Different neural network architectures are better suited to different tasks. These include:
a. Feedforward Neural Networks: This is the simplest type, where all data flows in one direction from input to output. They are further applied in classification and regression tasks.
b. Convolutional Neural Networks: They are specialized to process data with grid-like topology. For example, in images, they are extremely successful and achieve state-of-the-art performance in image and video recognition.
c. Recurrent Neural Networks (RNNs): These are network designs for the prediction of sequences, enabling them to maintain a ‘memory’ due to the formation of directed cycles formed by the connections. They apply in natural language processing and time series analysis.
d. Long Short-Term Memory Networks (LSTMs): This is a type of RNN that can learn long-term dependencies, so it applies to tasks such as language modeling and translation.
