Exploring DL models
Various types of DL architectures and techniques have been developed to tackle different tasks and challenges. Here are some examples:
- CNNs: Mainly used for image and video analysis, CNNs are designed to learn spatial hierarchies of features automatically and adaptively from input data. They have been highly successful in tasks such as image classification, object detection, and image segmentation.
- Recurrent NNs (RNNs): RNNs are well suited for tasks involving sequences, such as natural language processing (NLP) and speech recognition. They have an internal memory that allows them to maintain information about previous inputs, making them effective for handling sequential data.
- Long short-term memory (LSTM) networks: A type of RNN, LSTMs are designed to overcome the vanishing gradient problem in training deep networks. They are particularly useful for capturing long-range dependencies in sequential data, making them popular for tasks such as language...
