Complex Models
Complex models refer to machine learning models that have a large number of parameters and require significant computational resources to train. These models are often used in tasks such as image and speech recognition, natural language processing, and recommendation systems. Complex models can be deep neural networks, convolutional neural networks, recurrent neural networks, or other types of models that have many layers and connections between them. The complexity of these models allows them to capture intricate patterns and relationships in the data, leading to high accuracy and performance. However, the training process for complex models can be time-consuming and computationally expensive, requiring specialized hardware and software. Additionally, complex models can be prone to overfitting, where the model becomes too specialized to the training data and performs poorly on new, unseen data.
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