An artificial neural network (ANN) or simulated neural network (SNN) is a subset of machine learning at the core of deep learning algorithms. In their name and structure, they mimic how biological neurons communicate with one another.
In the fields of AI, machine learning, and deep learning, neural networks mimic the behavior of the human brain, allowing computers to recognize patterns and solve problems.
Below are the nine different types of neural networks:
Feed Forward Neural Network
Convolutional Neural Network
Radial Basis Functional Neural Network
LSTM – Long Short-Term Memory
Sequence to Sequence Models
Modular Neural Network
In real-life situations, neural networks can help people solve complex problems. As a result, they can learn and model the relationships between inputs and outputs that are nonlinear and complex. They can also make generalizations and inferences, and uncover hidden relationships, patterns, and predictions. Neural Networks also model highly volatile data (such as financial time series data) and variances needed to predict rare events (such as fraud detection). Neural networks can improve decision-making in areas such as:
Credit card and Medicare fraud detection
Optimization of logistics for transportation networks
Medical and disease diagnosis
Financial predictions for stock prices, currency, options, futures, bankruptcy, and bond ratings
Electrical load and energy demand forecasting
Chemical compound identification
Machine Learning uses algorithms to analyze data, learn from the data, and discover patterns of interest. At the same time, an Artificial Neural Network is a set of algorithms used in machine learning for modeling data using graphs of neurons.
A neural network with multiple hidden layers and multiple nodes in each layer is called a deep learning system or a deep neural network. Deep learning involves the development of algorithms that can be used to train and predict outcomes from complex data.
Random Forest is a technique of Machine Learning, while Neural Networks are exclusive to Deep Learning.
A neural network is an assembly of nodes that resembles the human brain, while a decision tree is a top-down approach to looking at data that is easy to follow.