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Atificial Neural Network in Machine Learning
An artificial neural network (ANN) is a type of machine learning model that is inspired by the structure and function of the human brain. ANNs are composed of interconnected nodes, or neurons, that are organized into layers. Each neuron receives input from other neurons and performs a simple calculation to determine its own output. The outputs of some neurons then become the inputs of other neurons, and this process continues until the final output layer is reached.
Neural Network layersOpens in a new window
Neural Network layers
ANNs are able to learn from data by adjusting the weights of the connections between neurons. This process is called Back propagation. When an ANN is presented with a new piece of data, it makes a prediction about the data's output. The ANN then compares its prediction to the actual output of the data and adjusts the weights of its Connections accordingly. This process is repeated over and over again until the ANN is able to make accurate predictions About new DATA.
ANNs are used in a wide variety of applications, including:
- Image recognition: ANNs are used to identify objects in Images and videos. This is a common task in Computer vision app , such as facial recognition and object detection.
- Natural language processing (NLP): ANNs are used to process and understand human language. This is a common task in NLP application such as machine translation and sentiment analysis.
- Speech recognition: ANNs are used to transcribe spoken language into text. This is a common task in speech recognition applications, Such as voice assistants and dictation Software.
- Recommendation systems: ANNs are used to recommend products, movies, music, and other items to users. This is a common task in recommendation systems used by e-commerce Websites and streaming services
- Anomaly detection: ANNs are used to detect anomalies in data. This is a common task in anomaly detection App, such as fraud detection and Network intrusion detection.