Welcome to my new blog , In this blog we will come to know what are neural networks and it's industry use case .
As we all know, today, neural networks (NN) are revolutionizing business and everyday life, bringing us to the next level in artificial intelligence (AI). By emulating the way interconnected brain cells function, NN-enabled machines (including the smartphones and computers that we use on a daily basis) are now trained to learn, recognize patterns, and make predictions in a humanoid fashion as well as solve problems in every business sector.
To understand what a neural network is, it helps to first understand what machine learning is. Machine learning is a type of artificial intelligence where data is collected and used to understand the behavior of a particular process and then predict how that process will act in future settings as the system is continually fed new data.
What Are Neural Networks ?
“In neural networks, when data is collected about a particular process, the model that is used to learn about and understand that process and predict how that process will perform in the future is a simplified representation of how a brain neuron works,” said Mark Stadtmueller, vice president of product strategy at AI platform provider Lucd. “A brain neuron receives an input and based on that input, fires off an output that is used by another neuron. The neural network simulates this behavior in learning about collected data and then predicting outcomes.”
Basics of neural networks...
A neural network works similarly to the human brain’s neural network. A “neuron” in a neural network is a mathematical function that collects and classifies information according to a specific architecture. The network bears a strong resemblance to statistical methods such as curve fitting and regression analysis.
A neural network contains layers of interconnected nodes. Each node is a perceptron and is similar to a multiple linear regression. The perceptron feeds the signal produced by a multiple linear regression into an activation function that may be nonlinear.
In a multi-layered perceptron (MLP), perceptrons are arranged in interconnected layers. The input layer collects input patterns. The output layer has classifications or output signals to which input patterns may map.
Hidden layers fine-tune the input weightings until the neural network’s margin of error is minimal. It is hypothesized that hidden layers extrapolate salient features in the input data that have predictive power regarding the outputs.
How krisp uses neural network..?
Krisp technology is based on deep neural networks and is trained using thousands of hours of audio. Krisp.AI uses deep neural networks to bring a whole new level to voice audio quality. With Their AI technology users can mute all sorts of noises in their background that stop users from taking that conference call literally from anywhere.
It takes just one noise or echo to disrupt an online meeting like kids playing loudly during your meetings , Your voice creates echo for others on the call , No quiet place in your home to make your calls , Dog barking impacting your calls?
Krisp makes it all go away and come up with a great solution
Remove background noise and experience HD Voice :- Krisp removes all unwanted noise from your call while delivering HD Voice quality. All in real time! You won’t sound underwater or distorted anymore.
Turn off noise coming from others on calls :- Krisp removes all types of background noise and echo coming from participants during meetings. You will not need to ask them to “go on mute” anymore.
Acoustic Echo and Room Echo Removal :- Krisp removes both the echo resonating from walls of empty room and the echo that occurs from your own voice during the call.
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