Neural Networks and its industry use cases
We humans are known as most intelligent species on the planet because of our ability to think. we can think because of our brain, but do you know how brain can able to think or remember the things so easily ?
I know, you might know. but let me tell if you don't know.
Our brain contains billions of nerve cells arranged in patterns that coordinate thought, emotion, behavior, movement and sensation. A complicated highway system of nerves connects your brain to the rest of your body, so communication can occur in split seconds.
So this neurons are responsible for working of brain.
When scientists did a long research, they came up with the conclusion that similar power can be given to the computers so that they can be able to think and take decisions. this gave rise to the Artificial Intelligence and Artificial Neural Network.
so you might wondering that what is neural network ?
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. A positive weight reflects an excitatory connection, while negative values mean inhibitory connections. All inputs are modified by a weight and summed. This activity is referred to as a linear combination. Finally, an activation function controls the amplitude of the output. For example, an acceptable range of output is usually between 0 and 1, or it could be −1 and 1.
What is ANN :
A neural network (NN), in the case of artificial neurons called artificial neural network (ANN) or simulated neural network (SNN), is an interconnected group of natural or artificial neurons that uses a mathematical or computational model for information processing based on a connectionistic approach to computation. In most cases an ANN is an adaptive system that changes its structure based on external or internal information that flows through the network.
How Neural network Works ?
An artificial neuron simulates how a biological neuron behaves by adding together the values of the inputs it receives. If this is above some threshold, it sends its own signal to its output, which is then received by other neurons. However, a neuron doesn’t have to treat each of its inputs with equal weight. Each of its inputs can be adjusted by multiplying it by some weighting factor. Say, if input A were twice as important as input B, then input A would have a weight of 2. Weights can also be negative, if the value of that input is unimportant.
Each neuron is thus connected to other neurons in the network through these synaptic connections whose values are decided weights. The process of training involves adjusting these weight values so that the final output of the network gives you the right answer.
The simplest version of an artificial neural network, based on Rosenblatt’s perceptron, has three layers of neurons. first layer is the input layer. This takes input values–say, the pixels of a photograph. The outputs of this first layer of neurons are connected to a middle layer, called the “hidden” layer. The outputs of these “hidden” neurons are then connected to the final output layer. This final layer is what gives you the answer to what the network has been trained to do.
In above visual example, a network can be trained to recognize photos of Dog. The output layer of the network would then have two outputs, “Dog” or “not Dog.”
Industry Use Cases:
Facebook:
As soon as you upload any photo to Facebook, the service automatically highlights faces and prompts friends to tag. How does it instantly identify which of your friends is in the photo?
The answer is simple — Artificial Intelligence. In a video highlighting Facebook’s Artificial Intelligence research, they discuss the applications of Neural Networks to power their facial recognition software. Facebook is investing heavily in this area, not only within the organization, but also through the acquisitions of facial-recognition startups like Face.com (acquired in 2012 for a rumored $60M), Masquerade (acquired in 2016 for an undisclosed sum), and Faciometrics (acquired in 2016 for an undisclosed sum).
In June 2016, Facebook announced a new Artificial Intelligence initiative that uses various deep neural networks such as DeepText — an artificial intelligence engine that can understand the textual content of thousands of posts per second, with near-human accuracy.
Instagram, acquired by Facebook back in 2012, uses deep learning by making use of a connection of recurrent neural networks to identify the contextual meaning of an emoji which has been steadily replacing slangs (for instance, a laughing emoji could replace “rofl”).
By algorithmically identifying the sentiments behind emojis, Instagram creates and auto-suggests emojis and emoji related hashtags. This may seem like a minor application of AI, but being able to interpret and analyze this emoji-to-text translation at a larger scale sets the basis for further analysis on how people use Instagram.
Pinterest uses computer vision another application of neural networks, where we teach computers to “see” like a human, in order to automatically identify objects in images (or “pins”, as they call it) and then recommend visually similar pins. Other applications of neural networks at Pinterest include spam prevention, search and discovery, ad performance and monetization, and email marketing.
Amazon
Amazon shows you recommendations using its “customers who viewed this item also viewed”, “customers who bought this item also bought”, and also via curated recommendations on your homepage, on the bottom of the item pages, and through emails. Amazon makes use of Artificial Neural Networks to train its algorithms to learn the pattern and behavior of its users. This, in turn, helps Amazon provide even better and customized recommendations.
Conclusion:
Neural networks are being used in almost every industry to take a smart decision.
They are mainly using for
- Function approximation, or regression analysis, including time series prediction and modeling.
- Classification, including pattern and sequence recognition, novelty detection and sequential decision making.
- Data processing, including filtering, clustering, blind signal separation and compression.
Application areas of ANNs include nonlinear system identification and control (vehicle control, process control), game-playing and decision making (backgammon, chess, racing), pattern recognition (radar systems, face identification, object recognition), sequence recognition (gesture, speech, handwritten text recognition), medical diagnosis, financial applications, data mining (or knowledge discovery in databases, “KDD”), visualization and e-mail spam filtering. For example, it is possible to create a semantic profile of user’s interests emerging from pictures trained for object recognition.
