What are neural networks
Neural networks represent deep learning using an artificial intelligence, it is the component of artificial intelligence that is meant to imitate the functioning of human brain. Neural networks are what computer scientist use to work on complex task such as making prediction, making strategy and recognizing trends.
Why Neural Networks
In present era Scientists and researchers are making the machine intelligent just like a human being. Neural network also learn as like as the human being learn and play a vital role in making the marketers successful.
As human being learn from childhood from their parents, teacher, and other in that way neural network also learn from data sets where the right answer is provided In advance.
Neural network learn and model relationship between Input and Output, make generalizations, and inferences, reveal hidden relationship make prediction and patterns, and model highly volatile data.
Types of neural networks
Here you will go through important types of neural network, that can be classified depending on their: Structure, Data flow, Neurons used and their density, and layers.
- Artificial Neural Network (ANN)
- Convolution Neural Network (CNN)
- Recurrent Neural Network (RNN)
- Autoencoder Neural Network
Artificial Neural Networks:
Artificial Neural Networks are a group of multiple neurons at each three layer, those three layers are – Input, Hidden, Output. The Input layer receive the input, while the hidden process the Input and the Output brings out the results.
Convolution Neural Networks:
Convolution Neural Network recognize image and process that is specifically designed to process pixel data. CNN is powerful in image processing and, Computer Vision, machine translation and artificial intelligence that use deep learning to perform generative and descriptive tasks.
Recurrent Neural Network:
Recurrent Neural Network uses the deep learning algorithm that are commonly used for ordinal and temporal problems such as language translation, natural language processing, speech recognition, and image captioning.
Autoencoder Neural Network:
Autoencoder Neural Network are used to create abstractions called encoders. The premise of autoencoders is to numb the irrelevant and sensitize the relevant. As layers are added, moreover abstractions are formulated at higher layers. These abstractions are used by linear or nonlinear classifiers.
Neural Network Application
The brain modelling uses for understanding of how the brain works, how behavior emerges from the interaction of networks of neurons, what needs to ‘get fixed’ in brain damaged patients.
Real World Application:
Financial modelling: – predicting the stock market.
Time series prediction: – weather seizures, climate,
Computer games: – intelligent agents, chess, backgammon.
Robotics:-autonomous adaptable robots.
Pattern recognition: – speech recognition, seismic activity, and sonar signals.
Data analysis: – data mining, and data compression
Bioinformatics: – DNA sequencing, and alignment.
How Neural Networks are used in marketing
Nowadays Neural Networks are uses in industries such as medicine, engineering, finance, and other industries.
ANN are transforming the marketing technology resources and giving marketers more efficient and dynamic tools:
- Predicting consumer behavior
- Creating and understanding more sophisticated buyer arguments
- Marketing automation
- Content creation
- Sales Forecasting