Machine Learning tasks are implemented using several types of Neural Networks, each with distinct architectures and applications:
Feedforward Neural Networks: Simplest type, with unidirectional data flow and no loops.
Recurrent Neural Networks: Incorporate loops, allowing for memory and processing of sequences.
Convolutional Neural Networks: Designed for image processing, utilizing convolutional layers to detect spatial features.
Long Short-Term Memory Networks: A type of RNN designed to handle long-term dependencies in sequences.
Radial Basis Function Networks: Use radial basis functions as activation functions, often applied in interpolation and approximation problems.
These diverse neural network types cater to different problem domains and data types, enabling a wide range of applications in machine learning