🏆 A Quick Overview
Explore the fundamentals of artificial neural networks including their structure, advantages, and challenges. Understand how ANNs apply to tabular, image, and text data and the role of activation functions in learning complex relationships. This lesson also highlights key limitations, such as handling sequential information and vanishing gradients, and marks a milestone in your deep learning journey.
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Artificial neural network (ANN)
An Artificial Neural Network (ANN) is composed of multiple perceptrons/ neurons at each layer. ANN is a feedforward neural network because inputs are only processed in the forward direction, i.e., each layer feeds its information to the layer next to it until the final layer is reached.
Each network consists of an input, hidden layers, and an output layer. Essentially, each layer learns the weights that best approximates the mapping between the input and the output.
Applications
ANN can be used to solve problems related to:
- Tabular data
- Image data
- Text data
Advantages
ANN is capable of learning any non-linear functions through the use of activation functions which introduce non-linear ...