn similarly.”

How Artificial Neural Networks Function-
The neurons, within each of the layer of a neural network, perform the same function. They simply calculate the weighted sum of inputs and weights, add the bias and execute an activation function.
Let’s analyse the different types of layers.

What Is An Input Layer?
The input layer is responsible for receiving the inputs. These inputs can be loaded from an external source such as a web service or a csv file.
There must always be one input layer in a neural network. The input layer takes in the inputs, performs the calculations via its neurons and then the output is transmitted onto the subsequent layers.

What Is A Hidden Layer?
The introduction of hidden layers make neural networks superior to most of the machine learning algorithms. Hidden layers reside in-between input and output layers and this is the primary reason why they are referred to as hidden. The word “hidden” implies that they are not visible to the external systems and are “private” to the neural network.
There could be zero or more hidden layers in a neural network.

What Is An Output Layer?
The output layer is responsible for producing the final result. There must always be one output layer in a neural network.
The output layer takes in the inputs which are passed in from the layers before it, performs the calculations via its neurons and then the output is computed.
In a complex neural network with multiple hidden layers, the output layer receives inputs from the previous hidden layer.

Attributes of Neural Networks-
With the human-like ability to problem-solve — and apply that skill to huge datasets — neural networks possess the following powerful attributes:

Adaptive Learning: Like humans, neural networks model non-linear and complex relationships and build on previous knowledge. For example, software uses adaptive learning to teach math and language arts.
Self-Organization: The ability to cluster and classify vast amounts of data makes neural networks uniquely suited for organizing the complicated visual problems posed by medical image analysis.
Real-Time Operation: Neural networks can (sometimes) provide real-time answers, as is the case with self-driving cars and drone navigation.
Prognosis: NN’s ability to predict based on models has a wide range of applications, including for weather and traffic.
Fault Tolerance: When significant parts of a network are lost or missing, neural networks can fill in the blanks. This ability is especially useful in space exploration, where the failure of electronic devices is always a possibility.

Tasks Neural Networks Perform-
Neural networks are highly valuable because they can carry out tasks to make sense of data while retaining all their other attributes. Here are the critical tasks that neural networks perform:
Classification: NNs organize patterns or datasets into predefined classes.
Prediction: They produce the expected output from given input.
Clustering: They identify a unique feature of the data and classify it without any knowledge of prior data.
Associating: You can train neural networks to “remember” patterns. When you show an unfamiliar version of a pattern, the network associates it with the most comparable version in its memory and reverts to the latter.

Use Cases of Neural Network -

Netflix -

Users who watch A are likely to watch B. This is perhaps the most well known feature of a Netflix. Netflix uses the watching history of other users with similar tastes to recommend what you may be most interested in watching next so that you stay engaged and continue your monthly subscription for more.
Using thousands of video frames from an existing movie or show as a starting point for thumbnail generation, Netflix annotates these images then ranks each image in an effort to identify which thumbnails have the highest likelihood of resulting in your click. These calculations are based on what others who are similar to you have clicked on. One finding could be that users who like certain actors / movie genres are more likely to click thumbnails with certain actors/image attributes.
Using past viewing data to predict bandwidth usage to help Netflix decide when to cache regional servers for faster load times during peak (expected) demand.

Facebook -

Facial Recognition is among the many wonders of Machine Learning on Facebook. It might be trivial for you to recognize your friends on social media (even under that thick layer of makeup!!!) but how does Facebook manage it? Well, if you have your “tag suggestions” or “face recognition” turned on in Facebook (this means you have provided permission for Facial Recognition), then the Machine Learning System analyses the pixels of the face in the image and creates a template which is basically a string of numbers. But this template is unique for every face (sort of a facial fingerprint!) and can be used to detect that face again in another face and suggest a tag.
The Facebook News Feed was one addition that everybody hated initially but now everybody loves!!! And if you are wondering why some stories show up higher in your Facebook News Feed and some are not even displayed, well here is how it works! Different photos, videos, articles, links or updates from your friends, family or businesses you like show up in your personal Facebook News Feed according to a complex system of ranking that is managed by a Machine Learning algorithm.
The rank of anything that appears in your News Feed is decided on three factors. Your friends, family, public figures or businesses that you interact with a lot are given top priority. Your feed is also customized according to the type of content you like (Movies, Books, Fashion, Video games, etc.) Also, posts that are quite popular on Facebook with lots of likes, comments and shares have a higher chance of appearing on your Facebook News Feed.

IBM -

For decades now, IBM has been a pioneer in the development of AI technologies and neural networks, highlighted by the development and evolution of IBM Watson. Watson is now a trusted solution for enterprises looking to apply advanced natural language processing and deep learning techniques to their systems using a proven tiered approach to AI adoption and implementation.
Watson uses the Apache Unstructured Information Management Architecture (UIMA) framework and IBM’s DeepQA software to make powerful deep learning capabilities available to applications. Utilizing tools like IBM Watson Studio and Watson Machine Learning, your enterprise can seamlessly bring your open-source AI projects into production while deploying and running your models on any cloud.

Google -

Google the search engine is powered by AI. Google’s search engine was always driven by algorithms that automatically generate a response to each query. But these algorithms amounted to a set of definite rules. Google engineers could readily change and refine these rules. And unlike neural nets, these algorithms didn’t learn on their own. But now, Google has incorporated deep learning into its search engine. And with its head of AI taking over search, the company seems to believe this is the way forward.
Youtube Safe Content uses machine learning techniques to ensure that brands are not displayed next to offensive content.
Google Translate uses an artificial neural network called Google Neural Machine Translation (GNMT) to increase fluency and accuracy of translations.
Google News uses AI to understand the people, places and things involved in a story as it evolves, organize them based on how they relate to one another.
Google Chrome uses AI to present short and highly related parts of a video while searching for something in Google Search, analyze the images on a website and plays an audio description or the alt text(when available) for people who are blind or have low vision.

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